system

Abstract

The system according to this embodiment aims to efficiently perform grammar and vocabulary suggestions, optimize structure, and adjust tone in order to improve the quality of writing. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a rewriting unit, and a provision unit. The reception unit receives text input from the user. The analysis unit analyzes the text input by the reception unit and makes suggestions for grammar and vocabulary, and optimizes the structure. The rewriting unit rewrites the text analyzed by the analysis unit in a tone suitable for the reader. The provision unit provides the rewritten text from the rewriting 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 prior art, there is a problem that it is time-consuming and difficult to efficiently propose grammar and vocabulary, optimize the composition, and adjust the tone in order to improve the quality of text creation.

[0005] The system according to the embodiment aims to efficiently propose grammar and vocabulary, optimize the composition, and adjust the tone in order to improve the quality of text creation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a rewriting unit, and a provision unit. The reception unit receives text input from the user. The analysis unit analyzes the text input by the reception unit and makes suggestions for grammar and vocabulary, and optimizes the structure. The rewriting unit rewrites the text analyzed by the analysis unit in a tone suitable for the reader. The provision unit provides the rewritten text from the rewriting unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently suggest grammar and vocabulary, optimize structure, and adjust tone in order to improve the quality of writing. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 receiving 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 receiving 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 writing assistant system according to an embodiment of the present invention is an innovative system that supports writing by utilizing generative AI. This writing assistant system automatically suggests grammar and vocabulary, optimizes structure, and rewrites the text in a tone appropriate for the reader, simply by the user inputting text. It learns the user's writing style and provides personalized feedback the more it is used. Users can experience a noticeable improvement in their writing skills in a short period, enabling more natural and effective expression. For creative writing, the generative AI provides plot and idea generation functions, helping to streamline the writing process. It is also ideal for everyday communication and business, streamlining the creation of emails and presentation materials and helping to accurately express the intended message. For example, when a user inputs text, the generative AI analyzes it, suggests grammar and vocabulary, and optimizes structure. Furthermore, it rewrites the text in a tone appropriate for the reader, generating the final text. The generative AI learns the user's writing style and provides personalized feedback the more it is used. As a result, users can experience a noticeable improvement in their writing skills in a short period, enabling more natural and effective expression. In creative writing, generative AI provides plot and idea generation capabilities, helping to streamline the writing process. For example, in novel writing, generative AI can suggest plot ideas, allowing the user to proceed with writing based on those ideas. Similarly, in business document and email creation, generative AI efficiently generates text, helping to accurately express the intended message. Thus, a writing assistant system utilizing generative AI is an innovative tool that makes writing more enjoyable and effective. It elevates user writing to a new level, making it easy to create high-quality documents. In this way, the writing assistant system efficiently supports the user's writing process, resulting in a high-quality final product.

[0029] The writing assistant system according to this embodiment comprises a reception unit, an analysis unit, a rewriting unit, and a provision unit. The reception unit receives text input from the user. The text input by the user includes, but is not limited to, business documents, emails, and creative writing. The reception unit can receive text by methods such as keyboard input, voice input, and handwriting input. The analysis unit uses a generation AI to analyze the text input by the reception unit and makes suggestions for grammar and vocabulary, and optimizes the structure. The analysis unit, for example, uses a grammar check algorithm to analyze the grammar of the text and makes suggestions for appropriate grammar. The analysis unit can also make suggestions for appropriate vocabulary based on vocabulary selection criteria. Furthermore, the analysis unit can also make suggestions to optimize the structure of the text, such as paragraph placement and ensuring a logical flow. The rewriting unit uses a generation AI to rewrite the text analyzed by the analysis unit in a tone that suits the reader. The rewriting unit can rewrite the text in a tone that suits the reader, such as a formal tone or a casual tone. Furthermore, the rewriting unit can use a generation AI to rewrite the text to make the expression more natural and effective. The provisioning unit provides the user with the text rewritten by the rewriting unit. The provisioning unit can provide the user with the rewritten text by methods such as displaying it on the screen, sending it by email, or printing it. In this way, the writing assistant system according to the embodiment can efficiently support the user's writing and achieve a high-quality result. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can input the text entered by the user into the generation AI and have the generation AI perform grammar and vocabulary suggestions and optimize the structure. Some or all of the above-described processes in the rewriting unit may be performed using a generation AI or not. For example, the rewriting unit can input the text analyzed by the analysis unit into the generation AI and have the generation AI perform rewriting in a tone that suits the reader. Some or all of the above-described processes in the provisioning unit may be performed using AI or not.For example, the delivery unit can input the text rewritten by the rewriting unit into the AI ​​and have the AI ​​execute the optimal method for providing it to the user.

[0030] The reception desk receives text input from the user. This text may include, but is not limited to, business documents, emails, or creative writing. The reception desk can accept text via methods such as keyboard input, voice input, or handwriting input. Specifically, keyboard input allows the user to input text using a computer or smartphone keyboard. Voice input uses technology to convert speech into text as the user speaks into a microphone. Handwriting input recognizes and converts handwritten characters using a tablet or stylus pen. This allows users to input text in the way that suits them best. Furthermore, the reception desk sends the input text to the analysis department in real time, allowing analysis to begin immediately. This enables users to create documents smoothly. The reception desk also saves and manages the input text, providing convenient features for users to re-edit or review it later. For example, it's possible to save the input text to the cloud and access it from different devices. This allows users to create documents regardless of location or device.

[0031] The analysis unit uses a generation AI to analyze the text input by the reception unit, making suggestions for grammar and vocabulary, and optimizing the structure. For example, the analysis unit uses a grammar checking algorithm to analyze the grammar of the text and suggest appropriate grammar. The analysis unit can also suggest appropriate vocabulary based on vocabulary selection criteria. Furthermore, the analysis unit can suggest ways to optimize the structure of the text, such as paragraph placement and ensuring logical flow. Specifically, the generation AI uses natural language processing technology to detect grammatical errors in the input text and suggests corrections. For example, it checks for subject-verb agreement, tense agreement, and appropriate punctuation placement. The generation AI also suggests more appropriate vocabulary according to the tone and style of the text. For example, it suggests formal vocabulary for business documents and more expressive vocabulary for creative writing. Furthermore, the generation AI analyzes the overall structure of the text and suggests ways to optimize paragraph order and logical flow. For example, it checks whether the introduction, body text, and conclusion are properly arranged and suggests moving or adding paragraphs as needed. In this way, the analysis unit provides support to refine the user's input into high-quality text.

