system

Abstract

We provide the system. [Solution] A means of receiving information, An analytical means for analyzing the acquired information, An automated correction mechanism for correcting information based on the analysis results, An evaluation means for evaluating corrected information and generating a response, An output means for outputting information based on the reaction, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In document creation, problems such as various formats, inappropriate configurations, and reduced readability often occur, which may impair the consistency of documents among creators. Also, the document correction work may take a lot of time, causing a problem of reducing the overall work efficiency.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that receives data using a receiving means and analyzes its contents using an analysis means. The analysis means utilizes natural language processing technology to detect grammatical errors and structural deficiencies in the data. For detected problems, an automatic correction means performs corrections, including unifying the format and font size. Furthermore, an evaluation means reviews the corrected data and generates feedback, so that the user can ultimately obtain consistent, high-quality data through an output means.

[0006] "Documents" refer to materials that organize information and express it visually or in writing, and are used in presentations, reports, and other similar materials.

[0007] "Receiving means" refers to a technical method or device for taking in information from an external source, and which has the function of acquiring data via a network.

[0008] "Analysis methods" refer to the process and techniques for examining received materials in detail, understanding their content and structure, and classifying them.

[0009] An "automatic correction mechanism" is a method or device for mechanically correcting discovered errors or inconsistencies, and has the function of improving the consistency and readability of the document.

[0010] "Evaluation means" refers to a technical method or apparatus for evaluating revised materials and determining their quality, and for generating feedback aimed at producing high-quality deliverables.

[0011] "Output means" refers to a method or device that provides processed data or information in a format usable by the user, and has the function of outputting the final material as print or an electronic file.

[0012] "Natural language processing technology" is an information technology aimed at processing and understanding human language using computers, and includes applications such as text analysis and speech recognition. [Brief explanation of the drawing]

[0013] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be described.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] The 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.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is implemented as a system to streamline the document creation process and improve quality. In one embodiment, a user uploads a document from their terminal, and various processes are performed on a server. The server first receives the document from the user using a receiving means. At this stage, the document format can be Word, PDF, or text file, etc.

[0035] Next, the server analyzes the document using analysis tools. This involves using natural language processing techniques to automatically detect grammatical errors and structural inconsistencies within the document, and extracting the main themes and key points of the document.

[0036] Based on the analysis results, the server uses automated correction mechanisms to improve the document. This process includes unifying fonts and paragraph styles, and correcting the placement of images and graphs appropriately, thereby improving the consistency and readability of the document.

[0037] The revised documents are reviewed by the server's evaluation system. This evaluation process assesses the overall quality of the documents and generates user-friendly feedback.

[0038] Finally, the user reviews the document based on the feedback and makes any necessary adjustments. The revised document is then provided as the final version via the server's output system. This ensures that the user receives high-quality documentation.

[0039] For example, when a user creates a business presentation, redundant sections are identified in the input material, and a summary of those sections is suggested by the server. Furthermore, inappropriate formatting and inconsistent fonts in the material are automatically corrected, resulting in a polished presentation document. This reduces the effort involved in creating documents and enables the efficient creation of consistent, high-quality materials.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users upload documents they are creating to the system using their devices. Supported formats include Word, PDF, and text files.

[0043] Step 2:

[0044] The server receives uploaded materials using a receiving mechanism and converts them into a format suitable for content analysis. If necessary, OCR technology is used to recognize PDF documents as text data.

[0045] Step 3:

[0046] The server uses analysis tools to analyze the document. Natural language processing technology is used to analyze the grammatical structure and topic of the document, identifying errors and areas for improvement.

[0047] Step 4:

[0048] The server uses automated correction mechanisms to automatically correct the documents based on the analysis results. Specifically, this includes unifying formats and fonts, and rearranging graphs and figures.

[0049] Step 5:

[0050] The server uses evaluation tools to assess the revised document and generates feedback. This feedback includes an overall quality assessment of the document and suggestions for further revisions.

[0051] Step 6:

[0052] The user receives feedback from the server via their device and makes necessary corrections. The user then performs a final review of the content.

[0053] Step 7:

[0054] The server provides the final version of the document to the terminal via an output device, making high-quality materials available to the user.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In today's information society, the business and academic fields require the creation of large volumes of documents. However, document creation is time-consuming and labor-intensive, and the tasks of structuring documents, ensuring visual consistency, and organizing content are particularly complex. Furthermore, maintaining document quality while efficiently completing the process is not easy. It is necessary to address these challenges and achieve efficient and high-quality document creation.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes receiving means for receiving information, analyzing means for analyzing information, and automatic correction means for correcting information based on the analysis results. This automates the document creation process and enables efficient structural analysis of documents, extraction of key points, and ensuring visual consistency.

[0060] "Information" refers to the data and content that make up documents and materials, and includes all forms, such as text, graphs, and images.

[0061] "Receiving means" refers to the functions or devices used by a server to acquire information transmitted from an external source.

[0062] "Analysis means" refers to methods and devices for analyzing received information and evaluating and extracting grammatical errors and the structure of the content.

[0063] "Automatic correction means" refers to functions or devices that improve the formatting and visual consistency of information based on analysis results.

[0064] "Evaluation means" refers to functions or devices for evaluating the quality of corrected information and generating opinions based on the results.

[0065] "Output means" refers to the methods or functions for providing the user with the final, adjusted information.

[0066] "Natural language processing technology" refers to various algorithms and methods that enable computers to understand and analyze human language.

[0067] "Visual consistency" refers to a state where fonts, paragraph styles, and the placement of figures and tables are unified within a document.

[0068] This invention is a system for streamlining information processing and improving the quality of documents. Users can select documents from their own terminals and send them to the server. The terminals can handle document formats such as Word, PDF, and text files.

[0069] The server is equipped with a dedicated receiving mechanism for receiving information, thereby accurately collecting data from external sources. Next, the received information is processed by an analysis mechanism. This process utilizes natural language processing techniques to automatically analyze grammatical errors and the structure of the information. For example, it can extract the main themes and key points from a business report. This analysis uses a generative AI model and a prompt such as, "Please extract the key points from this text."

[0070] Based on the analysis results, the server uses automated correction mechanisms to improve the information. Specifically, formatting and fonts within the document are corrected, and the positions of images and graphs are adjusted. This process improves the visual consistency and readability of the information. For example, in presentation materials, different fonts are unified and images are placed appropriately, making the information easier to understand and organize.

[0071] After revisions are complete, the document is reviewed by the server's evaluation system. This evaluation process checks the overall quality of the document and provides clear feedback to the user. The user then revises the document based on this feedback and makes any necessary adjustments. Finally, the server's output system generates the final version of the document and provides it to the user. This system allows users to efficiently create high-quality, well-organized documents.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user selects a document from their device and uploads it to the server. The input is a Word, PDF, or text file located on the user's device, and the server receives that file as output. Specifically, the user selects a document file and submits it to the server using a dedicated upload form.

[0075] Step 2:

[0076] The server receives information sent by the user using a receiving device. The input is the file itself sent by the user, and the output is the storage of that information within the server. At this stage, the server checks the file format and determines whether it is a valid file.

[0077] Step 3:

[0078] The server analyzes the received information using analysis tools. The input is the received file, and the output is the text data resulting from the analysis. Specifically, it uses natural language processing techniques to detect grammatical errors in the document and extract the main themes and key points. A generative AI model is used here, and prompts such as "Please extract the important points of this text" are used.

[0079] Step 4:

[0080] The server uses automated correction mechanisms based on the analysis results to improve the information. The input is the analyzed text data, and the output is a corrected formatted document. Specific actions include unifying fonts, unifying paragraph styles, and properly positioning images and graphs.

[0081] Step 5:

[0082] The server evaluates the corrected document using evaluation tools. The input is the corrected document, and the output is feedback to be provided to the user. Here, the overall quality of the document is checked, evaluated in terms of readability and consistency, and feedback that is easy for the user to understand is generated.

[0083] Step 6:

[0084] The user reviews feedback from the server and makes a final check of the document. The input is the feedback message provided by the server, and the output is the final document with the corrections made. The user adjusts the document based on the feedback and corrects the content if necessary.

[0085] Step 7:

[0086] The server outputs the user-edited document as the final version. The input is the user-edited document, and the output is the final version of the document provided to the user. At this stage, the server exports the document in the specified format and prepares it for the user to download.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Generating high-quality and consistent documents automatically is difficult in information creation. Furthermore, manual document compilation by users is time-consuming, labor-intensive, and increases the risk of errors. In particular, efficient and highly accurate automation methods are needed to organize complex information clearly and quickly create user guides in areas such as electronic payment services.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes a receiving means for acquiring information, an analysis means for analyzing the acquired information, and an automatic correction means for correcting the information based on the analysis results. This enables high-quality and consistent information distribution through the automatic generation of documents.

[0092] "Information" refers to data and knowledge expressed in a tangible form, which are processed for the purpose of understanding and utilizing their content.

[0093] A "receiving means" is a device or element that has the function of collecting information sent from an external source and plays the role of incorporating this information into the system.

[0094] "Analysis means" refers to techniques and devices used to examine acquired information in detail and identify its structure and content.

[0095] "Automatic correction methods" refer to technologies and means for automatically adjusting documents and data to correct errors in information based on analysis results and to ensure consistency.

[0096] "Evaluation means" refers to devices or elements that have the function of determining the quality and validity of corrected information and generating responses to appropriately convey that information to users.

[0097] "Output means" refers to devices or elements that have the function of providing processed and evaluated information to the user in its final form.

[0098] In a mode for carrying out the invention, the system that realizes this application example is mainly composed of a program that runs on a server. The server receives information, analyzes it, and automatically makes corrections.

[0099] The server first receives information from the user. At this stage, the information can take the form of text files or digital documents. The received information is then analyzed using natural language processing software such as "spaCy" or "transformers" for text analysis. This analysis includes checking grammatical structure and extracting key points.

[0100] Next, based on the analysis results, the information is corrected using an automated correction function. This process involves correcting typographical errors, converting to a unified format, and restructuring the information. It is common to use data processing libraries such as "Pandas" or "NumPy" for this stage.

[0101] At the end of the process, the corrected information is evaluated, and a final response is generated. The evaluation process verifies the integrity and consistency of the information and provides feedback to the user in an easy-to-understand format. This enables the automatic generation of documents such as user guides and FAQs for electronic payment services.

[0102] For example, if a user is trying to create a guide about a new cashback feature, the server will analyze information about that feature and generate text in a way that is easy for users to understand. A generative AI model may be used in this process. An example of a prompt might be, "Carefully analyze the following text, correct any errors, and transform it into an effective user guide."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] Users send information to the server using their own devices. The information is in the form of text files or digital documents. The server collects this information through receiving mechanisms. The input is information from the user, and the output is the original data stored on the server.

[0106] Step 2:

[0107] The server analyzes the received information using analysis tools. This analysis uses natural language processing software such as "spaCy" and "transformers." Specifically, it performs grammar checks, sentence structure analysis, and key point extraction. The input is the information received in step 1, and the output is the analysis result data.

