system

Abstract

We provide the system. [Solution] A means of receiving prompts, emphasis points, and usage instructions from the user, A means of uploading and analyzing past documents, A means of training an AI model based on prompts and past data, A means for generating materials based on prompts and emphasis points, A means of providing the generated materials to the user, 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional document creation process, it is often done manually, which requires time and labor, so there is a problem of reduced productivity. Also, it is difficult to maintain the consistency of the format and content of documents, and quality variations often occur. Furthermore, when a user creates a document based on a specific prompt, the effort required to refer to past documents is also a major problem.

Means for Solving the Problems

[0005] To solve these problems, the present invention provides a means for receiving prompts, emphasis points, and intended use from the user. This means allows the user to communicate specific requests to the system. Next, it provides a means for uploading and analyzing past materials. This means allows the system to learn important patterns and formats from past materials. Furthermore, it provides a means for training an AI model based on prompts and past materials. This means the system has the ability to generate efficient and consistent materials. Finally, by providing a means for generating materials based on prompts and emphasis points, and a means for providing the generated materials to the user, the user can easily and quickly obtain high-quality materials. In this way, the present invention achieves increased efficiency and improved quality in the material creation process.

[0006] A "user" refers to an individual or organization that operates the system, inputting prompts, highlights, and intended use to request document generation.

[0007] A "prompt" is text or instructions that a user enters to specify the content or subject of a document.

[0008] "Emphasis points" are the information users enter to specify parts or important points they want to draw particular attention to within a document.

[0009] "Intended use" refers to information that specifies the purpose and circumstances under which the generated materials will be used.

[0010] "Past documents" refer to documents and reports that have been created and uploaded to the system for learning purposes.

[0011] "Analysis" refers to the process of analyzing and organizing past data and user-generated information using natural language processing technology and other methods.

[0012] An "AI model" is an algorithm or system that uses artificial intelligence technology to learn patterns and rules from data and perform predictions and generation.

[0013] "Document generation" refers to the process of creating a new document based on user-entered prompts and highlights, as well as data obtained from past documents.

[0014] "Providing" refers to presenting the generated materials in a format that users can use. [Brief explanation of the drawing]

[0015] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of this invention automatically generates materials using AI based on prompts and emphasis points specified by the user. The specific implementation of this system is described below.

[0037] System Overview

[0038] The user accesses the system using a terminal. The terminal provides input forms for entering prompts, highlights, and usage details. The user uploads past materials, and the system initiates a process of generating new materials based on these uploads.

[0039] Program processing (explained in natural language)

[0040] 1. Obtaining user input

[0041] The terminal accepts input from the user. The user enters prompts, emphasis points, and intended use for generating the document into the terminal. For example, if the user wants to create a notice about an important meeting next week, they would enter that into the terminal.

[0042] 2. Sending input data

[0043] The terminal sends the data entered by the user to the server. This data serves as the basis for later document generation.

[0044] 3. Upload data

[0045] Users upload past documents to the system via their terminals. These documents include past reports, manuals, and other related documents.

[0046] 4. Learning Process

[0047] The server analyzes the uploaded materials and extracts patterns in format and content using natural language processing techniques. This allows the AI ​​model to learn patterns from past materials.

[0048] 5. Prompt Analysis

[0049] The server analyzes prompts and highlights sent by the user. In doing so, it also considers the intended use and determines the structure of the document based on those guidelines.

[0050] 6. Data generation

[0051] The server generates materials using a trained AI model. Specifically, it automatically generates the content of new materials based on user prompts and past materials. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a material like the following:

[0052] Subject: Notice regarding next week's important meeting

[0053] Content: A meeting (important) will be held next week. Please note that attendance is mandatory. Also, please review the materials in advance of the meeting.

[0054] 7. Application of the format

[0055] The server applies the appropriate format to the generated documents. Depending on the intended use, it prepares formats for internal announcements, external manuals, and other documents.

[0056] 8. Providing the results

[0057] Once the generated document is complete, the server sends it to the terminal for the user to review. The user can review the document and make corrections or regenerate it as needed.

[0058] This system allows users to efficiently generate high-quality documents with minimal effort. For example, using this system when creating internal announcements or external manuals can save time and effort while maintaining consistency and quality.