[0032] The rewriting unit uses generative AI to rewrite text analyzed by the analysis unit in a tone appropriate to the reader. For example, the rewriting unit can rewrite text in a formal tone, a casual tone, or any other tone suitable for the reader. Furthermore, the rewriting unit can use generative AI to rewrite text to make the expression more natural and effective. Specifically, the generative AI selects an appropriate tone and style according to the content and purpose of the text, and rewrites the entire text to match that tone. For example, it uses polite language and honorifics in business documents, and friendly language in casual emails. The generative AI also improves the flow and rhythm of the text, making it easier to understand and more engaging for the reader. For example, it condenses redundant expressions and adds expressions to emphasize important points. Moreover, the rewriting unit can continuously improve the accuracy and effectiveness of its rewriting based on user feedback. For example, users can provide evaluations and comments on the rewritten text, allowing the generative AI to learn from this feedback and incorporate it into future rewrites. This allows the rewriting department to consistently provide high-quality rewrites and meet user needs.

[0033] The delivery department provides users with rewritten documents created by the rewriting department. The delivery department can provide users with rewritten documents in various ways, such as displaying them on a screen, sending them via email, or printing them. Specifically, the delivery department can display the rewritten documents on the user's device in real time, allowing for immediate review. Users can also choose to have the rewritten documents sent via email for sharing with other relevant parties. Furthermore, the delivery department provides a printing format for the rewritten documents, enabling users to utilize them as physical documents. Considering user convenience, the delivery department offers multiple delivery methods, allowing users to receive the rewritten documents in the most suitable way. The delivery department also stores and manages the rewritten documents, providing convenient features for later reuse and editing. For example, rewritten documents can be stored in the cloud and accessed from different devices. This allows the delivery department to provide users with high convenience and flexibility, improving the efficiency of document creation.

[0034] The analysis unit can make grammatical and vocabulary suggestions using generative AI. For example, the analysis unit can use generative AI to analyze the grammar of a text and suggest appropriate grammar. For example, the generative AI can use a grammar checking algorithm to analyze the grammar of a text and suggest grammatically correct expressions. The analysis unit can also use generative AI to analyze the vocabulary of a text and suggest appropriate vocabulary. For example, the generative AI can suggest vocabulary suitable for the text based on vocabulary selection criteria. Furthermore, the analysis unit can use generative AI to analyze the structure of a text and optimize its structure, such as paragraph placement and ensuring logical flow. This improves the accuracy of grammatical and vocabulary suggestions by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input text entered by the user into the generation AI and have the generation AI perform grammatical and vocabulary suggestions.

[0035] The analysis unit can optimize the structure using a generative AI. For example, the analysis unit can use a generative AI to analyze the structure of a document and optimize it, such as by arranging paragraphs and ensuring a logical flow. For example, the generative AI can analyze the arrangement of each paragraph in a document and propose the optimal arrangement to ensure a logical flow. The analysis unit can also use a generative AI to analyze the logical flow of a document and propose an appropriate structure. For example, the generative AI can analyze the logical flow of a document and propose ways to strengthen the connections between paragraphs. Furthermore, the analysis unit can use a generative AI to analyze the overall structure of a document and propose an optimal structure. In this way, the structure of the document is optimized by using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input a document entered by a user into a generative AI and have the generative AI perform the structure optimization.

[0036] The rewriting function can rewrite text in a tone appropriate to the reader using a generative AI. For example, the rewriting function can use a generative AI to analyze the tone of the text and rewrite it in a tone appropriate to the reader. For example, the generative AI can rewrite the text in a tone appropriate to the reader, such as a formal tone or a casual tone. The rewriting function can also use a generative AI to rewrite the text in a way that makes the expression more natural and effective. For example, the generative AI can analyze the expression of the text and suggest more natural and effective expressions. Furthermore, the rewriting function can use a generative AI to adjust the tone of the text and rewrite it in a tone appropriate to the reader. In this way, it becomes possible to rewrite text in a tone appropriate to the reader by using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the rewriting function may be performed using a generative AI or not. For example, the rewriting unit inputs the text analyzed by the analysis unit into the generation AI, which then performs a rewrite in a tone that suits the reader.

[0037] The service provider can provide the rewritten text to the user. The service provider can provide the rewritten text to the user in ways such as displaying it on a screen, sending it by email, or printing it. For example, the service provider can display the rewritten text to the user through a web application or a mobile application. The service provider can also provide the rewritten text to the user by sending it by email. Furthermore, the service provider can print the rewritten text and provide it to the user in paper form. This improves user convenience by providing the user with the rewritten text. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the text rewritten by the rewriting service provider into the AI ​​and have the AI ​​execute the optimal method for providing it to the user.

[0038] The analysis unit can learn the user's writing style using a generative AI and provide personalized feedback. For example, the analysis unit can use a generative AI to analyze the user's past writings and learn the user's writing style. For example, the generative AI analyzes the user's past writings and learns the user's writing style and vocabulary choices. The analysis unit can also use a generative AI to provide personalized feedback based on the user's writing style. For example, the generative AI provides feedback on grammar, vocabulary choices, and structural optimization based on the user's past writings. Furthermore, the analysis unit can use a generative AI to continuously learn the user's writing style and improve the accuracy of the feedback. This allows for the provision of personalized feedback by learning the user's writing style. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the user's past writings into the generation AI, allowing the AI ​​to learn the user's writing style and provide feedback.

[0039] The rewriting section can provide plot and idea generation functions using generative AI. For example, the rewriting section uses generative AI to generate plots and ideas in creative writing. For example, the generative AI can suggest plot ideas in novel writing. The rewriting section can also use generative AI to generate ideas in the creation of business documents and emails. For example, the generative AI can suggest ideas for the structure and content of business documents. Furthermore, the rewriting section can use generative AI to support the user in generating ideas in their writing. This allows for smoother plot and idea generation by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the rewriting section may be performed using generative AI or not. For example, the rewriting section can have the generative AI perform the generation of plots and ideas in the user's writing.

[0040] The service provider can streamline the creation of business documents and emails. For example, the service provider can use generative AI to streamline the creation of business documents and emails. For example, the generative AI can analyze the format and content of business documents and propose efficient creation methods. The service provider can also use generative AI to streamline the creation of emails. For example, the generative AI can analyze the content of emails and propose appropriate expressions and structures. Furthermore, the service provider can use generative AI to support the creation of business documents and emails. This streamlines the creation of business documents and emails. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can have a generative AI create business documents and emails.

[0041] The service provider can accurately express the message to be conveyed. For example, the service provider can use generative AI to accurately express the message to be conveyed. For example, the generative AI can analyze the main points of a text and suggest appropriate ways of expressing them. The service provider can also use generative AI to suggest expressions to emphasize the main points of the message. For example, the generative AI can suggest expressions to emphasize important parts of a text. Furthermore, the service provider can use generative AI to provide support for effectively expressing the message to be conveyed. This ensures that the message to be conveyed is accurately expressed. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can have the generative AI execute the message to be conveyed.

[0042] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can use AI to analyze the user's past input history. For example, the reception desk can analyze patterns in text previously entered by the user and suggest the optimal input method. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the reception desk can select the optimal input method based on the user's past input history. In this way, the optimal input method is selected by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input method.