[0108] Step 3:

[0109] The server corrects the information using automated correction methods based on the analysis results. This process uses "Pandas" and "NumPy" to correct typos and standardize formatting. Furthermore, the overall document format is refined. The input is the analysis result data from step 2, and the output is the corrected document data.

[0110] Step 4:

[0111] The server verifies the corrected information using an evaluation tool. The evaluation criteria focus on document consistency and coherence. Through the evaluation, a response is generated for the user and recorded as feedback. The input is the corrected document data from step 3, and the output is the evaluated feedback data.

[0112] Step 5:

[0113] Ultimately, the server provides the evaluated and corrected information as the final version to the user's terminal via an output mechanism. The input is the evaluation feedback data from step 4, and the output is the completed document received by the user. In this step, prompt text generated by a generative AI model may also be used.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is implemented as a system that provides user-emotion-based feedback by incorporating an emotion engine into the document creation process. This system has a configuration in which the user uploads documents from their own device, and the documents are processed on a server.

[0116] The server first receives the data using a receiving device. Then, it analyzes the content of the data using an analysis device and analyzes the document structure in detail using natural language processing technology. In this process, grammatical errors and logical inconsistencies are identified.

[0117] After analysis, the server uses automated correction mechanisms to automatically make corrections such as standardizing the document's format and font size. These corrections ensure consistency and readability of the document.

[0118] Furthermore, the emotion engine analyzes the user's voice or text input to identify their emotional state. This emotional information is taken into account when generating feedback, which is delivered in a tone appropriate to the user's emotions. This feedback includes suggestions for improvement, merits, and further suggestions for the material.

[0119] For example, if the emotion engine detects stress in a user while they are creating a presentation, the feedback provided will be tailored to alleviate that stress. This allows the user to improve the quality of their presentation while reducing their psychological burden.

[0120] Ultimately, users refine their materials based on feedback, and the completed materials are provided as the final version through the server's output system. This allows users to efficiently create high-quality, consistent materials.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] Users create the target documents using their devices and upload them to the system. The file format can be Word, PDF, or text file.

[0124] Step 2:

[0125] The server receives the data using the receiving means and prepares it for processing by the analysis means. The data is converted into a format that can be analyzed as text data.

[0126] Step 3:

[0127] The server uses analysis tools and natural language processing techniques to analyze the content and structure of the document. It evaluates grammatical errors and content consistency.

[0128] Step 4:

[0129] Based on the analysis results, the server automatically corrects the document using automated correction mechanisms. This includes automatically adjusting formatting, unifying font sizes, and repositioning images and graphs as needed.

[0130] Step 5:

[0131] The user provides voice or text input from their device for the emotion engine to use. The emotion engine then analyzes the user's emotional state based on this input.

[0132] Step 6:

[0133] The server uses evaluation tools to re-evaluate the corrected material. Based on the sentiment information detected by the sentiment engine, it adjusts the tone and content of the feedback.

[0134] Step 7:

[0135] Users receive feedback from the server via their devices and perform final checks and revisions to the materials. Necessary revisions are made based on the feedback.

[0136] Step 8:

[0137] The server uses an output mechanism to output the corrected and verified final version of the document to the terminal. This allows the user to obtain a high-quality document.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In document creation, in addition to analyzing the structure and content of documents, there is a need to facilitate the provision of feedback that takes into account the user's emotional state, thereby supporting the creation of consistent, high-quality documents. In particular, there is a lack of means to provide effective, emotion-based feedback, and improvement is needed in this area.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes receiving means for receiving data, analyzing means for analyzing the received data, emotion recognition means for identifying the user's emotional state, and output means for final outputting the data based on the feedback. This makes it possible to provide feedback that takes the user's emotional state into consideration.

[0143] "Receiving means" refers to a device or function for receiving and temporarily storing data sent by a user on a server.

[0144] "Analysis means" refers to a device or program for analyzing the content of received materials and performing structural analysis of documents or error detection.

[0145] An "automatic correction means" is a device or function that automatically corrects the format and style of a document based on the analysis results in order to improve its quality.

[0146] An "emotion recognition means" is a device or program that analyzes a user's voice or text input to identify their emotional state.

[0147] A "feedback generation means" is a device or function for generating feedback that provides improvement suggestions and information based on analysis results and emotional information.

[0148] "Output means" refers to a device or function for providing the user with the final revised or improved material.

[0149] This invention aims to provide a system that helps users efficiently create high-quality and consistent documents. The system utilizes the following hardware and software.

[0150] First, the user uploads materials using a device. This device is connected to the server via the internet. The materials selected by the user are sent to the server via a dedicated web interface.

[0151] The server receives data via a receiving device and then temporarily stores it in storage. The received data is then analyzed by an analysis device using natural language processing techniques. This analysis utilizes open-source natural language processing libraries to analyze document structure and detect errors.

[0152] Next, the server uses automated correction mechanisms to standardize the document's formatting and font size. This ensures readability and consistency throughout the document. Software used at this stage includes a document formatting adjustment library.

[0153] User input (voice or text) is analyzed by emotion recognition means on the server to identify the emotional state. Emotion recognition uses specialized software for voice analysis and emotion analysis algorithms.

[0154] Based on emotional information, the server uses a generative AI model to generate feedback. This generated feedback is then returned to the user and used to refine and improve the document. An example of a prompt used as input to the generative AI model is, "Please provide feedback that will alleviate the user's anxiety."

[0155] Ultimately, the server provides the user with the final version of the document, which has been proactively and effectively adjusted, through its output mechanism. In this way, it is possible to improve the quality of the document while reducing the psychological burden on the user during the document creation process. For example, if a user experiences stress while creating a project report, the feedback will include a message encouraging relaxation.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user opens the interface on their device, selects a document, and uploads it. The input is the document file selected by the user, which is sent to the server via the device's internet connection. This process performs a basic check to ensure there are no problems with the format or content of the document. The output is the completion of the document's transmission to the server.

[0159] Step 2:

[0160] The server receives data sent from users using a receiving mechanism and stores it in storage. Input is data sent from the terminal, and the server automatically verifies the file format and integrity of the data upon saving. Output is the verified data stored in the database.

[0161] Step 3:

[0162] The server uses analysis tools to analyze received data using natural language processing techniques. The input is stored data, and the analysis includes document structure analysis, keyword extraction, and error detection. Specifically, it uses open-source natural language processing libraries. The output is a dataset containing the analysis results.

[0163] Step 4:

[0164] The server utilizes automated correction mechanisms to standardize the formatting and font size of documents based on the analysis results. The input is a dataset of the analyzed documents, and the document's appearance is refined through data processing. The output is the corrected document data.

[0165] Step 5:

[0166] The server uses emotion recognition means to identify the user's emotional state from their voice or text input. The input is the user's most recent voice or text data, and the emotion is inferred using an emotion analysis algorithm. The output is data of the inferred emotional state.

[0167] Step 6:

[0168] The server uses a generative AI model to execute a feedback generation mechanism, combining analysis results and emotional information to create feedback. The input consists of analyzed data and emotional state data from the document, and the generative AI model generates feedback based on a prompt. For example, the prompt might be "Please provide feedback to alleviate the anxiety the user is feeling." The output is specific feedback for the user.

[0169] Step 7:

[0170] The user receives feedback generated by the server and revises the document based on it. The input is the provided feedback, and the user manually adjusts the document. The output is the final version of the document, incorporating the feedback.

[0171] Step 8:

[0172] The server uses output methods to provide the user with the final version of the document, modified by the user. The final version is provided via download links or email through the server. The input is the completed document data, and the output is the final document viewable by the user.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] During document creation, it is essential to understand the user's emotions appropriately and provide feedback tailored to those emotions in order to reduce their psychological burden and improve the quality of the document. However, conventional systems have not adequately provided feedback that takes user emotions into account, sometimes causing users to experience excessive stress during the creation process. Therefore, there is a need for technology that provides appropriate feedback based on the user's emotions.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes emotion analysis means for detecting emotional states, feedback adjustment means for adjusting feedback based on the emotions identified by the emotion analysis means, and advice generation means for presenting the strengths and weaknesses of the material. This makes it possible to provide feedback that is tailored to the user's emotions.

[0178] "Emotion analysis means" refers to technology that analyzes a user's voice or text input to detect their emotions.

[0179] A "feedback adjustment mechanism" is a technology that has the function of adjusting the content and tone of feedback based on the user's emotions identified by an emotion analysis mechanism.

[0180] An "advice generation method" is a technology for automatically generating advice on the strengths and areas for improvement of a document based on its evaluation.

[0181] "Tone adjustment techniques" are technologies that adjust the style of expression so that the feedback provided reduces psychological burden.

[0182] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses and advice in response to user input.

[0183] A "prompt" is a sentence used as input data for a generative AI model, serving as a guide for the model when generating a response.

[0184] The system for realizing this application primarily consists of programs running on a server. The server receives data from the user's terminal and first analyzes the user's voice and text input using sentiment analysis tools. This sentiment analysis utilizes natural language processing and speech recognition technologies. In this process, speech recognition APIs such as Microsoft's Azure Cognitive Services and Google's Cloud Natural Language API can be used.

[0185] Based on the analyzed emotions, the feedback adjustment mechanism optimizes the feedback provided to the user via a generative AI model. This AI model can utilize open-source GPT (Generative Pre-trained Transformer), among others. Based on the evaluation of the material, the advice generation mechanism creates specific feedback, including the strengths and areas for improvement of the material. This feedback is then adjusted by the tone adjustment mechanism to reduce the user's psychological burden.

[0186] As a concrete example, imagine an elementary school student creating a presentation at home. The user (elementary school student) uses voice input to express their opinions and anxieties regarding the material they are creating. If the emotion analysis system detects stress in the student, the server will provide gentle feedback in a calm tone, such as "Take a deep breath and relax," and also offer specific advice regarding the content of the material, such as "Adding examples to the content will make it easier to understand."

[0187] An example of a prompt for a generative AI model is, "When a child is feeling stressed while creating school presentation materials, please provide advice on how to improve the quality of the materials while also alleviating their psychological burden." In this way, the system implementing the invention can provide helpful feedback that aligns with the user's emotions.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] When creating documents, users use voice input from their devices. The devices send this voice data to a server. This voice data is used as input for analyzing the user's emotions.

[0191] Step 2:

[0192] The server inputs the received audio data into an emotion analysis system. Here, Microsoft's Azure Cognitive Services speech recognition API is used to convert the audio data into text data. Furthermore, the Google Cloud Natural Language API is used to identify the user's emotions from the text. This step outputs data representing the user's emotional state.

[0193] Step 3:

[0194] The server generates user-facing feedback using a feedback adjustment mechanism, based on the emotional state identified by the emotion analysis mechanism. This process uses the open-source generative AI model GPT, and is input with the prompt "Provide stress-reducing advice based on the user's current emotions." The model then outputs specific feedback corresponding to the emotional state.

[0195] Step 4:

[0196] The generated feedback is adjusted in content and style using tone adjustment mechanisms. The generated feedback is given an appropriate tone to reduce the psychological burden on the user. As an output, specific advice can be displayed to the user.

[0197] Step 5:

[0198] The server sends the refined feedback to the user's terminal, providing them with specific advice to help them create their materials. Users can then use this feedback to refine their materials and create high-quality documents.