[0059] The above describes a specific embodiment for implementing the system of the present invention. This system aims to generate efficient and high-quality materials based on user input and utilizing AI technology.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] The user opens the input form.

[0063] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[0064] Step 2:

[0065] The user enters the required information.

[0066] The user enters a prompt (e.g., "Notice regarding an important meeting next week"), a key point (e.g., "Mandatory attendance, please review materials in advance"), and a purpose (e.g., "Internal announcement").

[0067] Step 3:

[0068] The terminal sends the input data to the server.

[0069] The terminal sends user-entered prompts, highlights, and usage data to the server. The data is transmitted using a secure communication protocol.

[0070] Step 4:

[0071] Users upload past documents.

[0072] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[0073] Step 5:

[0074] The device sends past data to the server.

[0075] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[0076] Step 6:

[0077] The server analyzes past data.

[0078] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[0079] Step 7:

[0080] The server trains the AI ​​model.

[0081] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0082] Step 8:

[0083] The server parses the prompt.

[0084] The server analyzes prompts and emphasis points sent by the user. It extracts keywords and context, and determines guidelines for creating the framework of the document.

[0085] Step 9:

[0086] The server generates the contents of the document.

[0087] The server uses a trained AI model to generate information based on prompts and past data. It generates specific content that reflects the information entered by the user.

[0088] Step 10:

[0089] The server formats the documents.

[0090] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0091] Step 11:

[0092] The server sends the generated data to the terminal.

[0093] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0094] Step 12:

[0095] The device displays the document.

[0096] The terminal displays the materials generated for each user. Users can review the materials and modify or regenerate them as needed.

[0097] (Example 1)

[0098] 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."

[0099] Conventional information generation systems require users to spend a significant amount of time and effort creating new documents, and in particular, it is difficult to generate consistent content that reflects past documents. Furthermore, standardizing the format and content of documents is also difficult, resulting in a user-unfriendly environment. This invention aims to solve these problems and provide a system that automatically generates efficient and high-quality documents.

[0100] 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.

[0101] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past documents; and means for training a generation AI model based on the prompts and past documents. This makes it possible to efficiently generate new documents based on prompts entered by the user and past documents. Furthermore, it is possible to maintain the consistency and quality of the generated documents.

[0102] A "prompt" is a set of instructions entered by the user to specify the content and purpose of the document they are generating.

[0103] "Emphasis points" are instructions that allow users to specify the parts or content they consider particularly important when creating a document.

[0104] "Intended use" refers to the context and purpose for which the generated materials will be used.

[0105] "Documents" refer to information including reports, manuals, and other related materials created in the past.

[0106] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate text data.

[0107] Natural Language Processing (NLP) is a technology that enables computers to understand and process human language.

[0108] A "pattern" refers to the regularity or similarity of structure and expression found in past documents.

[0109] A "server" is a centralized computing device that processes data sent by users and generates materials using generative AI models.

[0110] The system of the present invention automatically generates materials using a generative AI model based on prompts and emphasis points specified by the user. A specific embodiment of this system is described below.

[0111] First, a server, terminals, and a generative AI model are necessary to support the system's operation. The server can be a cloud server (e.g., Amazon Web Services, Google Cloud Platform). The terminals are devices that provide a user interface, such as PCs, smartphones, and tablets. For the generative AI model, natural language processing technologies such as GPT-3® and BERT are used.

[0112] Users access the system using a terminal. The terminal provides input forms for users to enter prompts, highlights, and intended use for generating documents. Users upload past documents, and the system initiates the process of generating documents based on these. For example, if a user wants to create an "Announcement Regarding an Important Meeting Next Week," they would enter this into the terminal. The terminal sends the data entered by the user to the server. This data serves as the basis for subsequent document generation.

[0113] Next, the user uploads existing documents, including past reports, manuals, and other relevant documents. The server analyzes the uploaded documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn patterns from past documents.

[0114] The server analyzes prompts and highlights sent by the user, and determines the structure of the document, taking into account its intended use. It then automatically generates new documents using a generative AI model. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a document like this:

[0115] Subject: Notice regarding next week's important meeting

[0116] Content: An important meeting will be held next week. Please note that attendance is mandatory. Also, please review the details before the meeting.