[0043] The reception desk can filter the user's current projects and areas of interest when they input text. For example, the reception desk can use AI to analyze the user's current projects and areas of interest. For example, the reception desk can analyze data from project management tools and the user's search history to filter relevant information. The reception desk can also prioritize displaying keywords related to the user's current projects. For example, the reception desk can prioritize displaying information related to the project the user is currently working on. Furthermore, the reception desk can filter and display relevant information based on the user's areas of interest. This ensures that highly relevant information is provided by filtering based on the current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0044] The reception desk can prioritize inputting highly relevant text by considering the user's geographical location information when text is entered. For example, the reception desk can use AI to analyze the user's geographical location information. For example, the reception desk can analyze GPS data or IP addresses to determine the user's current location. The reception desk can also prioritize inputting information related to a specific region if the user is in that region. For example, if the reception desk is traveling, it will prioritize inputting information related to the travel destination. Furthermore, the reception desk can filter and display highly relevant information based on the user's geographical location information. As a result, highly relevant text is prioritized by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​perform the filtering of highly relevant information.

[0045] The reception desk can analyze the user's social media activity and input relevant text when text is entered. For example, the reception desk can use AI to analyze the user's social media activity. For example, the reception desk can analyze the content of the user's social media posts and input relevant information. The reception desk can also prioritize inputting keywords that the user frequently uses on social media. For example, the reception desk can analyze the interests of the user's social media followers and input relevant information. Furthermore, the reception desk can filter and display highly relevant information based on the user's social media activity. In this way, relevant text is input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant information.

[0046] The analysis unit can adjust the level of detail of its analysis based on the importance of the text. For example, the analysis unit can use AI to evaluate the importance of a text. For example, the analysis unit can evaluate the importance of a text based on the frequency of keyword occurrences or the type of document. Furthermore, for important texts, the analysis unit can perform a detailed analysis and make suggestions for grammar and vocabulary. For example, the analysis unit can perform a detailed grammatical check and vocabulary selection for important texts. In addition, for general texts, the analysis unit can perform a basic analysis and make simple suggestions for grammar and vocabulary. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the text. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the text into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the document during analysis. For example, the analysis unit can use AI to classify the category of the document. For example, the analysis unit can apply different analysis algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, the analysis unit can apply a business-specific analysis algorithm. For example, the analysis unit can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, in the case of creative writing, the analysis unit can apply a creative-specific analysis algorithm. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the document into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0048] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis process. For example, the analysis unit can use AI to evaluate the submission date of documents. For example, the analysis unit can evaluate the submission date of documents based on the submission deadline or submission date. The analysis unit can also prioritize the analysis of documents with approaching deadlines. For example, the analysis unit can prioritize providing grammatical and vocabulary suggestions for documents with approaching submission deadlines. Furthermore, the analysis unit can postpone the analysis of documents with distant submission dates. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the documents into the AI ​​and have the AI ​​determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the texts during analysis. For example, the analysis unit can use AI to evaluate the relevance of texts. For example, the analysis unit can evaluate the relevance of texts based on the degree of topic match or keyword co-occurrence. The analysis unit can also prioritize the analysis of highly relevant texts. For example, the analysis unit can prioritize grammatical and vocabulary suggestions for highly relevant texts. Furthermore, the analysis unit can postpone the analysis of less relevant texts. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of texts into the AI ​​and have the AI ​​adjust the order of analysis.

[0050] The rewriting function can adjust the level of detail in the rewriting process based on the importance of the text. For example, the rewriting function can use AI to evaluate the importance of a text. For instance, it might evaluate the importance based on keyword frequency or document type. Furthermore, for important texts, the rewriting function can perform detailed rewriting and offer grammatical and vocabulary suggestions. For example, it can perform detailed grammatical checks and vocabulary selection for important texts. Additionally, for general texts, it can perform basic rewriting and offer simple grammatical and vocabulary suggestions. This allows for efficient rewriting by adjusting the level of detail based on the importance of the text. Some or all of the above processes in the rewriting function may be performed using AI or not. For example, the rewriting function can input the importance of the text into the AI ​​and have the AI ​​adjust the level of detail in the rewriting.

[0051] The rewriting function can apply different rewriting algorithms depending on the category of the text during the rewriting process. For example, the rewriting function can use AI to classify the category of the text. For instance, the rewriting function can apply different rewriting algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, the rewriting function can apply a business-specific rewriting algorithm. For example, the rewriting function can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, in the case of creative writing, the rewriting function can apply a creative-specific rewriting algorithm. This allows for more appropriate rewriting by applying different rewriting algorithms depending on the category of the text. Some or all of the above-described processes in the rewriting function may be performed using AI or not. For example, the rewriting function can input the category of the text into the AI ​​and have the AI ​​execute the application of different rewriting algorithms.

[0052] The rewriting unit can determine the priority of rewriting based on the submission date of the document. For example, the rewriting unit can use AI to evaluate the submission date of a document. For example, the rewriting unit can evaluate the submission date of a document based on the submission deadline or submission date. The rewriting unit can also prioritize rewriting documents with approaching deadlines. For example, the rewriting unit can prioritize grammar and vocabulary suggestions for documents with approaching deadlines. Furthermore, the rewriting unit can postpone rewriting documents with distant submission dates. This allows for efficient rewriting by determining the priority of rewriting based on the submission date. Some or all of the above processes in the rewriting unit may be performed using AI or not. For example, the rewriting unit can input the submission date of the document into the AI ​​and have the AI ​​determine the priority of rewriting.

[0053] The rewriting unit can adjust the order of rewriting based on the relevance of the texts during the rewriting process. For example, the rewriting unit can use AI to evaluate the relevance of the texts. For instance, it can evaluate text relevance based on topic similarity or keyword co-occurrence. Furthermore, the rewriting unit can prioritize rewriting highly relevant texts. For example, it can prioritize grammatical and vocabulary suggestions for highly relevant texts. Additionally, the rewriting unit can postpone rewriting less relevant texts. This allows for efficient rewriting by adjusting the order of rewriting based on relevance. Some or all of the above processes in the rewriting unit may be performed using AI or not. For example, the rewriting unit can input the relevance of the texts into the AI ​​and have the AI ​​adjust the order of rewriting.

[0054] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. The service provider can, for example, use AI to analyze the user's past operation history. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also suggest the optimal display method based on the user's past operation history. For example, the service provider can automatically select a display method that the user has preferred to use in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. In this way, the optimal display method is selected by referring to the past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into AI and have the AI ​​perform the selection of the optimal display method.

[0055] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, the service provider can use AI to analyze the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a desktop, the service provider can provide a display method that includes detailed information. In this way, the optimal display method is selected by considering the device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0056] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, the service provider can use AI to analyze the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a desktop, the service provider can provide a display method that includes detailed information. In this way, the optimal display method is selected by considering the device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The input system can analyze user input in real time and provide appropriate feedback during the input process. For example, it can instantly point out grammatical errors or poor vocabulary choices while the user is typing and suggest corrections. The input system can also evaluate the structure and logical flow of the text before the user completes the input, suggesting paragraph rearrangement or strengthening logical connections as needed. Furthermore, the input system can analyze the user's typing speed and patterns and provide advice to improve typing efficiency. This allows users to create higher-quality texts while receiving real-time feedback.