[0199] 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.

[0200] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0206] 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.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0208] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] 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.

[0210] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The 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.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention is implemented as a system to streamline the document creation process and improve quality. In one embodiment, a user uploads a document from their terminal, and various processes are performed on a server. The server first receives the document from the user using a receiving means. At this stage, the document format can be Word, PDF, or text file, etc.

[0216] Next, the server analyzes the document using analysis tools. This involves using natural language processing techniques to automatically detect grammatical errors and structural inconsistencies within the document, and extracting the main themes and key points of the document.

[0217] Based on the analysis results, the server uses automated correction mechanisms to improve the document. This process includes unifying fonts and paragraph styles, and correcting the placement of images and graphs appropriately, thereby improving the consistency and readability of the document.

[0218] The revised documents are reviewed by the server's evaluation system. This evaluation process assesses the overall quality of the documents and generates user-friendly feedback.

[0219] Finally, the user reviews the document based on the feedback and makes any necessary adjustments. The revised document is then provided as the final version via the server's output system. This ensures that the user receives high-quality documentation.

[0220] For example, when a user creates a business presentation, redundant sections are identified in the input material, and a summary of those sections is suggested by the server. Furthermore, inappropriate formatting and inconsistent fonts in the material are automatically corrected, resulting in a polished presentation document. This reduces the effort involved in creating documents and enables the efficient creation of consistent, high-quality materials.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users upload documents they are creating to the system using their devices. Supported formats include Word, PDF, and text files.

[0224] Step 2:

[0225] The server receives uploaded materials using a receiving mechanism and converts them into a format suitable for content analysis. If necessary, OCR technology is used to recognize PDF documents as text data.

[0226] Step 3:

[0227] The server uses analysis tools to analyze the document. Natural language processing technology is used to analyze the grammatical structure and topic of the document, identifying errors and areas for improvement.

[0228] Step 4:

[0229] The server uses automated correction mechanisms to automatically correct the documents based on the analysis results. Specifically, this includes unifying formats and fonts, and rearranging graphs and figures.

[0230] Step 5:

[0231] The server uses evaluation tools to assess the revised document and generates feedback. This feedback includes an overall quality assessment of the document and suggestions for further revisions.

[0232] Step 6:

[0233] The user receives feedback from the server via their device and makes necessary corrections. The user then performs a final review of the content.

[0234] Step 7:

[0235] The server provides the final version of the document to the terminal via an output device, making high-quality materials available to the user.

[0236] (Example 1)

[0237] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0238] In today's information society, the business and academic fields require the creation of large volumes of documents. However, document creation is time-consuming and labor-intensive, and the tasks of structuring documents, ensuring visual consistency, and organizing content are particularly complex. Furthermore, maintaining document quality while efficiently completing the process is not easy. It is necessary to address these challenges and achieve efficient and high-quality document creation.

[0239] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0240] In this invention, the server includes receiving means for receiving information, analyzing means for analyzing information, and automatic correction means for correcting information based on the analysis results. This automates the document creation process and enables efficient structural analysis of documents, extraction of key points, and ensuring visual consistency.

[0241] "Information" refers to the data and content that make up documents and materials, and includes all forms, such as text, graphs, and images.

[0242] "Receiving means" refers to the functions or devices used by a server to acquire information transmitted from an external source.

[0243] "Analysis means" refers to methods and devices for analyzing received information and evaluating and extracting grammatical errors and the structure of the content.

[0244] "Automatic correction means" refers to functions or devices that improve the formatting and visual consistency of information based on analysis results.

[0245] "Evaluation means" refers to functions or devices for evaluating the quality of corrected information and generating opinions based on the results.

[0246] "Output means" refers to the methods or functions for providing the user with the final, adjusted information.

[0247] "Natural language processing technology" refers to various algorithms and methods that enable computers to understand and analyze human language.

[0248] "Visual consistency" refers to a state where fonts, paragraph styles, and the placement of figures and tables are unified within a document.

[0249] This invention is a system for streamlining information processing and improving the quality of documents. Users can select documents from their own terminals and send them to the server. The terminals can handle document formats such as Word, PDF, and text files.

[0250] The server is equipped with a dedicated receiving mechanism for receiving information, thereby accurately collecting data from external sources. Next, the received information is processed by an analysis mechanism. This process utilizes natural language processing techniques to automatically analyze grammatical errors and the structure of the information. For example, it can extract the main themes and key points from a business report. This analysis uses a generative AI model and a prompt such as, "Please extract the key points from this text."

[0251] Based on the analysis results, the server uses automated correction mechanisms to improve the information. Specifically, formatting and fonts within the document are corrected, and the positions of images and graphs are adjusted. This process improves the visual consistency and readability of the information. For example, in presentation materials, different fonts are unified and images are placed appropriately, making the information easier to understand and organize.

[0252] After revisions are complete, the document is reviewed by the server's evaluation system. This evaluation process checks the overall quality of the document and provides clear feedback to the user. The user then revises the document based on this feedback and makes any necessary adjustments. Finally, the server's output system generates the final version of the document and provides it to the user. This system allows users to efficiently create high-quality, well-organized documents.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user selects a document from their device and uploads it to the server. The input is a Word, PDF, or text file located on the user's device, and the server receives that file as output. Specifically, the user selects a document file and submits it to the server using a dedicated upload form.

[0256] Step 2:

[0257] The server receives information sent by the user using a receiving device. The input is the file itself sent by the user, and the output is the storage of that information within the server. At this stage, the server checks the file format and determines whether it is a valid file.

[0258] Step 3:

[0259] The server analyzes the received information using analysis tools. The input is the received file, and the output is the text data resulting from the analysis. Specifically, it uses natural language processing techniques to detect grammatical errors in the document and extract the main themes and key points. A generative AI model is used here, and prompts such as "Please extract the important points of this text" are used.

[0260] Step 4:

[0261] The server uses automated correction mechanisms based on the analysis results to improve the information. The input is the analyzed text data, and the output is a corrected formatted document. Specific actions include unifying fonts, unifying paragraph styles, and properly positioning images and graphs.

[0262] Step 5:

[0263] The server evaluates the corrected document using evaluation tools. The input is the corrected document, and the output is feedback to be provided to the user. Here, the overall quality of the document is checked, evaluated in terms of readability and consistency, and feedback that is easy for the user to understand is generated.

[0264] Step 6:

[0265] The user reviews feedback from the server and makes a final check of the document. The input is the feedback message provided by the server, and the output is the final document with the corrections made. The user adjusts the document based on the feedback and corrects the content if necessary.

[0266] Step 7:

[0267] The server outputs the user-edited document as the final version. The input is the user-edited document, and the output is the final version of the document provided to the user. At this stage, the server exports the document in the specified format and prepares it for the user to download.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] Generating high-quality and consistent documents automatically is difficult in information creation. Furthermore, manual document compilation by users is time-consuming, labor-intensive, and increases the risk of errors. In particular, efficient and highly accurate automation methods are needed to organize complex information clearly and quickly create user guides in areas such as electronic payment services.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes a receiving means for acquiring information, an analysis means for analyzing the acquired information, and an automatic correction means for correcting the information based on the analysis results. This enables high-quality and consistent information distribution through the automatic generation of documents.

[0273] "Information" refers to data and knowledge expressed in a tangible form, which are processed for the purpose of understanding and utilizing their content.

[0274] A "receiving means" is a device or element that has the function of collecting information sent from an external source and plays the role of incorporating this information into the system.

[0275] "Analysis means" refers to techniques and devices used to examine acquired information in detail and identify its structure and content.

[0276] "Automatic correction methods" refer to technologies and means for automatically adjusting documents and data to correct errors in information based on analysis results and to ensure consistency.

[0277] "Evaluation means" refers to devices or elements that have the function of determining the quality and validity of corrected information and generating responses to appropriately convey that information to users.

[0278] "Output means" refers to devices or elements that have the function of providing processed and evaluated information to the user in its final form.

[0279] In a mode for carrying out the invention, the system that realizes this application example is mainly composed of a program that runs on a server. The server receives information, analyzes it, and automatically makes corrections.

[0280] The server first receives information from the user. At this stage, the information can take the form of a text file or a digital document. The received information is analyzed using natural language processing software such as "spaCy" and "transformers" for text analysis. This analysis includes checking the grammar structure and extracting key points.

[0281] Next, based on the analysis results, the information is corrected using an automatic correction function. In this process, corrections for typos and omissions, conversion to a unified format, and even reorganization of the information are carried out. In this case, it is common to use data processing libraries such as "Pandas" and "NumPy".

[0282] Finally, the corrected information is evaluated, and a final response is generated. In the evaluation process, the consistency and coherence of the information are confirmed, and feedback is provided to the user in an easy-to-understand form. This enables the automatic generation of documents such as user guides and FAQs for electronic payment services.

[0283] As a specific example, when a user tries to create a guide about a new cashback function, the server analyzes the information about that function and generates a text in a form that is easy for the user to understand. In this process, a generative AI model may also be used. Examples of prompt texts include "Please carefully analyze the following text, correct the errors, and then convert it into an effective user guide."

[0284] The flow of specific processing in Application Example 1 will be described using Figure 12.

[0285] Step 1:

[0286] The user uses their terminal to send information to the server. The format of the information is a text file or a digital document. The server accumulates this information through receiving means. The input is the information from the user, and the output is the original data stored in the server.

[0287] Step 2:

[0288] The server analyzes the received information by means of analysis tools. Natural language processing software such as "spaCy" and "transformers" is used for this analysis. Specifically, grammar checking, sentence structure analysis, and key point extraction are performed. The input is the information received in Step 1, and the output is the analysis result data.

[0289] Step 3:

[0290] Based on the analysis results, the server modifies the information using the automatic correction means. In this process, "Pandas" and "NumPy" are used to correct typos and unify the format. Furthermore, the format of the entire document is adjusted. The input is the analysis result data of Step 2, and the output is the corrected document data.

[0291] Step 4:

[0292] The server checks the corrected information with the evaluation means. The perspectives are the consistency and coherence of the document. Through the evaluation, a response for providing to the user is generated and recorded as feedback. The input is the corrected document data of Step 3, and the output is the evaluated feedback data.

[0293] Step 5:

[0294] Finally, the server provides the evaluated and corrected information to the user's terminal as the final version by means of the output means. The input is the evaluation feedback data of Step 4, and the output is the completed document received by the user. In this step, prompt texts by the generative AI model may also be used.

[0295] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0296] This invention is implemented as a system that provides user-emotion-based feedback by incorporating an emotion engine into the document creation process. This system has a configuration in which the user uploads documents from their own device, and the documents are processed on a server.

[0297] The server first receives the data using a receiving device. Then, it analyzes the content of the data using an analysis device and analyzes the document structure in detail using natural language processing technology. In this process, grammatical errors and logical inconsistencies are identified.

[0298] After analysis, the server uses automated correction mechanisms to automatically make corrections such as standardizing the document's format and font size. These corrections ensure consistency and readability of the document.