[0117] The server applies the appropriate format to the generated documents. Specifically, it formats internal announcements and external manuals according to their intended use. The completed documents are sent from the server to the terminal, where the user can review them. The user can review the documents and make corrections or regenerate them as needed.

[0118] As described above, the system of the present invention allows users to efficiently generate high-quality materials without requiring much effort. For example, by using this system when creating internal announcements or external manuals, it is possible to save time and effort while maintaining consistency and quality of the materials.

[0119] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0120] Step 1:

[0121] Obtaining user input

[0122] Input: The user uses the terminal to enter prompts, emphasis points, and usage details.

[0123] Specific operation: The terminal provides an input form and accepts the content specified by the user. For example, the user enters a prompt such as "Notice regarding an important meeting next week" and an emphasis such as "Mandatory attendance."

[0124] Output: The user data entered into the terminal is prepared.

[0125] Step 2:

[0126] Sending input data

[0127] Input: Data entered by the user into the device.

[0128] Specific operation: The terminal sends the entered prompts, highlights, and usage information to the server. The data is securely transferred over the network.

[0129] Output: The input data is sent to the server and used for subsequent processing.

[0130] Step 3:

[0131] Uploading past documents

[0132] Input: Past document files (PDF, text files, etc.) uploaded by the user.

[0133] Specific operation: The user uploads past reports and manuals to the server via a terminal. The documents are saved in the appropriate format.

[0134] Output: Uploaded documents are stored on the server and used for later analysis.

[0135] Step 4:

[0136] Learning process

[0137] Input: Uploaded past documents.

[0138] Specific operation: The server analyzes uploaded documents using natural language processing techniques. Specifically, it uses Python's NLTK library and spaCy to tokenize the text within the documents and extract patterns and formats. A generative AI model (such as GPT-3 or BERT) is then trained based on the extracted patterns.

[0139] Output: A trained generative AI model is generated, and the process proceeds to the next step.

[0140] Step 5:

[0141] Prompt analysis

[0142] Input: Prompts and emphasis points sent by the user.

[0143] Specific operation: The server analyzes prompts and highlights to determine the structure and organization of the document. The intended use is also considered. Natural language processing technology is used in this process, including keyword extraction and semantic analysis.

[0144] Output: Analysis data based on prompts and highlight points is generated.

[0145] Step 6:

[0146] Data generation

[0147] Input: A pre-trained generative AI model and analyzed prompt data.

[0148] Specific operation: The server generates documents using a trained generative AI model. Based on prompts and patterns derived from past documents, it automatically constructs the content of new documents. For example, it generates a document titled "Announcement regarding next week's important meeting."

[0149] Output: Automatically generated documents will be produced.

[0150] Step 7:

[0151] Application of format

[0152] Input: Automated document.

[0153] Specific operation: The server applies the appropriate format to the generated document. It adjusts fonts, layout, headers, footers, etc., according to the intended use.

[0154] Output: A completed document with the appropriate formatting applied will be generated.

[0155] Step 8:

[0156] Providing results

[0157] Input: Completed document.

[0158] Specific operation: The server sends the generated document to the terminal. The user can review the document through the terminal and make corrections or provide feedback as needed.

[0159] Output: The completed document will be provided in a format that the user can review.

[0160] (Application Example 1)

[0161] 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."

[0162] Traditional document generation systems struggled to maintain consistency in work manuals and quality control reports generated by factory workers, requiring significant time and effort. Furthermore, errors and inconsistencies in the generated documents could reduce on-site work efficiency and potentially impact safety. A solution to this problem is needed.

[0163] 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.

[0164] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past data; means for training an AI model based on the prompts and past data; means for generating data based on the prompts and emphasis points; means for applying a specified format to the generated data; and means for checking and correcting the generated data on the operating terminal of the machine. This makes it possible to quickly generate high-quality data while saving effort and time, while maintaining consistency in factory work manuals and quality control reports.

[0165] A "prompt" refers to the instructions or initial information that a user enters into the system.

[0166] "Emphasis points" refer to the parts or content in the generated document that should be given particular importance.

[0167] "Intended use" refers to the specific situations and purposes in which the generated materials will be used.