[0059] The analysis unit can analyze the user's past input history and select the optimal analysis algorithm. For example, it can analyze patterns in sentences previously entered by the user and select the optimal grammar check algorithm. It can also analyze the frequency of use of specific vocabulary and expressions from the user's past input history and select the optimal vocabulary suggestion algorithm. Furthermore, the analysis unit can select the optimal structure analysis algorithm based on the user's past input history. As a result, by analyzing past input history, the optimal analysis algorithm is selected, enabling more accurate analysis.

[0060] The rewriting function can analyze the user's past rewriting history and select the optimal rewriting algorithm. For example, it can analyze patterns in texts previously rewritten by the user and select the optimal tone adjustment algorithm. It can also analyze the frequency of use of specific expressions and vocabulary from the user's past rewriting history and select the optimal expression suggestion algorithm. Furthermore, the rewriting function can select the optimal structure adjustment algorithm based on the user's past rewriting history. As a result, by analyzing past rewriting history, the optimal rewriting algorithm is selected, enabling more accurate rewriting.

[0061] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing display methods that the user has used in the past. It can also suggest the optimal display method based on the user's past operation history. For example, the service provider can automatically select a display method that the user has preferred to use in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. As a result, the optimal display method is selected by referring to past operation history, improving user convenience.

[0062] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a desktop, it can provide a display method that includes detailed information. In this way, by considering device information, the optimal display method is selected, improving user convenience.

[0063] The analysis unit can apply different analysis algorithms depending on the category of the document during analysis. For example, it can apply different analysis algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, a business-specific analysis algorithm can be applied. For instance, it can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, for creative writing, a creative-specific analysis algorithm can be applied. This allows for more accurate analysis by applying different analysis algorithms depending on the document category.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives text input from the user. This text can include business documents, emails, and creative writing. The reception desk can accept text via keyboard input, voice input, handwriting input, etc. Step 2: The analysis unit uses a generation AI to analyze the text input by the reception unit, and makes suggestions for grammar and vocabulary, and optimizes the structure. The analysis unit uses a grammar checking algorithm to analyze the grammar of the text and makes suggestions for appropriate grammar. It can also suggest appropriate vocabulary based on vocabulary selection criteria. Furthermore, it can make suggestions to optimize the structure of the text, such as paragraph placement and ensuring a logical flow. Step 3: The rewriting section uses generation AI to rewrite the text analyzed by the analysis section in a tone that suits the reader. The rewriting section can rewrite the text in a tone that suits the reader, such as a formal tone or a casual tone. It can also rewrite the text to make the expression more natural and effective. Step 4: The delivery unit provides the user with the rewritten text from the rewriting unit. The delivery unit can provide the user with the rewritten text by displaying it on the screen, sending it by email, printing it, or other methods.

[0066] (Example of form 2) The writing assistant system according to an embodiment of the present invention is an innovative system that supports writing by utilizing generative AI. This writing assistant system automatically suggests grammar and vocabulary, optimizes structure, and rewrites the text in a tone appropriate for the reader, simply by the user inputting text. It learns the user's writing style and provides personalized feedback the more it is used. Users can experience a noticeable improvement in their writing skills in a short period, enabling more natural and effective expression. For creative writing, the generative AI provides plot and idea generation functions, helping to streamline the writing process. It is also ideal for everyday communication and business, streamlining the creation of emails and presentation materials and helping to accurately express the intended message. For example, when a user inputs text, the generative AI analyzes it, suggests grammar and vocabulary, and optimizes structure. Furthermore, it rewrites the text in a tone appropriate for the reader, generating the final text. The generative AI learns the user's writing style and provides personalized feedback the more it is used. As a result, users can experience a noticeable improvement in their writing skills in a short period, enabling more natural and effective expression. In creative writing, generative AI provides plot and idea generation capabilities, helping to streamline the writing process. For example, in novel writing, generative AI can suggest plot ideas, allowing the user to proceed with writing based on those ideas. Similarly, in business document and email creation, generative AI efficiently generates text, helping to accurately express the intended message. Thus, a writing assistant system utilizing generative AI is an innovative tool that makes writing more enjoyable and effective. It elevates user writing to a new level, making it easy to create high-quality documents. In this way, the writing assistant system efficiently supports the user's writing process, resulting in a high-quality final product.

[0067] The writing assistant system according to this embodiment comprises a reception unit, an analysis unit, a rewriting unit, and a provision unit. The reception unit receives text input from the user. The text input by the user includes, but is not limited to, business documents, emails, and creative writing. The reception unit can receive text by methods such as keyboard input, voice input, and handwriting input. The analysis unit uses a generation AI to analyze the text input by the reception unit and makes suggestions for grammar and vocabulary, and optimizes the structure. The analysis unit, for example, uses a grammar check algorithm to analyze the grammar of the text and makes suggestions for appropriate grammar. The analysis unit can also make suggestions for appropriate vocabulary based on vocabulary selection criteria. Furthermore, the analysis unit can also make suggestions to optimize the structure of the text, such as paragraph placement and ensuring a logical flow. The rewriting unit uses a generation AI to rewrite the text analyzed by the analysis unit in a tone that suits the reader. The rewriting unit can rewrite the text in a tone that suits the reader, such as a formal tone or a casual tone. Furthermore, the rewriting unit can use a generation AI to rewrite the text to make the expression more natural and effective. The provisioning unit provides the user with the text rewritten by the rewriting unit. The provisioning unit can provide the user with the rewritten text by methods such as displaying it on the screen, sending it by email, or printing it. In this way, the writing assistant system according to the embodiment can efficiently support the user's writing and achieve a high-quality result. Some or all of the above-described processes in the analysis unit may be performed using a generation AI or not. For example, the analysis unit can input the text entered by the user into the generation AI and have the generation AI perform grammar and vocabulary suggestions and optimize the structure. Some or all of the above-described processes in the rewriting unit may be performed using a generation AI or not. For example, the rewriting unit can input the text analyzed by the analysis unit into the generation AI and have the generation AI perform rewriting in a tone that suits the reader. Some or all of the above-described processes in the provisioning unit may be performed using AI or not.For example, the delivery unit can input the text rewritten by the rewriting unit into the AI ​​and have the AI ​​execute the optimal method for providing it to the user.

[0068] The reception desk receives text input from the user. This text may include, but is not limited to, business documents, emails, or creative writing. The reception desk can accept text via methods such as keyboard input, voice input, or handwriting input. Specifically, keyboard input allows the user to input text using a computer or smartphone keyboard. Voice input uses technology to convert speech into text as the user speaks into a microphone. Handwriting input recognizes and converts handwritten characters using a tablet or stylus pen. This allows users to input text in the way that suits them best. Furthermore, the reception desk sends the input text to the analysis department in real time, allowing analysis to begin immediately. This enables users to create documents smoothly. The reception desk also saves and manages the input text, providing convenient features for users to re-edit or review it later. For example, it's possible to save the input text to the cloud and access it from different devices. This allows users to create documents regardless of location or device.