[0299] Furthermore, the emotion engine analyzes the user's voice or text input to identify their emotional state. This emotional information is taken into account when generating feedback, which is delivered in a tone appropriate to the user's emotions. This feedback includes suggestions for improvement, merits, and further suggestions for the material.

[0300] For example, if the emotion engine detects stress in a user while they are creating a presentation, the feedback provided will be tailored to alleviate that stress. This allows the user to improve the quality of their presentation while reducing their psychological burden.

[0301] Ultimately, users refine their materials based on feedback, and the completed materials are provided as the final version through the server's output system. This allows users to efficiently create high-quality, consistent materials.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The user uses the terminal to create the target document and upload it to the system. The file format is Word, PDF, or a text file.

[0305] Step 2:

[0306] The server uses the receiving means to receive the document and prepares it for processing by the analysis means. The document is converted into a state where it can be analyzed as text data.

[0307] Step 3:

[0308] The server uses the analysis means to analyze the content and structure of the document using natural language processing technology. It evaluates grammatical errors and content consistency.

[0309] Step 4:

[0310] Based on the analysis results, the server corrects the document using the automatic correction means. It automatically adjusts the formatting, unifies the font size, and corrects the placement of images and graphs if necessary.

[0311] Step 5:

[0312] The user provides an input in voice or text for the emotion engine to use from the terminal. Based on this, the emotion engine analyzes the user's emotional state.

[0313] Step 6:

[0314] The server uses the evaluation means to re-evaluate the corrected document. Based on the emotion information detected by the emotion engine, it adjusts the tone and content of the feedback.

[0315] Step 7:

[0316] The user receives the feedback provided by the server through the terminal and performs the final confirmation and correction of the document. Revisions are made as necessary based on the feedback.

[0317] Step 8:

[0318] The server uses an output mechanism to output the corrected and verified final version of the document to the terminal. This allows the user to obtain a high-quality document.

[0319] (Example 2)

[0320] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0321] In document creation, in addition to analyzing the structure and content of documents, there is a need to facilitate the provision of feedback that takes into account the user's emotional state, thereby supporting the creation of consistent, high-quality documents. In particular, there is a lack of means to provide effective, emotion-based feedback, and improvement is needed in this area.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes receiving means for receiving data, analyzing means for analyzing the received data, emotion recognition means for identifying the user's emotional state, and output means for final outputting the data based on the feedback. This makes it possible to provide feedback that takes the user's emotional state into consideration.

[0324] "Receiving means" refers to a device or function for receiving and temporarily storing data sent by a user on a server.

[0325] "Analysis means" refers to a device or program for analyzing the content of received materials and performing structural analysis of documents or error detection.

[0326] An "automatic correction means" is a device or function that automatically corrects the format and style of a document based on the analysis results in order to improve its quality.

[0327] An "emotion recognition means" is a device or program that analyzes a user's voice or text input to identify their emotional state.

[0328] A "feedback generation means" is a device or function for generating feedback that provides improvement suggestions and information based on analysis results and emotional information.

[0329] "Output means" refers to a device or function for providing the user with the final revised or improved material.

[0330] This invention aims to provide a system that helps users efficiently create high-quality and consistent documents. The system utilizes the following hardware and software.

[0331] First, the user uploads materials using a device. This device is connected to the server via the internet. The materials selected by the user are sent to the server via a dedicated web interface.

[0332] The server receives data via a receiving device and then temporarily stores it in storage. The received data is then analyzed by an analysis device using natural language processing techniques. This analysis utilizes open-source natural language processing libraries to analyze document structure and detect errors.

[0333] Next, the server uses automated correction mechanisms to standardize the document's formatting and font size. This ensures readability and consistency throughout the document. Software used at this stage includes a document formatting adjustment library.

[0334] User input (voice or text) is analyzed by emotion recognition means on the server to identify the emotional state. Emotion recognition uses specialized software for voice analysis and emotion analysis algorithms.

[0335] Based on emotional information, the server uses a generative AI model to generate feedback. This generated feedback is then returned to the user and used to refine and improve the document. An example of a prompt used as input to the generative AI model is, "Please provide feedback that will alleviate the user's anxiety."

[0336] Ultimately, the server provides the user with the final version of the document, which has been proactively and effectively adjusted, through its output mechanism. In this way, it is possible to improve the quality of the document while reducing the psychological burden on the user during the document creation process. For example, if a user experiences stress while creating a project report, the feedback will include a message encouraging relaxation.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The user opens the interface on their device, selects a document, and uploads it. The input is the document file selected by the user, which is sent to the server via the device's internet connection. This process performs a basic check to ensure there are no problems with the format or content of the document. The output is the completion of the document's transmission to the server.

[0340] Step 2:

[0341] The server receives data sent from users using a receiving mechanism and stores it in storage. Input is data sent from the terminal, and the server automatically verifies the file format and integrity of the data upon saving. Output is the verified data stored in the database.

[0342] Step 3:

[0343] The server uses analysis tools to analyze received data using natural language processing techniques. The input is stored data, and the analysis includes document structure analysis, keyword extraction, and error detection. Specifically, it uses open-source natural language processing libraries. The output is a dataset containing the analysis results.

[0344] Step 4:

[0345] The server utilizes automated correction mechanisms to standardize the formatting and font size of documents based on the analysis results. The input is a dataset of the analyzed documents, and the document's appearance is refined through data processing. The output is the corrected document data.

[0346] Step 5:

[0347] The server uses emotion recognition means to identify the user's emotional state from their voice or text input. The input is the user's most recent voice or text data, and the emotion is inferred using an emotion analysis algorithm. The output is data of the inferred emotional state.

[0348] Step 6:

[0349] The server uses a generative AI model to execute a feedback generation mechanism, combining analysis results and emotional information to create feedback. The input consists of analyzed data and emotional state data from the document, and the generative AI model generates feedback based on a prompt. For example, the prompt might be "Please provide feedback to alleviate the anxiety the user is feeling." The output is specific feedback for the user.

[0350] Step 7:

[0351] The user receives feedback generated by the server and revises the document based on it. The input is the provided feedback, and the user manually adjusts the document. The output is the final version of the document, incorporating the feedback.

[0352] Step 8:

[0353] The server uses output methods to provide the user with the final version of the document, modified by the user. The final version is provided via download links or email through the server. The input is the completed document data, and the output is the final document viewable by the user.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0356] During document creation, it is essential to understand the user's emotions appropriately and provide feedback tailored to those emotions in order to reduce their psychological burden and improve the quality of the document. However, conventional systems have not adequately provided feedback that takes user emotions into account, sometimes causing users to experience excessive stress during the creation process. Therefore, there is a need for technology that provides appropriate feedback based on the user's emotions.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes emotion analysis means for detecting emotional states, feedback adjustment means for adjusting feedback based on the emotions identified by the emotion analysis means, and advice generation means for presenting the strengths and weaknesses of the material. This makes it possible to provide feedback that is tailored to the user's emotions.

[0359] "Emotion analysis means" refers to technology that analyzes a user's voice or text input to detect their emotions.

[0360] A "feedback adjustment mechanism" is a technology that has the function of adjusting the content and tone of feedback based on the user's emotions identified by an emotion analysis mechanism.

[0361] An "advice generation method" is a technology for automatically generating advice on the strengths and areas for improvement of a document based on its evaluation.

[0362] "Tone adjustment techniques" are technologies that adjust the style of expression so that the feedback provided reduces psychological burden.

[0363] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses and advice in response to user input.

[0364] A "prompt" is a sentence used as input data for a generative AI model, serving as a guide for the model when generating a response.

[0365] The system for realizing this application primarily consists of programs running on a server. The server receives data from the user's terminal and first analyzes the user's voice and text input using sentiment analysis techniques. This sentiment analysis utilizes natural language processing and speech recognition technologies. Speech recognition APIs such as Microsoft's Azure Cognitive Services or Google Cloud Natural Language API can be used for this purpose.

[0366] Based on the analyzed emotions, the feedback adjustment mechanism optimizes the feedback provided to the user via a generative AI model. This AI model can utilize open-source GPT (Generative Pre-trained Transformer), among others. Based on the evaluation of the material, the advice generation mechanism creates specific feedback, including the strengths and areas for improvement of the material. This feedback is then adjusted by the tone adjustment mechanism to reduce the user's psychological burden.

[0367] As a concrete example, imagine an elementary school student creating a presentation at home. The user (elementary school student) uses voice input to express their opinions and anxieties regarding the material they are creating. If the emotion analysis system detects stress in the student, the server will provide gentle feedback in a calm tone, such as "Take a deep breath and relax," and also offer specific advice regarding the content of the material, such as "Adding examples to the content will make it easier to understand."

[0368] An example of a prompt for a generative AI model is, "When a child is feeling stressed while creating school presentation materials, please provide advice on how to improve the quality of the materials while also alleviating their psychological burden." In this way, the system implementing the invention can provide helpful feedback that aligns with the user's emotions.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] When creating documents, users use voice input from their devices. The devices send this voice data to a server. This voice data is used as input for analyzing the user's emotions.

[0372] Step 2:

[0373] The server inputs the received audio data into an emotion analysis system. Here, Microsoft's Azure Cognitive Services speech recognition API is used to convert the audio data into text data. Furthermore, the Google Cloud Natural Language API is used to identify the user's emotions from the text. This step outputs data representing the user's emotional state.

[0374] Step 3:

[0375] The server generates user-facing feedback using a feedback adjustment mechanism, based on the emotional state identified by the emotion analysis mechanism. This process uses the open-source generative AI model GPT, and is input with the prompt "Provide stress-reducing advice based on the user's current emotions." The model then outputs specific feedback corresponding to the emotional state.

[0376] Step 4:

[0377] The generated feedback is adjusted in content and style using tone adjustment mechanisms. The generated feedback is given an appropriate tone to reduce the psychological burden on the user. As an output, specific advice can be displayed to the user.

[0378] Step 5:

[0379] The server sends the refined feedback to the user's terminal, providing them with specific advice to help them create their materials. Users can then use this feedback to refine their materials and create high-quality documents.

[0380] 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.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0387] 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.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0389] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] 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.

[0391] 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.

[0392] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The 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.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] This invention is implemented as a system to streamline the document creation process and improve quality. In one embodiment, a user uploads a document from their terminal, and various processes are performed on a server. The server first receives the document from the user using a receiving means. At this stage, the document format can be Word, PDF, or text file, etc.

[0397] Next, the server analyzes the document using analysis tools. This involves using natural language processing techniques to automatically detect grammatical errors and structural inconsistencies within the document, and extracting the main themes and key points of the document.

[0398] Based on the analysis results, the server uses automated correction mechanisms to improve the document. This process includes unifying fonts and paragraph styles, and correcting the placement of images and graphs appropriately, thereby improving the consistency and readability of the document.

[0399] The revised documents are reviewed by the server's evaluation system. This evaluation process assesses the overall quality of the documents and generates user-friendly feedback.

[0400] Finally, the user reviews the document based on the feedback and makes any necessary adjustments. The revised document is then provided as the final version via the server's output system. This ensures that the user receives high-quality documentation.