[0168] "Past documents" refer to documents and data created previously, and are used as reference material for generating new documents.

[0169] An "AI model" refers to a learning model that automatically generates the content and format of materials based on artificial intelligence technology.

[0170] "Natural language processing" is a technology that uses computers to process and understand human language.

[0171] "Mechanical equipment" refers to devices such as work terminals and robots used within a factory.

[0172] An "operating terminal" is an electronic device used by a user to access, operate, and verify a system.

[0173] "Specified format" refers to the predetermined document format or style to which the generated material will be applied.

[0174] "Review and correction" means that the user checks the generated document and corrects its accuracy and suitability as needed.

[0175] The system of the present invention automatically generates materials using AI based on prompts and emphasis points specified by the user. Specific embodiments of the system are described below.

[0176] Hardware and software to be used

[0177] 1. Hardware:

[0178] Operating terminal: A console terminal within the factory, or a device operated directly by the user.

[0179] Machinery and equipment: Dedicated terminals and robots for displaying documents within the factory.

[0180] 2. Software:

[0181] The overall programming language used is Python.

[0182] SpaCy and NLTK are used for natural language processing.

[0183] The GPT-3 API of OpenAI (registered trademark) is used for the automatic generation of documents.

[0184] The server has the following functions:

[0185] Detailed processing

[0186] 1. Prompt input form:

[0187] The user inputs prompts and key points for the document they want to generate via the operating terminal. For example, if the user wants to create a document summarizing important points regarding the next scheduled maintenance, they would input that information.

[0188] 2. Uploading past documents:

[0189] Users upload past work manuals and quality control reports to the server via their operating terminals. The uploaded materials are used as training material for the AI ​​model.

[0190] 3. Analysis Process:

[0191] The server analyzes the uploaded historical documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn these patterns from past documents.

[0192] 4. Document generation process:

[0193] The server analyzes user prompts and emphasis points, and determines the structure of the document based on those guidelines. Using GPT-3, it generates new documents based on past documents and user input. For example, it might create a document summarizing important points regarding the next scheduled maintenance work, or a document specifically mentioning enhanced safety measures.

[0194] 5. Apply format:

[0195] The server applies a specified format within the factory to the generated documents. For example, it formats them to internal memos or quality control reports.

[0196] 6. Review and Correction:

[0197] The user reviews the generated documents on the operating terminal and corrects the accuracy and suitability of the content as needed.

[0198] Specific example

[0199] Example of a prompt:

[0200] 1. Prompt: Document summarizing important points regarding the next scheduled maintenance work.

[0201] 2. Emphasis: Please specifically mention the strengthened safety measures.

[0202] This allows for significant savings in labor and time while maintaining consistency in work manuals and quality control reports in factory operations. It also contributes to improved safety and operational efficiency.

[0203] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0204] Step 1:

[0205] The user uses a terminal to input prompts, emphasis points, and intended use of the document they wish to generate into an input form. This input form accepts prompts and emphasis points, and the user's input is saved as data on the terminal.

[0206] Step 2:

[0207] The terminal sends the entered prompts, highlights, and usage data to the server. This transmitted data serves as a guide for generating the document.

[0208] Step 3:

[0209] Users upload past documents (e.g., work manuals, quality control reports, etc.) to the system via an operating terminal. The uploaded documents are stored on the server.

[0210] Step 4:

[0211] The server analyzes the uploaded historical data. Using natural language processing techniques, it extracts patterns in the format and content of the uploaded data. Specifically, it uses SpaCy and NLTK to decompose and analyze the text, and applies important patterns and structures as data to a learning model.

[0212] Step 5:

[0213] The server analyzes prompts and highlights, and sets the framework of the document by comparing them with patterns extracted from past materials. In this process, an AI model (such as GPT-3) is used to perform data calculations based on user prompts, and initial data for the new document content is generated.

[0214] Step 6:

[0215] The server then applies patterns from past documents to the initial generated documents, smoothly formatting them. For example, it processes them into internal memo formats or quality report formats. During this process, the documents are restructured according to the specified format.

[0216] Step 7:

[0217] The server sends the final generated document to the terminal. The terminal displays the received document to the user, who then reviews the content and makes corrections as needed. At this stage, the data corrected by the user is fed back into the system, improving the quality of the document.