[0069] The analysis unit uses a generation AI to analyze the text input by the reception unit, making suggestions for grammar and vocabulary, and optimizing the structure. For example, the analysis unit uses a grammar checking algorithm to analyze the grammar of the text and suggest appropriate grammar. The analysis unit can also suggest appropriate vocabulary based on vocabulary selection criteria. Furthermore, the analysis unit can suggest ways to optimize the structure of the text, such as paragraph placement and ensuring logical flow. Specifically, the generation AI uses natural language processing technology to detect grammatical errors in the input text and suggests corrections. For example, it checks for subject-verb agreement, tense agreement, and appropriate punctuation placement. The generation AI also suggests more appropriate vocabulary according to the tone and style of the text. For example, it suggests formal vocabulary for business documents and more expressive vocabulary for creative writing. Furthermore, the generation AI analyzes the overall structure of the text and suggests ways to optimize paragraph order and logical flow. For example, it checks whether the introduction, body text, and conclusion are properly arranged and suggests moving or adding paragraphs as needed. In this way, the analysis unit provides support to refine the user's input into high-quality text.

[0070] The rewriting unit uses generative AI to rewrite text analyzed by the analysis unit in a tone appropriate to the reader. For example, the rewriting unit can rewrite text in a formal tone, a casual tone, or any other tone suitable for the reader. Furthermore, the rewriting unit can use generative AI to rewrite text to make the expression more natural and effective. Specifically, the generative AI selects an appropriate tone and style according to the content and purpose of the text, and rewrites the entire text to match that tone. For example, it uses polite language and honorifics in business documents, and friendly language in casual emails. The generative AI also improves the flow and rhythm of the text, making it easier to understand and more engaging for the reader. For example, it condenses redundant expressions and adds expressions to emphasize important points. Moreover, the rewriting unit can continuously improve the accuracy and effectiveness of its rewriting based on user feedback. For example, users can provide evaluations and comments on the rewritten text, allowing the generative AI to learn from this feedback and incorporate it into future rewrites. This allows the rewriting department to consistently provide high-quality rewrites and meet user needs.

[0071] The delivery department provides users with rewritten documents created by the rewriting department. The delivery department can provide users with rewritten documents in various ways, such as displaying them on a screen, sending them via email, or printing them. Specifically, the delivery department can display the rewritten documents on the user's device in real time, allowing for immediate review. Users can also choose to have the rewritten documents sent via email for sharing with other relevant parties. Furthermore, the delivery department provides a printing format for the rewritten documents, enabling users to utilize them as physical documents. Considering user convenience, the delivery department offers multiple delivery methods, allowing users to receive the rewritten documents in the most suitable way. The delivery department also stores and manages the rewritten documents, providing convenient features for later reuse and editing. For example, rewritten documents can be stored in the cloud and accessed from different devices. This allows the delivery department to provide users with high convenience and flexibility, improving the efficiency of document creation.

[0072] The analysis unit can make grammatical and vocabulary suggestions using generative AI. For example, the analysis unit can use generative AI to analyze the grammar of a text and suggest appropriate grammar. For example, the generative AI can use a grammar checking algorithm to analyze the grammar of a text and suggest grammatically correct expressions. The analysis unit can also use generative AI to analyze the vocabulary of a text and suggest appropriate vocabulary. For example, the generative AI can suggest vocabulary suitable for the text based on vocabulary selection criteria. Furthermore, the analysis unit can use generative AI to analyze the structure of a text and optimize its structure, such as paragraph placement and ensuring logical flow. This improves the accuracy of grammatical and vocabulary suggestions by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input text entered by the user into the generation AI and have the generation AI perform grammatical and vocabulary suggestions.

[0073] The analysis unit can optimize the structure using a generative AI. For example, the analysis unit can use a generative AI to analyze the structure of a document and optimize it, such as by arranging paragraphs and ensuring a logical flow. For example, the generative AI can analyze the arrangement of each paragraph in a document and propose the optimal arrangement to ensure a logical flow. The analysis unit can also use a generative AI to analyze the logical flow of a document and propose an appropriate structure. For example, the generative AI can analyze the logical flow of a document and propose ways to strengthen the connections between paragraphs. Furthermore, the analysis unit can use a generative AI to analyze the overall structure of a document and propose an optimal structure. In this way, the structure of the document is optimized by using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input a document entered by a user into a generative AI and have the generative AI perform the structure optimization.

[0074] The rewriting function can rewrite text in a tone appropriate to the reader using a generative AI. For example, the rewriting function can use a generative AI to analyze the tone of the text and rewrite it in a tone appropriate to the reader. For example, the generative AI can rewrite the text in a tone appropriate to the reader, such as a formal tone or a casual tone. The rewriting function can also use a generative AI to rewrite the text in a way that makes the expression more natural and effective. For example, the generative AI can analyze the expression of the text and suggest more natural and effective expressions. Furthermore, the rewriting function can use a generative AI to adjust the tone of the text and rewrite it in a tone appropriate to the reader. In this way, it becomes possible to rewrite text in a tone appropriate to the reader by using a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the rewriting function may be performed using a generative AI or not. For example, the rewriting unit inputs the text analyzed by the analysis unit into the generation AI, which then performs a rewrite in a tone that suits the reader.

[0075] The service provider can provide the rewritten text to the user. The service provider can provide the rewritten text to the user in ways such as displaying it on a screen, sending it by email, or printing it. For example, the service provider can display the rewritten text to the user through a web application or a mobile application. The service provider can also provide the rewritten text to the user by sending it by email. Furthermore, the service provider can print the rewritten text and provide it to the user in paper form. This improves user convenience by providing the user with the rewritten text. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the text rewritten by the rewriting service provider into the AI ​​and have the AI ​​execute the optimal method for providing it to the user.

[0076] The analysis unit can learn the user's writing style using a generative AI and provide personalized feedback. For example, the analysis unit can use a generative AI to analyze the user's past writings and learn the user's writing style. For example, the generative AI analyzes the user's past writings and learns the user's writing style and vocabulary choices. The analysis unit can also use a generative AI to provide personalized feedback based on the user's writing style. For example, the generative AI provides feedback on grammar, vocabulary choices, and structural optimization based on the user's past writings. Furthermore, the analysis unit can use a generative AI to continuously learn the user's writing style and improve the accuracy of the feedback. This allows for the provision of personalized feedback by learning the user's writing style. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the user's past writings into the generation AI, allowing the AI ​​to learn the user's writing style and provide feedback.