[0401] For example, when a user creates a business presentation, redundant sections are identified in the input material, and a summary of those sections is suggested by the server. Furthermore, inappropriate formatting and inconsistent fonts in the material are automatically corrected, resulting in a polished presentation document. This reduces the effort involved in creating documents and enables the efficient creation of consistent, high-quality materials.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] Users upload documents they are creating to the system using their devices. Supported formats include Word, PDF, and text files.

[0405] Step 2:

[0406] The server receives uploaded materials using a receiving mechanism and converts them into a format suitable for content analysis. If necessary, OCR technology is used to recognize PDF documents as text data.

[0407] Step 3:

[0408] The server uses analysis tools to analyze the document. Natural language processing technology is used to analyze the grammatical structure and topic of the document, identifying errors and areas for improvement.

[0409] Step 4:

[0410] The server uses automated correction mechanisms to automatically correct the documents based on the analysis results. Specifically, this includes unifying formats and fonts, and rearranging graphs and figures.

[0411] Step 5:

[0412] The server uses evaluation tools to assess the revised document and generates feedback. This feedback includes an overall quality assessment of the document and suggestions for further revisions.

[0413] Step 6:

[0414] The user receives feedback from the server via their device and makes necessary corrections. The user then performs a final review of the content.

[0415] Step 7:

[0416] The server provides the final version of the document to the terminal via an output device, making high-quality materials available to the user.

[0417] (Example 1)

[0418] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0419] In today's information society, the business and academic fields require the creation of large volumes of documents. However, document creation is time-consuming and labor-intensive, and the tasks of structuring documents, ensuring visual consistency, and organizing content are particularly complex. Furthermore, maintaining document quality while efficiently completing the process is not easy. It is necessary to address these challenges and achieve efficient and high-quality document creation.

[0420] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0421] In this invention, the server includes receiving means for receiving information, analyzing means for analyzing information, and automatic correction means for correcting information based on the analysis results. This automates the document creation process and enables efficient structural analysis of documents, extraction of key points, and ensuring visual consistency.

[0422] "Information" refers to the data and content that make up documents and materials, and includes all forms, such as text, graphs, and images.

[0423] "Receiving means" refers to the functions or devices used by a server to acquire information transmitted from an external source.

[0424] "Analysis means" refers to methods and devices for analyzing received information and evaluating and extracting grammatical errors and the structure of the content.

[0425] "Automatic correction means" refers to functions or devices that improve the formatting and visual consistency of information based on analysis results.

[0426] "Evaluation means" refers to functions or devices for evaluating the quality of corrected information and generating opinions based on the results.

[0427] "Output means" refers to the methods or functions for providing the user with the final, adjusted information.

[0428] "Natural language processing technology" refers to various algorithms and methods that enable computers to understand and analyze human language.

[0429] "Visual consistency" refers to a state where fonts, paragraph styles, and the placement of figures and tables are unified within a document.

[0430] This invention is a system for streamlining information processing and improving the quality of documents. Users can select documents from their own terminals and send them to the server. The terminals can handle document formats such as Word, PDF, and text files.

[0431] The server is equipped with a dedicated receiving mechanism for receiving information, thereby accurately collecting data from external sources. Next, the received information is processed by an analysis mechanism. This process utilizes natural language processing techniques to automatically analyze grammatical errors and the structure of the information. For example, it can extract the main themes and key points from a business report. This analysis uses a generative AI model and a prompt such as, "Please extract the key points from this text."

[0432] Based on the analysis results, the server uses automated correction mechanisms to improve the information. Specifically, formatting and fonts within the document are corrected, and the positions of images and graphs are adjusted. This process improves the visual consistency and readability of the information. For example, in presentation materials, different fonts are unified and images are placed appropriately, making the information easier to understand and organize.

[0433] After revisions are complete, the document is reviewed by the server's evaluation system. This evaluation process checks the overall quality of the document and provides clear feedback to the user. The user then revises the document based on this feedback and makes any necessary adjustments. Finally, the server's output system generates the final version of the document and provides it to the user. This system allows users to efficiently create high-quality, well-organized documents.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The user selects a document from their device and uploads it to the server. The input is a Word, PDF, or text file located on the user's device, and the server receives that file as output. Specifically, the user selects a document file and submits it to the server using a dedicated upload form.

[0437] Step 2:

[0438] The server receives information sent by the user using a receiving device. The input is the file itself sent by the user, and the output is the storage of that information within the server. At this stage, the server checks the file format and determines whether it is a valid file.

[0439] Step 3:

[0440] The server analyzes the received information using analysis tools. The input is the received file, and the output is the text data resulting from the analysis. Specifically, it uses natural language processing techniques to detect grammatical errors in the document and extract the main themes and key points. A generative AI model is used here, and prompts such as "Please extract the important points of this text" are used.

[0441] Step 4:

[0442] The server uses automated correction mechanisms based on the analysis results to improve the information. The input is the analyzed text data, and the output is a corrected formatted document. Specific actions include unifying fonts, unifying paragraph styles, and properly positioning images and graphs.

[0443] Step 5:

[0444] The server evaluates the corrected document using evaluation tools. The input is the corrected document, and the output is feedback to be provided to the user. Here, the overall quality of the document is checked, evaluated in terms of readability and consistency, and feedback that is easy for the user to understand is generated.

[0445] Step 6:

[0446] The user reviews feedback from the server and makes a final check of the document. The input is the feedback message provided by the server, and the output is the final document with the corrections made. The user adjusts the document based on the feedback and corrects the content if necessary.

[0447] Step 7:

[0448] The server outputs the user-edited document as the final version. The input is the user-edited document, and the output is the final version of the document provided to the user. At this stage, the server exports the document in the specified format and prepares it for the user to download.

[0449] (Application Example 1)

[0450] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] Generating high-quality and consistent documents automatically is difficult in information creation. Furthermore, manual document compilation by users is time-consuming, labor-intensive, and increases the risk of errors. In particular, efficient and highly accurate automation methods are needed to organize complex information clearly and quickly create user guides in areas such as electronic payment services.

[0452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0453] In this invention, the server includes a receiving means for acquiring information, an analysis means for analyzing the acquired information, and an automatic correction means for correcting the information based on the analysis results. This enables high-quality and consistent information distribution through the automatic generation of documents.

[0454] "Information" refers to data and knowledge expressed in a tangible form, which are processed for the purpose of understanding and utilizing their content.

[0455] A "receiving means" is a device or element that has the function of collecting information sent from an external source and plays the role of incorporating this information into the system.

[0456] "Analysis means" refers to techniques and devices used to examine acquired information in detail and identify its structure and content.

[0457] "Automatic correction methods" refer to technologies and means for automatically adjusting documents and data to correct errors in information based on analysis results and to ensure consistency.

[0458] "Evaluation means" refers to devices or elements that have the function of determining the quality and validity of corrected information and generating responses to appropriately convey that information to users.

[0459] "Output means" refers to devices or elements that have the function of providing processed and evaluated information to the user in its final form.

[0460] In a mode for carrying out the invention, the system that realizes this application example is mainly composed of a program that runs on a server. The server receives information, analyzes it, and automatically makes corrections.

[0461] The server first receives information from the user. At this stage, the information can take the form of text files or digital documents. The received information is then analyzed using natural language processing software such as "spaCy" or "transformers" for text analysis. This analysis includes checking grammatical structure and extracting key points.

[0462] Next, based on the analysis results, the information is corrected using an automated correction function. This process involves correcting typographical errors, converting to a unified format, and restructuring the information. It is common to use data processing libraries such as "Pandas" or "NumPy" for this stage.

[0463] At the end of the process, the corrected information is evaluated, and a final response is generated. The evaluation process verifies the integrity and consistency of the information and provides feedback to the user in an easy-to-understand format. This enables the automatic generation of documents such as user guides and FAQs for electronic payment services.

[0464] For example, if a user is trying to create a guide about a new cashback feature, the server will analyze information about that feature and generate text in a way that is easy for users to understand. A generative AI model may be used in this process. An example of a prompt might be, "Carefully analyze the following text, correct any errors, and transform it into an effective user guide."

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] Users send information to the server using their own devices. The information is in the form of text files or digital documents. The server collects this information through receiving mechanisms. The input is information from the user, and the output is the original data stored on the server.

[0468] Step 2:

[0469] The server analyzes the received information using analysis tools. This analysis uses natural language processing software such as "spaCy" and "transformers." Specifically, it performs grammar checks, sentence structure analysis, and key point extraction. The input is the information received in step 1, and the output is the analysis result data.

[0470] Step 3:

[0471] The server corrects the information using automated correction methods based on the analysis results. This process uses "Pandas" and "NumPy" to correct typos and standardize formatting. Furthermore, the overall document format is refined. The input is the analysis result data from step 2, and the output is the corrected document data.

[0472] Step 4:

[0473] The server verifies the corrected information using an evaluation tool. The evaluation criteria focus on document consistency and coherence. Through the evaluation, a response is generated for the user and recorded as feedback. The input is the corrected document data from step 3, and the output is the evaluated feedback data.

[0474] Step 5:

[0475] Ultimately, the server provides the evaluated and corrected information as the final version to the user's terminal via an output mechanism. The input is the evaluation feedback data from step 4, and the output is the completed document received by the user. In this step, prompt text generated by a generative AI model may also be used.

[0476] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0477] This invention is implemented as a system that provides user-emotion-based feedback by incorporating an emotion engine into the document creation process. This system has a configuration in which the user uploads documents from their own device, and the documents are processed on a server.

[0478] The server first receives the data using a receiving device. Then, it analyzes the content of the data using an analysis device and analyzes the document structure in detail using natural language processing technology. In this process, grammatical errors and logical inconsistencies are identified.

[0479] After analysis, the server uses automated correction mechanisms to automatically make corrections such as standardizing the document's format and font size. These corrections ensure consistency and readability of the document.

[0480] Furthermore, the emotion engine analyzes the user's voice or text input to identify their emotional state. This emotional information is taken into account when generating feedback, which is delivered in a tone appropriate to the user's emotions. This feedback includes suggestions for improvement, merits, and further suggestions for the material.

[0481] For example, if the emotion engine detects stress in a user while they are creating a presentation, the feedback provided will be tailored to alleviate that stress. This allows the user to improve the quality of their presentation while reducing their psychological burden.

[0482] Ultimately, users refine their materials based on feedback, and the completed materials are provided as the final version through the server's output system. This allows users to efficiently create high-quality, consistent materials.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] Users create the target documents using their devices and upload them to the system. The file format can be Word, PDF, or text file.

[0486] Step 2:

[0487] The server receives the data using the receiving means and prepares it for processing by the analysis means. The data is converted into a format that can be analyzed as text data.

[0488] Step 3:

[0489] The server uses analysis tools and natural language processing techniques to analyze the content and structure of the document. It evaluates grammatical errors and content consistency.

[0490] Step 4:

[0491] Based on the analysis results, the server automatically corrects the document using automated correction mechanisms. This includes automatically adjusting formatting, unifying font sizes, and repositioning images and graphs as needed.

[0492] Step 5:

[0493] The user provides voice or text input from their device for the emotion engine to use. The emotion engine then analyzes the user's emotional state based on this input.