[0218] 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.

[0219] The system of the present invention automatically generates materials using AI based on prompts, emphasis points, and intended use specified by the user, and further incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials. The specific implementation of this system is described below.

[0220] System Overview

[0221] The user uses a terminal to input prompts, emphasis points, and intended use through an input form. During this process, an emotion engine recognizes the user's emotions from the input data and adjusts the content and tone of the material based on the results. The user can also upload past materials, which initiates a process where the system generates new materials.

[0222] Program processing (explained in natural language)

[0223] 1. Obtaining user input

[0224] The terminal provides a form for the user to input prompts, emphasis points, and usage purposes. For example, the user might enter "Notice regarding an important meeting next week."

[0225] 2. Recognition of emotions

[0226] While the user enters information, the emotion engine analyzes the input data and recognizes the user's emotions. For example, it can sense "urgency" or "importance" from the user's input.

[0227] 3. Sending input data

[0228] The device sends data regarding prompts, emphasis points, usage, and sentiment to the server. This data forms the basis for generating the document.

[0229] 4. Upload data

[0230] Users upload past documents to their devices. These documents include past reports, manuals, and other related materials.

[0231] 5. Sending past documents

[0232] The device sends previously uploaded data to the server.

[0233] 6. Analysis of historical documents

[0234] The server analyzes the received historical document data using natural language processing techniques to extract the content, format, and important patterns of the documents.

[0235] 7. Training the AI ​​model

[0236] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0237] 8. Analysis of prompts and emotions

[0238] The server analyzes user prompts, emphasis points, and sentiment data. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[0239] 9. Data generation

[0240] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, when creating a "Notice Regarding an Important Meeting Next Week," if the emotion engine detects "urgency," it will generate material such as:

[0241] Subject: Notice of a very important meeting

[0242] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[0243] 10. Formatting of documents

[0244] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0245] 11. Sending of generated materials

[0246] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0247] 12. Display of materials

[0248] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[0249] Specific example

[0250] Example 1: Creating an internal company announcement

[0251] The user enters "Notice regarding an important meeting next week" and adds "Mandatory attendance, please review materials in advance" as a point of emphasis. If the emotion engine detects urgency, the system adjusts the tone of the material and generates a notice that includes the emphasized content.

[0252] Example 2: Creating a manual for external use

[0253] The user enters "How to use the new product" and "Safety precautions" as a point of emphasis. If the sentiment engine detects that a "user-friendly" tone is desired, the system adjusts the content of the document to make it more approachable and generates the final document.

[0254] In this way, the present invention can efficiently generate materials that reflect the user's emotions and satisfy the user's requirements.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The user opens the input form.

[0258] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[0259] Step 2:

[0260] The user enters the required information.

[0261] The user enters prompts (e.g., "Notice regarding an important meeting next week"), emphasis points (e.g., "Mandatory attendance, please review materials in advance"), and intended use (e.g., "Internal announcement"). As the user enters the information, the sentiment engine analyzes it and recognizes the user's emotions.

[0262] Step 3:

[0263] The terminal sends the input data to the server.

[0264] The device sends user-entered prompts, emphasis points, usage, and sentiment data to the server. The data is transmitted using a secure communication protocol.

[0265] Step 4:

[0266] Users upload past documents.

[0267] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[0268] Step 5:

[0269] The device sends past data to the server.

[0270] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[0271] Step 6:

[0272] The server analyzes past data.

[0273] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[0274] Step 7:

[0275] The server causes the AI model to learn

[0276] The server updates and causes the AI model to learn based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary for generating new materials.

[0277] Step 8:

[0278] The server analyzes the prompt and sentiment

[0279] The server analyzes the prompt, emphasis points, and sentiment data sent from the user. It extracts keywords, context, and the user's sentiment, and determines guidelines for creating the framework of the material.

[0280] Step 9:

[0281] The server generates the content of the material

[0282] The server uses the learned AI model to generate the material. Based on the prompt and sentiment from the user, it generates specific content. For example, when the user wants to create a "Notice about an important meeting next week", when the sentiment engine senses "urgency", it generates the following material:

[0283] Subject: Notice of a very important meeting

[0284] Content: There is an important meeting to be held next week. All are required to attend. Please check the materials in advance.