[0077] The rewriting section can provide plot and idea generation functions using generative AI. For example, the rewriting section uses generative AI to generate plots and ideas in creative writing. For example, the generative AI can suggest plot ideas in novel writing. The rewriting section can also use generative AI to generate ideas in the creation of business documents and emails. For example, the generative AI can suggest ideas for the structure and content of business documents. Furthermore, the rewriting section can use generative AI to support the user in generating ideas in their writing. This allows for smoother plot and idea generation by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the rewriting section may be performed using generative AI or not. For example, the rewriting section can have the generative AI perform the generation of plots and ideas in the user's writing.

[0078] The service provider can streamline the creation of business documents and emails. For example, the service provider can use generative AI to streamline the creation of business documents and emails. For example, the generative AI can analyze the format and content of business documents and propose efficient creation methods. The service provider can also use generative AI to streamline the creation of emails. For example, the generative AI can analyze the content of emails and propose appropriate expressions and structures. Furthermore, the service provider can use generative AI to support the creation of business documents and emails. This streamlines the creation of business documents and emails. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can have a generative AI create business documents and emails.

[0079] The service provider can accurately express the message to be conveyed. For example, the service provider can use generative AI to accurately express the message to be conveyed. For example, the generative AI can analyze the main points of a text and suggest appropriate ways of expressing them. The service provider can also use generative AI to suggest expressions to emphasize the main points of the message. For example, the generative AI can suggest expressions to emphasize important parts of a text. Furthermore, the service provider can use generative AI to provide support for effectively expressing the message to be conveyed. This ensures that the message to be conveyed is accurately expressed. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can have the generative AI execute the message to be conveyed.

[0080] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated emotions. For example, the reception unit estimates the user's emotions using an emotion estimation algorithm. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can estimate emotions based on fluctuations in heart rate. This allows for more appropriate text input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, or they may not be performed using AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of input timing.

[0081] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can use AI to analyze the user's past input history. For example, the reception desk can analyze patterns in text previously entered by the user and suggest the optimal input method. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. Furthermore, the reception desk can select the optimal input method based on the user's past input history. In this way, the optimal input method is selected by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​select the optimal input method.

[0082] The reception desk can filter the user's current projects and areas of interest when they input text. For example, the reception desk can use AI to analyze the user's current projects and areas of interest. For example, the reception desk can analyze data from project management tools and the user's search history to filter relevant information. The reception desk can also prioritize displaying keywords related to the user's current projects. For example, the reception desk can prioritize displaying information related to the project the user is currently working on. Furthermore, the reception desk can filter and display relevant information based on the user's areas of interest. This ensures that highly relevant information is provided by filtering based on the current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's current projects and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0083] The reception unit can estimate the user's emotions and determine the priority of the text to be entered based on the estimated emotions. For example, the reception unit estimates the user's emotions using an emotion estimation algorithm. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can estimate emotions based on fluctuations in heart rate. This allows for more appropriate text input by determining the priority of the text to be entered according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI or not. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation and determine the priority of the text to be input.

[0084] The reception desk can prioritize inputting highly relevant text by considering the user's geographical location information when text is entered. For example, the reception desk can use AI to analyze the user's geographical location information. For example, the reception desk can analyze GPS data or IP addresses to determine the user's current location. The reception desk can also prioritize inputting information related to a specific region if the user is in that region. For example, if the reception desk is traveling, it will prioritize inputting information related to the travel destination. Furthermore, the reception desk can filter and display highly relevant information based on the user's geographical location information. As a result, highly relevant text is prioritized by considering geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​perform the filtering of highly relevant information.

[0085] The reception desk can analyze the user's social media activity and input relevant text when text is entered. For example, the reception desk can use AI to analyze the user's social media activity. For example, the reception desk can analyze the content of the user's social media posts and input relevant information. The reception desk can also prioritize inputting keywords that the user frequently uses on social media. For example, the reception desk can analyze the interests of the user's social media followers and input relevant information. Furthermore, the reception desk can filter and display highly relevant information based on the user's social media activity. In this way, relevant text is input by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the grammar and vocabulary suggestion methods based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on fluctuations in heart rate. This allows for more appropriate suggestions by adjusting the grammar and vocabulary suggestion methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of grammar and vocabulary suggestion methods.

[0087] The analysis unit can adjust the level of detail of its analysis based on the importance of the text. For example, the analysis unit can use AI to evaluate the importance of a text. For example, the analysis unit can evaluate the importance of a text based on the frequency of keyword occurrences or the type of document. Furthermore, for important texts, the analysis unit can perform a detailed analysis and make suggestions for grammar and vocabulary. For example, the analysis unit can perform a detailed grammatical check and vocabulary selection for important texts. In addition, for general texts, the analysis unit can perform a basic analysis and make simple suggestions for grammar and vocabulary. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the text. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the text into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the document during analysis. For example, the analysis unit can use AI to classify the category of the document. For example, the analysis unit can apply different analysis algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, the analysis unit can apply a business-specific analysis algorithm. For example, the analysis unit can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, in the case of creative writing, the analysis unit can apply a creative-specific analysis algorithm. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the category of the document into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and determine the priority of grammar and vocabulary suggestions based on the estimated emotions. For example, the analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit estimates emotions based on fluctuations in heart rate. This allows for more appropriate suggestions by determining the priority of grammar and vocabulary suggestions according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and determine the priority of grammar and vocabulary suggestions.

[0090] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis process. For example, the analysis unit can use AI to evaluate the submission date of documents. For example, the analysis unit can evaluate the submission date of documents based on the submission deadline or submission date. The analysis unit can also prioritize the analysis of documents with approaching deadlines. For example, the analysis unit can prioritize providing grammatical and vocabulary suggestions for documents with approaching submission deadlines. Furthermore, the analysis unit can postpone the analysis of documents with distant submission dates. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the submission date of the documents into the AI ​​and have the AI ​​determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the texts during analysis. For example, the analysis unit can use AI to evaluate the relevance of texts. For example, the analysis unit can evaluate the relevance of texts based on the degree of topic match or keyword co-occurrence. The analysis unit can also prioritize the analysis of highly relevant texts. For example, the analysis unit can prioritize grammatical and vocabulary suggestions for highly relevant texts. Furthermore, the analysis unit can postpone the analysis of less relevant texts. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of texts into the AI ​​and have the AI ​​adjust the order of analysis.

[0092] The rewriting unit can estimate the user's emotions and adjust the rewriting method based on the estimated emotions. For example, the rewriting unit estimates the user's emotions using an emotion estimation algorithm. For example, the rewriting unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The rewriting unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the rewriting unit can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the rewriting unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the rewriting unit can estimate emotions based on fluctuations in heart rate. This allows for more appropriate rewriting by adjusting the rewriting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the rewriting section may be performed using AI or not. For example, the rewriting section can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the rewriting expression method.