[0494] Step 6:

[0495] The server uses evaluation tools to re-evaluate the corrected material. Based on the sentiment information detected by the sentiment engine, it adjusts the tone and content of the feedback.

[0496] Step 7:

[0497] Users receive feedback from the server via their devices and perform final checks and revisions to the materials. Necessary revisions are made based on the feedback.

[0498] Step 8:

[0499] The server uses an output mechanism to output the corrected and verified final version of the document to the terminal. This allows the user to obtain a high-quality document.

[0500] (Example 2)

[0501] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0502] In document creation, in addition to analyzing the structure and content of documents, there is a need to facilitate the provision of feedback that takes into account the user's emotional state, thereby supporting the creation of consistent, high-quality documents. In particular, there is a lack of means to provide effective, emotion-based feedback, and improvement is needed in this area.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes receiving means for receiving data, analyzing means for analyzing the received data, emotion recognition means for identifying the user's emotional state, and output means for final outputting the data based on the feedback. This makes it possible to provide feedback that takes the user's emotional state into consideration.

[0505] "Receiving means" refers to a device or function for receiving and temporarily storing data sent by a user on a server.

[0506] "Analysis means" refers to a device or program for analyzing the content of received materials and performing structural analysis of documents or error detection.

[0507] An "automatic correction means" is a device or function that automatically corrects the format and style of a document based on the analysis results in order to improve its quality.

[0508] An "emotion recognition means" is a device or program that analyzes a user's voice or text input to identify their emotional state.

[0509] A "feedback generation means" is a device or function for generating feedback that provides improvement suggestions and information based on analysis results and emotional information.

[0510] "Output means" refers to a device or function for providing the user with the final revised or improved material.

[0511] This invention aims to provide a system that helps users efficiently create high-quality and consistent documents. The system utilizes the following hardware and software.

[0512] First, the user uploads materials using a device. This device is connected to the server via the internet. The materials selected by the user are sent to the server via a dedicated web interface.

[0513] The server receives data via a receiving device and then temporarily stores it in storage. The received data is then analyzed by an analysis device using natural language processing techniques. This analysis utilizes open-source natural language processing libraries to analyze document structure and detect errors.

[0514] Next, the server uses automated correction mechanisms to standardize the document's formatting and font size. This ensures readability and consistency throughout the document. Software used at this stage includes a document formatting adjustment library.

[0515] User input (voice or text) is analyzed by emotion recognition means on the server to identify the emotional state. Emotion recognition uses specialized software for voice analysis and emotion analysis algorithms.

[0516] Based on emotional information, the server uses a generative AI model to generate feedback. This generated feedback is then returned to the user and used to refine and improve the document. An example of a prompt used as input to the generative AI model is, "Please provide feedback that will alleviate the user's anxiety."

[0517] Ultimately, the server provides the user with the final version of the document, which has been proactively and effectively adjusted, through its output mechanism. In this way, it is possible to improve the quality of the document while reducing the psychological burden on the user during the document creation process. For example, if a user experiences stress while creating a project report, the feedback will include a message encouraging relaxation.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The user opens the interface on their device, selects a document, and uploads it. The input is the document file selected by the user, which is sent to the server via the device's internet connection. This process performs a basic check to ensure there are no problems with the format or content of the document. The output is the completion of the document's transmission to the server.

[0521] Step 2:

[0522] The server receives data sent from users using a receiving mechanism and stores it in storage. Input is data sent from the terminal, and the server automatically verifies the file format and integrity of the data upon saving. Output is the verified data stored in the database.

[0523] Step 3:

[0524] The server uses analysis tools to analyze received data using natural language processing techniques. The input is stored data, and the analysis includes document structure analysis, keyword extraction, and error detection. Specifically, it uses open-source natural language processing libraries. The output is a dataset containing the analysis results.

[0525] Step 4:

[0526] The server utilizes automated correction mechanisms to standardize the formatting and font size of documents based on the analysis results. The input is a dataset of the analyzed documents, and the document's appearance is refined through data processing. The output is the corrected document data.

[0527] Step 5:

[0528] The server uses emotion recognition means to identify the user's emotional state from their voice or text input. The input is the user's most recent voice or text data, and the emotion is inferred using an emotion analysis algorithm. The output is data of the inferred emotional state.

[0529] Step 6:

[0530] The server uses a generative AI model to execute a feedback generation mechanism, combining analysis results and emotional information to create feedback. The input consists of analyzed data and emotional state data from the document, and the generative AI model generates feedback based on a prompt. For example, the prompt might be "Please provide feedback to alleviate the anxiety the user is feeling." The output is specific feedback for the user.

[0531] Step 7:

[0532] The user receives feedback generated by the server and revises the document based on it. The input is the provided feedback, and the user manually adjusts the document. The output is the final version of the document, incorporating the feedback.

[0533] Step 8:

[0534] The server uses output methods to provide the user with the final version of the document, modified by the user. The final version is provided via download links or email through the server. The input is the completed document data, and the output is the final document viewable by the user.

[0535] (Application Example 2)

[0536] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] During document creation, it is essential to understand the user's emotions appropriately and provide feedback tailored to those emotions in order to reduce their psychological burden and improve the quality of the document. However, conventional systems have not adequately provided feedback that takes user emotions into account, sometimes causing users to experience excessive stress during the creation process. Therefore, there is a need for technology that provides appropriate feedback based on the user's emotions.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0539] In this invention, the server includes emotion analysis means for detecting emotional states, feedback adjustment means for adjusting feedback based on the emotions identified by the emotion analysis means, and advice generation means for presenting the strengths and weaknesses of the material. This makes it possible to provide feedback that is tailored to the user's emotions.

[0540] "Emotion analysis means" refers to technology that analyzes a user's voice or text input to detect their emotions.

[0541] A "feedback adjustment mechanism" is a technology that has the function of adjusting the content and tone of feedback based on the user's emotions identified by an emotion analysis mechanism.

[0542] An "advice generation method" is a technology for automatically generating advice on the strengths and areas for improvement of a document based on its evaluation.

[0543] "Tone adjustment techniques" are technologies that adjust the style of expression so that the feedback provided reduces psychological burden.

[0544] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses and advice in response to user input.

[0545] A "prompt" is a sentence used as input data for a generative AI model, serving as a guide for the model when generating a response.

[0546] The system for realizing this application primarily consists of programs running on a server. The server receives data from the user's terminal and first analyzes the user's voice and text input using sentiment analysis techniques. This sentiment analysis utilizes natural language processing and speech recognition technologies. Speech recognition APIs such as Microsoft's Azure Cognitive Services or Google Cloud Natural Language API can be used for this purpose.

[0547] Based on the analyzed emotions, the feedback adjustment mechanism optimizes the feedback provided to the user via a generative AI model. This AI model can utilize open-source GPT (Generative Pre-trained Transformer), among others. Based on the evaluation of the material, the advice generation mechanism creates specific feedback, including the strengths and areas for improvement of the material. This feedback is then adjusted by the tone adjustment mechanism to reduce the user's psychological burden.

[0548] As a concrete example, imagine an elementary school student creating a presentation at home. The user (elementary school student) uses voice input to express their opinions and anxieties regarding the material they are creating. If the emotion analysis system detects stress in the student, the server will provide gentle feedback in a calm tone, such as "Take a deep breath and relax," and also offer specific advice regarding the content of the material, such as "Adding examples to the content will make it easier to understand."

[0549] An example of a prompt for a generative AI model is, "When a child is feeling stressed while creating school presentation materials, please provide advice on how to improve the quality of the materials while also alleviating their psychological burden." In this way, the system implementing the invention can provide helpful feedback that aligns with the user's emotions.

[0550] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0551] Step 1:

[0552] When creating documents, users use voice input from their devices. The devices send this voice data to a server. This voice data is used as input for analyzing the user's emotions.

[0553] Step 2:

[0554] The server inputs the received audio data into an emotion analysis system. Here, Microsoft's Azure Cognitive Services speech recognition API is used to convert the audio data into text data. Furthermore, the Google Cloud Natural Language API is used to identify the user's emotions from the text. This step outputs data representing the user's emotional state.

[0555] Step 3:

[0556] The server generates user-facing feedback using a feedback adjustment mechanism, based on the emotional state identified by the emotion analysis mechanism. This process uses the open-source generative AI model GPT, and is input with the prompt "Provide stress-reducing advice based on the user's current emotions." The model then outputs specific feedback corresponding to the emotional state.

[0557] Step 4:

[0558] The generated feedback is adjusted in content and style using tone adjustment mechanisms. The generated feedback is given an appropriate tone to reduce the psychological burden on the user. As an output, specific advice can be displayed to the user.

[0559] Step 5:

[0560] The server sends the refined feedback to the user's terminal, providing them with specific advice to help them create their materials. Users can then use this feedback to refine their materials and create high-quality documents.

[0561] 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.

[0562] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0564] [Fourth Embodiment]

[0565] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0566] 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.

[0567] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0568] 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.

[0569] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0570] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0571] 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.

[0572] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0573] 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.

[0574] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0575] The 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.

[0576] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0577] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0578] This invention is implemented as a system to streamline the document creation process and improve quality. In one embodiment, a user uploads a document from their terminal, and various processes are performed on a server. The server first receives the document from the user using a receiving means. At this stage, the document format can be Word, PDF, or text file, etc.

[0579] Next, the server analyzes the document using analysis tools. This involves using natural language processing techniques to automatically detect grammatical errors and structural inconsistencies within the document, and extracting the main themes and key points of the document.

[0580] Based on the analysis results, the server uses automated correction mechanisms to improve the document. This process includes unifying fonts and paragraph styles, and correcting the placement of images and graphs appropriately, thereby improving the consistency and readability of the document.

[0581] The revised documents are reviewed by the server's evaluation system. This evaluation process assesses the overall quality of the documents and generates user-friendly feedback.

[0582] Finally, the user reviews the document based on the feedback and makes any necessary adjustments. The revised document is then provided as the final version via the server's output system. This ensures that the user receives high-quality documentation.

[0583] For example, when a user creates a business presentation, redundant sections are identified in the input material, and a summary of those sections is suggested by the server. Furthermore, inappropriate formatting and inconsistent fonts in the material are automatically corrected, resulting in a polished presentation document. This reduces the effort involved in creating documents and enables the efficient creation of consistent, high-quality materials.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] Users upload documents they are creating to the system using their devices. Supported formats include Word, PDF, and text files.

[0587] Step 2:

[0588] The server receives uploaded materials using a receiving mechanism and converts them into a format suitable for content analysis. If necessary, OCR technology is used to recognize PDF documents as text data.

[0589] Step 3:

[0590] The server uses analysis tools to analyze the document. Natural language processing technology is used to analyze the grammatical structure and topic of the document, identifying errors and areas for improvement.

[0591] Step 4:

[0592] The server uses automated correction mechanisms to automatically correct the documents based on the analysis results. Specifically, this includes unifying formats and fonts, and rearranging graphs and figures.

[0593] Step 5:

[0594] The server uses evaluation tools to assess the revised document and generates feedback. This feedback includes an overall quality assessment of the document and suggestions for further revisions.