[0285] Step 10:

[0286] The server formats the material

[0287] The server applies a format suitable for the intended use (e.g., the layout of an in-house notice) to the generated material and finalizes the material.

[0288] Step 11:

[0289] The server sends the generated data to the terminal.

[0290] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0291] Step 12:

[0292] The device displays the document.

[0293] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[0294] (Example 2)

[0295] 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".

[0296] Conventional document generation systems have struggled to automatically generate documents based on specific tones and emotions desired by users. Furthermore, many systems have insufficient learning from past documents, requiring significant manual modifications to meet diverse user needs. Moreover, the tone and content of generated documents often do not align with user emotions, limiting their ability to accurately reflect user intent. This invention aims to solve these problems.

[0297] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past materials; means for recognizing the user's emotions using an emotion engine; means for training a generation AI model based on prompts and past materials; means for generating materials based on prompts and emphasis points; means for adjusting the content and tone of the generated materials based on the user's emotions; and means for providing the generated materials to the user. This makes it possible to automatically generate more accurate and intent-based materials based on the user's emotions and prompts.

[0298] A "user" is a person or organization that uses the system to provide data such as prompts, emphasis points, and usage purposes.

[0299] A "prompt" is a set of instructions regarding the subject or theme of the generated material that a user provides to the system.

[0300] "Emphasis points" are data that indicates elements or parts that the user wants to particularly emphasize in the document they are generating.

[0301] "Intended use" refers to information indicating the specific situations and purposes in which the generated materials will be used.

[0302] "Past documents" refer to text data such as documents and reports that the user has previously created or collected.

[0303] An "emotion engine" is a program that analyzes user input data and recognizes the emotions contained within it (e.g., urgency, importance, etc.).

[0304] A "generative AI model" is an artificial intelligence model trained using machine learning techniques to generate new information from user prompts and past data.

[0305] "Natural language processing" is a technical field for a computer to understand, analyze, and generate human language.

[0306] "Tone" refers to the style and emotional nuances of the generated document.

[0307] "Analysis" is a process of examining the content of input data and past documents in detail and extracting specific patterns and important information.

[0308] "Format" refers to the layout, structure, or visual style of a document.

[0309] "Secure protocol" is a communication standard for ensuring security when sending and receiving data, and generally includes TLS and HTTPS.

[0310] "Document generation" is a process of creating a new document based on user prompts and key points.

[0311] The system of the present invention automatically generates a document using a generative AI model based on prompts, key points, and usage purposes specified by the user, and further combines an emotion engine to recognize the user's emotion and adjust the content and tone of the document. A specific method for implementing this system will be described below.

[0312] First, the user uses a terminal to input prompts, key points, and usage purposes through an input form. As a specific example, the user inputs prompts and key points such as "Notice regarding next week's important meeting" and "Must attend, prior confirmation of documents". At this time, the emotion engine analyzes the user's input data in real time and recognizes emotions such as "urgency" and "importance". The user can also upload past documents. These documents include reports, manuals, and other relevant documents.

[0313] The device sends prompts, emphasis points, usage, and sentiment data received from the user to the server. This data is securely transmitted via a secure protocol such as HTTPS. In addition, past materials uploaded by the user are also sent from the device to the server.

[0314] The server uses natural language processing techniques and sentiment engines to analyze received prompts, sentiment data, and historical data. Specifically, it employs natural language processing libraries such as Python's nltk and spaCy, Google Cloud Natural Language API, and Microsoft® Azure® Text Analytics API. Using these techniques, the server extracts important patterns from historical data and trains a generative AI model. Libraries used for training include Scikit-learn, Keras, and TENSORFLOW®.

[0315] The server updates the generative AI model to reflect the learning results and generates new materials based on prompts and sentiment data received from the user. This generation process reflects the emphasis points specified by the user and creates materials in an appropriate format and tone. For example, based on the prompt "Announcement regarding an important meeting next week" and the emphasis point "Mandatory attendance, please review the materials in advance," it will create materials such as "There is an important meeting next week. Attendance is mandatory for everyone, please review the materials in advance."

[0316] The generated document is sent from the server to the terminal. This transmission also uses a secure protocol. Finally, the terminal displays the generated document to the user. The user can review the displayed document and make corrections or regenerate it as needed.