[0093] The rewriting function can adjust the level of detail in the rewriting process based on the importance of the text. For example, the rewriting function can use AI to evaluate the importance of a text. For instance, it might evaluate the importance based on keyword frequency or document type. Furthermore, for important texts, the rewriting function can perform detailed rewriting and offer grammatical and vocabulary suggestions. For example, it can perform detailed grammatical checks and vocabulary selection for important texts. Additionally, for general texts, it can perform basic rewriting and offer simple grammatical and vocabulary suggestions. This allows for efficient rewriting by adjusting the level of detail based on the importance of the text. Some or all of the above processes in the rewriting function may be performed using AI or not. For example, the rewriting function can input the importance of the text into the AI ​​and have the AI ​​adjust the level of detail in the rewriting.

[0094] The rewriting function can apply different rewriting algorithms depending on the category of the text during the rewriting process. For example, the rewriting function can use AI to classify the category of the text. For instance, the rewriting function can apply different rewriting algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, the rewriting function can apply a business-specific rewriting algorithm. For example, the rewriting function can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, in the case of creative writing, the rewriting function can apply a creative-specific rewriting algorithm. This allows for more appropriate rewriting by applying different rewriting algorithms depending on the category of the text. Some or all of the above-described processes in the rewriting function may be performed using AI or not. For example, the rewriting function can input the category of the text into the AI ​​and have the AI ​​execute the application of different rewriting algorithms.

[0095] The rewriting unit can estimate the user's emotions and adjust the length of the rewrite based on the estimated emotions. For example, the rewriting unit can estimate the user's emotions using an emotion estimation algorithm. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, the rewriting unit can record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to estimate emotions. Furthermore, the rewriting unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can estimate emotions based on fluctuations in heart rate. This allows for more appropriate rewriting by adjusting the length of the rewrite according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the rewriting section may be performed using AI, or they may not be performed using AI. For example, the rewriting section can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the rewriting length.

[0096] The rewriting unit can determine the priority of rewriting based on the submission date of the document. For example, the rewriting unit can use AI to evaluate the submission date of a document. For example, the rewriting unit can evaluate the submission date of a document based on the submission deadline or submission date. The rewriting unit can also prioritize rewriting documents with approaching deadlines. For example, the rewriting unit can prioritize grammar and vocabulary suggestions for documents with approaching deadlines. Furthermore, the rewriting unit can postpone rewriting documents with distant submission dates. This allows for efficient rewriting by determining the priority of rewriting based on the submission date. Some or all of the above processes in the rewriting unit may be performed using AI or not. For example, the rewriting unit can input the submission date of the document into the AI ​​and have the AI ​​determine the priority of rewriting.

[0097] The rewriting unit can adjust the order of rewriting based on the relevance of the texts during the rewriting process. For example, the rewriting unit can use AI to evaluate the relevance of the texts. For instance, it can evaluate text relevance based on topic similarity or keyword co-occurrence. Furthermore, the rewriting unit can prioritize rewriting highly relevant texts. For example, it can prioritize grammatical and vocabulary suggestions for highly relevant texts. Additionally, the rewriting unit can postpone rewriting less relevant texts. This allows for efficient rewriting by adjusting the order of rewriting based on relevance. Some or all of the above processes in the rewriting unit may be performed using AI or not. For example, the rewriting unit can input the relevance of the texts into the AI ​​and have the AI ​​adjust the order of rewriting.

[0098] The service provider can estimate the user's emotions and adjust the display method of the text based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate the emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the service provider can estimate emotions based on fluctuations in heart rate. This allows for more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using AI or not. For example, the service provision unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the display method.

[0099] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. The service provider can, for example, use AI to analyze the user's past operation history. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also suggest the optimal display method based on the user's past operation history. For example, the service provider can automatically select a display method that the user has preferred to use in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. In this way, the optimal display method is selected by referring to the past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past operation history into AI and have the AI ​​perform the selection of the optimal display method.

[0100] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, the service provider can use AI to analyze the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a desktop, the service provider can provide a display method that includes detailed information. In this way, the optimal display method is selected by considering the device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0101] The service provider can estimate the user's emotions and determine the priority of the text to be provided based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate the emotions using voice analysis technology. For example, the service provider can analyze the tone and speed of the voice and estimate the emotions. Furthermore, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, the service provider can estimate emotions based on fluctuations in heart rate. This enables the provision of more appropriate text by determining the priority of the text 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. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and determine the priority of the text to be provided.

[0102] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, the service provider can use AI to analyze the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the service provider is using a desktop, the service provider can provide a display method that includes detailed information. In this way, the optimal display method is selected by considering the device information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's device information into AI and have the AI ​​select the optimal display method.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The input system can analyze user input in real time and provide appropriate feedback during the input process. For example, it can instantly point out grammatical errors or poor vocabulary choices while the user is typing and suggest corrections. The input system can also evaluate the structure and logical flow of the text before the user completes the input, suggesting paragraph rearrangement or strengthening logical connections as needed. Furthermore, the input system can analyze the user's typing speed and patterns and provide advice to improve typing efficiency. This allows users to create higher-quality texts while receiving real-time feedback.

[0105] The analysis unit can estimate the user's emotions and adjust its grammar and vocabulary suggestions based on those estimates. For example, if the user is stressed, the analysis unit will provide concise and clear suggestions; if the user is relaxed, it will provide more detailed and complex suggestions. Furthermore, if the user is excited, the analysis unit can suggest expressions that reflect those emotions; if the user is calm, it can suggest calm and logical expressions. In addition, the analysis unit can track changes in the user's emotions in real time and dynamically adjust its suggestion methods. This enables the provision of optimal grammar and vocabulary suggestions tailored to the user's emotions.

[0106] The rewriting function can estimate the user's emotions and adjust the rewriting style based on those emotions. For example, if the user is sad, the rewriting function will use a gentle tone; if the user is happy, it will use a bright and positive tone. Similarly, if the user is angry, it will use a calm and composed tone; and if the user is feeling anxious, it can use a reassuring tone. Furthermore, the rewriting function can track changes in the user's emotions in real time and dynamically adjust the rewriting style. This enables optimal rewriting tailored to the user's emotions.

[0107] The service provider can estimate the user's emotions and adjust how the text is displayed based on those emotions. For example, if the user is tired, the service provider will display the text in a simple, easy-to-read format; if the user is focused, it will display the text in a format that includes detailed information. Furthermore, if the user is relaxed, the service provider can display the text in a casual format; if the user is stressed, it can display the text in a formal format. In addition, the service provider can track changes in the user's emotions in real time and dynamically adjust the display method. This ensures that the optimal display method is provided according to the user's emotions.

[0108] The delivery unit can estimate the user's emotions and prioritize the text it delivers based on those emotions. For example, if the user is in a hurry, the delivery unit will prioritize displaying important information; if the user is relaxed, it will prioritize text containing detailed information. Furthermore, if the user is stressed, the delivery unit can prioritize displaying concise and clear information; and if the user is excited, it can prioritize displaying information that reflects their emotions. In addition, the delivery unit can track changes in the user's emotions in real time and dynamically adjust the priority of the text it delivers. This enables the delivery of optimal text tailored to the user's emotions.