[0595] Step 6:

[0596] The user receives feedback from the server via their device and makes necessary corrections. The user then performs a final review of the content.

[0597] Step 7:

[0598] The server provides the final version of the document to the terminal via an output device, making high-quality materials available to the user.

[0599] (Example 1)

[0600] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] In today's information society, the business and academic fields require the creation of large volumes of documents. However, document creation is time-consuming and labor-intensive, and the tasks of structuring documents, ensuring visual consistency, and organizing content are particularly complex. Furthermore, maintaining document quality while efficiently completing the process is not easy. It is necessary to address these challenges and achieve efficient and high-quality document creation.

[0602] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0603] In this invention, the server includes receiving means for receiving information, analyzing means for analyzing information, and automatic correction means for correcting information based on the analysis results. This automates the document creation process and enables efficient structural analysis of documents, extraction of key points, and ensuring visual consistency.

[0604] "Information" refers to the data and content that make up documents and materials, and includes all forms, such as text, graphs, and images.

[0605] "Receiving means" refers to the functions or devices used by a server to acquire information transmitted from an external source.

[0606] "Analysis means" refers to methods and devices for analyzing received information and evaluating and extracting grammatical errors and the structure of the content.

[0607] "Automatic correction means" refers to functions or devices that improve the formatting and visual consistency of information based on analysis results.

[0608] "Evaluation means" refers to functions or devices for evaluating the quality of corrected information and generating opinions based on the results.

[0609] "Output means" refers to the methods or functions for providing the user with the final, adjusted information.

[0610] "Natural language processing technology" refers to various algorithms and methods that enable computers to understand and analyze human language.

[0611] "Visual consistency" refers to a state where fonts, paragraph styles, and the placement of figures and tables are unified within a document.

[0612] This invention is a system for streamlining information processing and improving the quality of documents. Users can select documents from their own terminals and send them to the server. The terminals can handle document formats such as Word, PDF, and text files.

[0613] The server is equipped with a dedicated receiving mechanism for receiving information, thereby accurately collecting data from external sources. Next, the received information is processed by an analysis mechanism. This process utilizes natural language processing techniques to automatically analyze grammatical errors and the structure of the information. For example, it can extract the main themes and key points from a business report. This analysis uses a generative AI model and a prompt such as, "Please extract the key points from this text."

[0614] Based on the analysis results, the server uses automated correction mechanisms to improve the information. Specifically, formatting and fonts within the document are corrected, and the positions of images and graphs are adjusted. This process improves the visual consistency and readability of the information. For example, in presentation materials, different fonts are unified and images are placed appropriately, making the information easier to understand and organize.

[0615] After revisions are complete, the document is reviewed by the server's evaluation system. This evaluation process checks the overall quality of the document and provides clear feedback to the user. The user then revises the document based on this feedback and makes any necessary adjustments. Finally, the server's output system generates the final version of the document and provides it to the user. This system allows users to efficiently create high-quality, well-organized documents.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The user selects a document from their device and uploads it to the server. The input is a Word, PDF, or text file located on the user's device, and the server receives that file as output. Specifically, the user selects a document file and submits it to the server using a dedicated upload form.

[0619] Step 2:

[0620] The server receives information sent by the user using a receiving device. The input is the file itself sent by the user, and the output is the storage of that information within the server. At this stage, the server checks the file format and determines whether it is a valid file.

[0621] Step 3:

[0622] The server analyzes the received information using analysis tools. The input is the received file, and the output is the text data resulting from the analysis. Specifically, it uses natural language processing techniques to detect grammatical errors in the document and extract the main themes and key points. A generative AI model is used here, and prompts such as "Please extract the important points of this text" are used.

[0623] Step 4:

[0624] The server uses automated correction mechanisms based on the analysis results to improve the information. The input is the analyzed text data, and the output is a corrected formatted document. Specific actions include unifying fonts, unifying paragraph styles, and properly positioning images and graphs.

[0625] Step 5:

[0626] The server evaluates the corrected document using evaluation tools. The input is the corrected document, and the output is feedback to be provided to the user. Here, the overall quality of the document is checked, evaluated in terms of readability and consistency, and feedback that is easy for the user to understand is generated.

[0627] Step 6:

[0628] The user reviews feedback from the server and makes a final check of the document. The input is the feedback message provided by the server, and the output is the final document with the corrections made. The user adjusts the document based on the feedback and corrects the content if necessary.

[0629] Step 7:

[0630] The server outputs the user-edited document as the final version. The input is the user-edited document, and the output is the final version of the document provided to the user. At this stage, the server exports the document in the specified format and prepares it for the user to download.

[0631] (Application Example 1)

[0632] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Generating high-quality and consistent documents automatically is difficult in information creation. Furthermore, manual document compilation by users is time-consuming, labor-intensive, and increases the risk of errors. In particular, efficient and highly accurate automation methods are needed to organize complex information clearly and quickly create user guides in areas such as electronic payment services.

[0634] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0635] In this invention, the server includes a receiving means for acquiring information, an analysis means for analyzing the acquired information, and an automatic correction means for correcting the information based on the analysis results. This enables high-quality and consistent information distribution through the automatic generation of documents.

[0636] "Information" refers to data and knowledge expressed in a tangible form, which are processed for the purpose of understanding and utilizing their content.

[0637] A "receiving means" is a device or element that has the function of collecting information sent from an external source and plays the role of incorporating this information into the system.

[0638] "Analysis means" refers to techniques and devices used to examine acquired information in detail and identify its structure and content.

[0639] "Automatic correction methods" refer to technologies and means for automatically adjusting documents and data to correct errors in information based on analysis results and to ensure consistency.

[0640] "Evaluation means" refers to devices or elements that have the function of determining the quality and validity of corrected information and generating responses to appropriately convey that information to users.

[0641] "Output means" refers to devices or elements that have the function of providing processed and evaluated information to the user in its final form.

[0642] In a mode for carrying out the invention, the system that realizes this application example is mainly composed of a program that runs on a server. The server receives information, analyzes it, and automatically makes corrections.

[0643] The server first receives information from the user. At this stage, the information can take the form of text files or digital documents. The received information is then analyzed using natural language processing software such as "spaCy" or "transformers" for text analysis. This analysis includes checking grammatical structure and extracting key points.

[0644] Next, based on the analysis results, the information is corrected using an automated correction function. This process involves correcting typographical errors, converting to a unified format, and restructuring the information. It is common to use data processing libraries such as "Pandas" or "NumPy" for this stage.

[0645] At the end of the process, the corrected information is evaluated, and a final response is generated. The evaluation process verifies the integrity and consistency of the information and provides feedback to the user in an easy-to-understand format. This enables the automatic generation of documents such as user guides and FAQs for electronic payment services.

[0646] For example, if a user is trying to create a guide about a new cashback feature, the server will analyze information about that feature and generate text in a way that is easy for users to understand. A generative AI model may be used in this process. An example of a prompt might be, "Carefully analyze the following text, correct any errors, and transform it into an effective user guide."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] Users send information to the server using their own devices. The information is in the form of text files or digital documents. The server collects this information through receiving mechanisms. The input is information from the user, and the output is the original data stored on the server.

[0650] Step 2:

[0651] The server analyzes the received information using analysis tools. This analysis uses natural language processing software such as "spaCy" and "transformers." Specifically, it performs grammar checks, sentence structure analysis, and key point extraction. The input is the information received in step 1, and the output is the analysis result data.

[0652] Step 3:

[0653] The server corrects the information using automated correction methods based on the analysis results. This process uses "Pandas" and "NumPy" to correct typos and standardize formatting. Furthermore, the overall document format is refined. The input is the analysis result data from step 2, and the output is the corrected document data.

[0654] Step 4:

[0655] The server verifies the corrected information using an evaluation tool. The evaluation criteria focus on document consistency and coherence. Through the evaluation, a response is generated for the user and recorded as feedback. The input is the corrected document data from step 3, and the output is the evaluated feedback data.

[0656] Step 5:

[0657] Ultimately, the server provides the evaluated and corrected information as the final version to the user's terminal via an output mechanism. The input is the evaluation feedback data from step 4, and the output is the completed document received by the user. In this step, prompt text generated by a generative AI model may also be used.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] This invention is implemented as a system that provides user-emotion-based feedback by incorporating an emotion engine into the document creation process. This system has a configuration in which the user uploads documents from their own device, and the documents are processed on a server.

[0660] The server first receives the data using a receiving device. Then, it analyzes the content of the data using an analysis device and analyzes the document structure in detail using natural language processing technology. In this process, grammatical errors and logical inconsistencies are identified.

[0661] After analysis, the server uses automated correction mechanisms to automatically make corrections such as standardizing the document's format and font size. These corrections ensure consistency and readability of the document.

[0662] Furthermore, the emotion engine analyzes the user's voice or text input to identify their emotional state. This emotional information is taken into account when generating feedback, which is delivered in a tone appropriate to the user's emotions. This feedback includes suggestions for improvement, merits, and further suggestions for the material.

[0663] For example, if the emotion engine detects stress in a user while they are creating a presentation, the feedback provided will be tailored to alleviate that stress. This allows the user to improve the quality of their presentation while reducing their psychological burden.

[0664] Ultimately, users refine their materials based on feedback, and the completed materials are provided as the final version through the server's output system. This allows users to efficiently create high-quality, consistent materials.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] Users create the target documents using their devices and upload them to the system. The file format can be Word, PDF, or text file.

[0668] Step 2:

[0669] The server receives the data using the receiving means and prepares it for processing by the analysis means. The data is converted into a format that can be analyzed as text data.

[0670] Step 3:

[0671] The server uses analysis tools and natural language processing techniques to analyze the content and structure of the document. It evaluates grammatical errors and content consistency.

[0672] Step 4:

[0673] Based on the analysis results, the server automatically corrects the document using automated correction mechanisms. This includes automatically adjusting formatting, unifying font sizes, and repositioning images and graphs as needed.

[0674] Step 5:

[0675] The user provides voice or text input from their device for the emotion engine to use. The emotion engine then analyzes the user's emotional state based on this input.

[0676] Step 6:

[0677] The server uses evaluation tools to re-evaluate the corrected material. Based on the sentiment information detected by the sentiment engine, it adjusts the tone and content of the feedback.

[0678] Step 7:

[0679] Users receive feedback from the server via their devices and perform final checks and revisions to the materials. Necessary revisions are made based on the feedback.

[0680] Step 8:

[0681] The server uses an output mechanism to output the corrected and verified final version of the document to the terminal. This allows the user to obtain a high-quality document.

[0682] (Example 2)

[0683] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] In document creation, in addition to analyzing the structure and content of documents, there is a need to facilitate the provision of feedback that takes into account the user's emotional state, thereby supporting the creation of consistent, high-quality documents. In particular, there is a lack of means to provide effective, emotion-based feedback, and improvement is needed in this area.

[0685] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0686] In this invention, the server includes receiving means for receiving data, analyzing means for analyzing the received data, emotion recognition means for identifying the user's emotional state, and output means for final outputting the data based on the feedback. This makes it possible to provide feedback that takes the user's emotional state into consideration.

[0687] "Receiving means" refers to a device or function for receiving and temporarily storing data sent by a user on a server.

[0688] "Analysis means" refers to a device or program for analyzing the content of received materials and performing structural analysis of documents or error detection.

[0689] An "automatic correction means" is a device or function that automatically corrects the format and style of a document based on the analysis results in order to improve its quality.

[0690] An "emotion recognition means" is a device or program that analyzes a user's voice or text input to identify their emotional state.

[0691] A "feedback generation means" is a device or function for generating feedback that provides improvement suggestions and information based on analysis results and emotional information.

[0692] "Output means" refers to a device or function for providing the user with the final revised or improved material.

[0693] This invention aims to provide a system that helps users efficiently create high-quality and consistent documents. The system utilizes the following hardware and software.

[0694] First, the user uploads materials using a device. This device is connected to the server via the internet. The materials selected by the user are sent to the server via a dedicated web interface.

[0695] The server receives data via a receiving device and then temporarily stores it in storage. The received data is then analyzed by an analysis device using natural language processing techniques. This analysis utilizes open-source natural language processing libraries to analyze document structure and detect errors.

[0696] Next, the server uses automated correction mechanisms to standardize the document's formatting and font size. This ensures readability and consistency throughout the document. Software used at this stage includes a document formatting adjustment library.

[0697] User input (voice or text) is analyzed by emotion recognition means on the server to identify the emotional state. Emotion recognition uses specialized software for voice analysis and emotion analysis algorithms.

[0698] Based on emotional information, the server uses a generative AI model to generate feedback. This generated feedback is then returned to the user and used to refine and improve the document. An example of a prompt used as input to the generative AI model is, "Please provide feedback that will alleviate the user's anxiety."

[0699] Ultimately, the server provides the user with the final version of the document, which has been proactively and effectively adjusted, through its output mechanism. In this way, it is possible to improve the quality of the document while reducing the psychological burden on the user during the document creation process. For example, if a user experiences stress while creating a project report, the feedback will include a message encouraging relaxation.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The user opens the interface on their device, selects a document, and uploads it. The input is the document file selected by the user, which is sent to the server via the device's internet connection. This process performs a basic check to ensure there are no problems with the format or content of the document. The output is the completion of the document's transmission to the server.

[0703] Step 2:

[0704] The server receives data sent from users using a receiving mechanism and stores it in storage. Input is data sent from the terminal, and the server automatically verifies the file format and integrity of the data upon saving. Output is the verified data stored in the database.

[0705] Step 3:

[0706] The server uses analysis tools to analyze received data using natural language processing techniques. The input is stored data, and the analysis includes document structure analysis, keyword extraction, and error detection. Specifically, it uses open-source natural language processing libraries. The output is a dataset containing the analysis results.

[0707] Step 4:

[0708] The server utilizes automated correction mechanisms to standardize the formatting and font size of documents based on the analysis results. The input is a dataset of the analyzed documents, and the document's appearance is refined through data processing. The output is the corrected document data.

[0709] Step 5:

[0710] The server uses emotion recognition means to identify the user's emotional state from their voice or text input. The input is the user's most recent voice or text data, and the emotion is inferred using an emotion analysis algorithm. The output is data of the inferred emotional state.

[0711] Step 6:

[0712] The server uses a generative AI model to execute a feedback generation mechanism, combining analysis results and emotional information to create feedback. The input consists of analyzed data and emotional state data from the document, and the generative AI model generates feedback based on a prompt. For example, the prompt might be "Please provide feedback to alleviate the anxiety the user is feeling." The output is specific feedback for the user.

[0713] Step 7:

[0714] The user receives feedback generated by the server and revises the document based on it. The input is the provided feedback, and the user manually adjusts the document. The output is the final version of the document, incorporating the feedback.

[0715] Step 8:

[0716] The server uses output methods to provide the user with the final version of the document, modified by the user. The final version is provided via download links or email through the server. The input is the completed document data, and the output is the final document viewable by the user.

[0717] (Application Example 2)

[0718] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] During document creation, it is essential to understand the user's emotions appropriately and provide feedback tailored to those emotions in order to reduce their psychological burden and improve the quality of the document. However, conventional systems have not adequately provided feedback that takes user emotions into account, sometimes causing users to experience excessive stress during the creation process. Therefore, there is a need for technology that provides appropriate feedback based on the user's emotions.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0721] In this invention, the server includes emotion analysis means for detecting emotional states, feedback adjustment means for adjusting feedback based on the emotions identified by the emotion analysis means, and advice generation means for presenting the strengths and weaknesses of the material. This makes it possible to provide feedback that is tailored to the user's emotions.

[0722] "Emotion analysis means" refers to technology that analyzes a user's voice or text input to detect their emotions.

[0723] A "feedback adjustment mechanism" is a technology that has the function of adjusting the content and tone of feedback based on the user's emotions identified by an emotion analysis mechanism.

[0724] An "advice generation method" is a technology for automatically generating advice on the strengths and areas for improvement of a document based on its evaluation.

[0725] "Tone adjustment techniques" are technologies that adjust the style of expression so that the feedback provided reduces psychological burden.

[0726] A "generative AI model" is a model that uses artificial intelligence to generate appropriate responses and advice in response to user input.

[0727] A "prompt" is a sentence used as input data for a generative AI model, serving as a guide for the model when generating a response.

[0728] The system for realizing this application primarily consists of programs running on a server. The server receives data from the user's terminal and first analyzes the user's voice and text input using sentiment analysis techniques. This sentiment analysis utilizes natural language processing and speech recognition technologies. Speech recognition APIs such as Microsoft's Azure Cognitive Services or Google Cloud Natural Language API can be used for this purpose.

[0729] Based on the analyzed emotions, the feedback adjustment mechanism optimizes the feedback provided to the user via a generative AI model. This AI model can utilize open-source GPT (Generative Pre-trained Transformer), among others. Based on the evaluation of the material, the advice generation mechanism creates specific feedback, including the strengths and areas for improvement of the material. This feedback is then adjusted by the tone adjustment mechanism to reduce the user's psychological burden.

[0730] As a concrete example, imagine an elementary school student creating a presentation at home. The user (elementary school student) uses voice input to express their opinions and anxieties regarding the material they are creating. If the emotion analysis system detects stress in the student, the server will provide gentle feedback in a calm tone, such as "Take a deep breath and relax," and also offer specific advice regarding the content of the material, such as "Adding examples to the content will make it easier to understand."

[0731] An example of a prompt for a generative AI model is, "When a child is feeling stressed while creating school presentation materials, please provide advice on how to improve the quality of the materials while also alleviating their psychological burden." In this way, the system implementing the invention can provide helpful feedback that aligns with the user's emotions.

[0732] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0733] Step 1:

[0734] When creating documents, users use voice input from their devices. The devices send this voice data to a server. This voice data is used as input for analyzing the user's emotions.

[0735] Step 2:

[0736] The server inputs the received audio data into an emotion analysis system. Here, Microsoft's Azure Cognitive Services speech recognition API is used to convert the audio data into text data. Furthermore, the Google Cloud Natural Language API is used to identify the user's emotions from the text. This step outputs data representing the user's emotional state.

[0737] Step 3:

[0738] The server generates user-facing feedback using a feedback adjustment mechanism, based on the emotional state identified by the emotion analysis mechanism. This process uses the open-source generative AI model GPT, and is input with the prompt "Provide stress-reducing advice based on the user's current emotions." The model then outputs specific feedback corresponding to the emotional state.

[0739] Step 4:

[0740] The generated feedback is adjusted in content and style using tone adjustment mechanisms. The generated feedback is given an appropriate tone to reduce the psychological burden on the user. As an output, specific advice can be displayed to the user.

[0741] Step 5:

[0742] The server sends the refined feedback to the user's terminal, providing them with specific advice to help them create their materials. Users can then use this feedback to refine their materials and create high-quality documents.

[0743] 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.

[0744] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0745] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0746] 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.

[0747] Figure 9 shows an 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0748] 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.

[0749] 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.

[0750] 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, motorcycles, etc., 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, for example, based 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.

[0751] 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."

[0752] 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.

[0753] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0754] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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 the like 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.

[0763] 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.

[0764] The following is further disclosed regarding the embodiments described above.

[0765] (Claim 1)

[0766] A means of receiving data,

[0767] An analytical means for analyzing received data,

[0768] An automated correction mechanism for correcting documents based on analysis results,

[0769] An evaluation method for evaluating the revised materials and generating feedback,

[0770] An output method for finalizing the document based on feedback,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, wherein the analysis means analyzes the structure of the document using natural language processing technology.

[0774] (Claim 3)

[0775] The system according to claim 1, wherein the automatic correction means unifies the formatting and font size of the document.

[0776] "Example 1"

[0777] (Claim 1)

[0778] A means of receiving information,

[0779] An analysis means for analyzing the received information,

[0780] An automatic correction mechanism for correcting information based on the analysis results,

[0781] An evaluation method for evaluating corrected information and generating opinions,

[0782] An output means for finalizing information based on opinions,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, wherein the analysis means analyzes the structure of information using natural language processing technology and extracts the main themes and key points.

[0786] (Claim 3)

[0787] The system according to claim 1, wherein the automatic correction means unifies the formatting and character style in order to improve the visual consistency and readability of the information.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] A means of receiving information,

[0791] An analytical means for analyzing the acquired information,

[0792] An automated correction mechanism for correcting information based on the analysis results,

[0793] An evaluation means for evaluating corrected information and generating a response,

[0794] An output means for outputting information based on the reaction,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, wherein the analysis means analyzes the structure of information using natural language processing technology.

[0798] (Claim 3)

[0799] The system according to claim 1, wherein the automatic correction means unifies the formatting and character style of the information.

[0800] "Example 2 of combining an emotion engine"

[0801] (Claim 1)

[0802] A means of receiving data,

[0803] An analytical means for analyzing received data,

[0804] An automated correction mechanism for correcting documents based on analysis results,

[0805] A means of recognizing an emotion to identify the user's emotional state,

[0806] A feedback generation means for generating feedback based on emotional information,

[0807] An output method for finalizing the document based on feedback,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the analysis means analyzes the structure of the document using natural language processing technology.

[0811] (Claim 3)

[0812] The system according to claim 1, wherein the automatic correction means unifies the formatting and font size of the document.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] An emotion analysis method for detecting emotional states,

[0816] A feedback adjustment means for adjusting feedback based on emotions identified by emotion analysis means,

[0817] A means for generating advice to present the strengths and areas for improvement of the material,

[0818] Tone adjustment measures to reduce psychological burden when advice is given,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, wherein the emotion analysis means analyzes the input using speech recognition technology.

[0822] (Claim 3)

[0823] The system according to claim 1, wherein the advice generation means generates prompt sentences using a generation AI model. [Explanation of Symbols]

[0824] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information, An analytical means for analyzing the acquired information, An automated correction mechanism for correcting information based on the analysis results, An evaluation means for evaluating corrected information and generating a response, An output means for outputting information based on the reaction, A system that includes this.

2. The system according to claim 1, wherein the analysis means analyzes the structure of information using natural language processing technology.

3. The system according to claim 1, wherein the automatic correction means unifies the formatting and character style of the information.

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