[0109] The analysis unit can analyze the user's past input history and select the optimal analysis algorithm. For example, it can analyze patterns in sentences previously entered by the user and select the optimal grammar check algorithm. It can also analyze the frequency of use of specific vocabulary and expressions from the user's past input history and select the optimal vocabulary suggestion algorithm. Furthermore, the analysis unit can select the optimal structure analysis algorithm based on the user's past input history. As a result, by analyzing past input history, the optimal analysis algorithm is selected, enabling more accurate analysis.

[0110] The rewriting function can analyze the user's past rewriting history and select the optimal rewriting algorithm. For example, it can analyze patterns in texts previously rewritten by the user and select the optimal tone adjustment algorithm. It can also analyze the frequency of use of specific expressions and vocabulary from the user's past rewriting history and select the optimal expression suggestion algorithm. Furthermore, the rewriting function can select the optimal structure adjustment algorithm based on the user's past rewriting history. As a result, by analyzing past rewriting history, the optimal rewriting algorithm is selected, enabling more accurate rewriting.

[0111] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing display methods that the user has used in the past. It can also suggest the optimal display method based on the user's past operation history. For example, the service provider can automatically select a display method that the user has preferred to use in the past. Furthermore, the service provider can select the optimal display method based on the user's past operation history. As a result, the optimal display method is selected by referring to past operation history, improving user convenience.

[0112] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a desktop, it can provide a display method that includes detailed information. In this way, by considering device information, the optimal display method is selected, improving user convenience.

[0113] The analysis unit can apply different analysis algorithms depending on the category of the document during analysis. For example, it can apply different analysis algorithms based on categories such as business documents, creative writing, and academic papers. Furthermore, in the case of business documents, a business-specific analysis algorithm can be applied. For instance, it can analyze the structure and content of business documents and suggest appropriate grammar and vocabulary. In addition, for creative writing, a creative-specific analysis algorithm can be applied. This allows for more accurate analysis by applying different analysis algorithms depending on the document category.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reception desk receives text input from the user. This text can include business documents, emails, and creative writing. The reception desk can accept text via keyboard input, voice input, handwriting input, etc. Step 2: The analysis unit uses a generation AI to analyze the text input by the reception unit, and makes suggestions for grammar and vocabulary, and optimizes the structure. The analysis unit uses a grammar checking algorithm to analyze the grammar of the text and makes suggestions for appropriate grammar. It can also suggest appropriate vocabulary based on vocabulary selection criteria. Furthermore, it can make suggestions to optimize the structure of the text, such as paragraph placement and ensuring a logical flow. Step 3: The rewriting section uses generation AI to rewrite the text analyzed by the analysis section in a tone that suits the reader. The rewriting section can rewrite the text in a tone that suits the reader, such as a formal tone or a casual tone. It can also rewrite the text to make the expression more natural and effective. Step 4: The delivery unit provides the user with the rewritten text from the rewriting unit. The delivery unit can provide the user with the rewritten text by displaying it on the screen, sending it by email, printing it, or other methods.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reception unit, analysis unit, rewriting 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 reception device 38 of the smart device 14 and receives text by methods such as keyboard input, voice input, and handwriting input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generation AI to suggest grammar and vocabulary and optimize the structure. The rewriting unit is implemented by the control unit 46A of the smart device 14 and rewrites the text in a tone that suits the reader. The provision unit is implemented by the output device 40 of the smart device 14 and displays the rewritten text on the screen. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reception unit, analysis unit, rewriting 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 microphone 238 of the smart glasses 214 and receives text via voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generation AI to suggest grammar and vocabulary and optimize the structure. The rewriting unit is implemented by the control unit 46A of the smart glasses 214 and rewrites the text in a tone that suits the reader. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the rewritten text as audio. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, rewriting unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives text via voice input. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generation AI to suggest grammar and vocabulary and optimize the structure. The rewriting unit is implemented by, for example, the control unit 46A of the headset terminal 314 and rewrites the text in a tone appropriate to the reader. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and displays the rewritten text on the screen. 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, analysis unit, rewriting unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives text via voice input. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generation AI to suggest grammar and vocabulary and optimize the structure. The rewriting unit is implemented by, for example, the control unit 46A of the robot 414 and rewrites the text in a tone suitable for the reader. The delivery unit is implemented by, for example, the speaker 240 of the robot 414 and provides the rewritten text in voice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A reception area where the user enters text, The analysis unit analyzes the text input by the reception unit and performs grammar and vocabulary suggestions and optimizes the structure. A rewriting unit rewrites the text analyzed by the aforementioned analysis unit in a tone suitable for the reader, The system includes a providing unit that provides the user with the text rewritten by the rewriting unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Generative AI is used to suggest grammar and vocabulary. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Optimize the configuration using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned rewrite section is The AI ​​generates the text to rewrite it in a tone that suits the reader. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide users with rewritten text. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The generative AI learns the user's writing style and provides personalized feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned rewrite section is The generation AI provides the ability to generate plots and ideas. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Streamline the creation of business documents and emails. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, To accurately express the message you want to convey. 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 adjusts the timing of text input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering text, 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 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the text to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When entering text, the system prioritizes inputting highly relevant text by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When you enter text, the system analyzes your social media activity and inputs relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the grammar and vocabulary suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the text. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and prioritizes grammar and vocabulary suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the documents were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned rewrite section is The system estimates the user's emotions and adjusts the rewritten text based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned rewrite section is When rewriting, adjust the level of detail based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned rewrite section is When rewriting, different rewriting algorithms are applied depending on the category of the text. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned rewrite section is The system estimates the user's emotions and adjusts the length of the rewrite based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned rewrite section is When rewriting, prioritize the rewrites based on the submission deadline of the document. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned rewrite section is When rewriting, adjust the order of the rewritten text based on its relevance. 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 adjusts how the text is displayed based on those estimated 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 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 30) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 where the user enters text, The analysis unit analyzes the text input by the reception unit and performs grammar and vocabulary suggestions and optimizes the structure. A rewriting unit rewrites the text analyzed by the aforementioned analysis unit in a tone suitable for the reader, The system includes a providing unit that provides the user with the text rewritten by the rewriting unit. A system characterized by the following features.

2. The aforementioned analysis unit, Generative AI is used to suggest grammar and vocabulary. The system according to feature 1.

3. The aforementioned analysis unit, Optimize the configuration using generative AI. The system according to feature 1.

4. The aforementioned rewrite section is The AI ​​generates the text to be rewritten in a tone that suits the reader. The system according to feature 1.

5. The aforementioned supply unit is, Provide users with rewritten text. The system according to feature 1.

6. The aforementioned analysis unit, Generative AI learns the user's writing style and provides personalized feedback. The system according to feature 1.

7. The aforementioned rewrite section is The AI ​​provides a function to generate plots and ideas. The system according to feature 1.

8. The aforementioned supply unit is, Streamline the creation of business documents and emails. The system according to feature 1.

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