[0317] This system allows users to efficiently generate documents that accurately reflect their emotions and intentions.

[0318] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0319] Step 1:

[0320] The user uses their device to enter prompts, emphasis points, and intended use into an input form. This form contains text fields where the user enters specific sentences, such as "Notice regarding next week's important meeting." The entered data includes prompts (subject or theme), emphasis points (information of particular importance), and intended use. The entered data is temporarily stored in the device's local storage.

[0321] Step 2:

[0322] While the user is entering information, the emotion engine analyzes it in real time. The emotion engine receives the input data and extracts emotions such as "urgency" and "importance" from it. Specifically, it uses natural language processing technology to tokenize the text data and applies an emotion analysis algorithm. As a result, the emotion engine can perceive "urgency" from the context of, for example, "an important meeting next week."

[0323] Step 3:

[0324] The device sends prompts, emphasis points, usage information, and recognized sentiment data obtained from the user to the server. Data transmission is performed using a secure protocol such as HTTPS. Specifically, the input data and sentiment data are packaged in JSON format and a POST request is sent to the server's API endpoint. This allows the server to receive the necessary data.

[0325] Step 4:

[0326] The user uploads past documents to the device. The document upload is done via a file selection dialog, where the user selects a file from local storage and clicks the upload button. The device reads this file and sends a POST request to the server's API endpoint as binary data in order to send it to the server.

[0327] Step 5:

[0328] The device sends previously uploaded data to the server. This is also done via a secure protocol such as HTTPS. A POST request containing file data and metadata is sent to the server, and the data is stored in the server's storage.

[0329] Step 6:

[0330] The server analyzes received historical data using natural language processing techniques. Specifically, it uses libraries such as Python's nltk and spaCy to tokenize the text data of the materials and calculate the frequency of important keywords and phrases. This analysis process grasps the content and format of the materials and extracts important patterns.

[0331] Step 7:

[0332] The server trains a generative AI model based on patterns and content obtained from the analyzed data. This training process utilizes machine learning libraries such as Scikit-learn, Keras, and TensorFlow. The server encodes features from past data and uses this as the model's training dataset. The model is then updated to apply newly learned knowledge.

[0333] Step 8:

[0334] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords using tools such as KeyBERT and TF-IDF and sets guidelines for generating materials. The server understands the context and topic of the received prompts and determines the tone and focus points of the content based on the user's sentiment.

[0335] Step 9:

[0336] The server generates materials using an updated generative AI model. Natural language generation technology is used in the process of automatically generating specific content based on prompts and sentiment data. For example, when creating a "Notice Regarding an Important Meeting Next Week," the system generates material content that reflects "urgency." This generation process automatically creates materials such as:

[0337] Subject: Notice of a very important meeting

[0338] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[0339] Step 10:

[0340] The server applies a format suitable for the intended use to the generated document. Specifically, it converts it to Markdown or HTML format and applies a specific layout template. It adjusts the appearance and structure of the document to match the user's requirements.

[0341] Step 11:

[0342] The server sends the generated data to the terminal. This transmission is also performed using a secure protocol. To ensure data consistency and security, the generated data is sent in an appropriate encoding format.

[0343] Step 12:

[0344] The device displays the generated document to the user. The user can review the displayed document and modify or regenerate it as needed. The document is displayed on a web browser or mobile app, making it easy for the user to access and interact with.

[0345] (Application Example 2)

[0346] 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 device 14 will be referred to as the "terminal."

[0347] Currently, content distribution services require users to efficiently generate large-scale and diverse content, but doing so manually is time-consuming and labor-intensive. Furthermore, generated content may not align with users' emotions or intentions, leading to decreased user satisfaction and engagement. Additionally, effectively reusing past content and continuously providing high-quality content is difficult.

[0348] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and usage purposes from the user, means for uploading and analyzing past data, and means for training an AI model based on the prompts and past data. This makes it possible to quickly and effectively generate content based on user input and adjust its content and tone to match the user's emotions. Furthermore, it becomes possible to effectively reuse past data and continuously provide high-quality content.

[0349] A "prompt" is a specific input statement used to provide instructions or topics to a system.

[0350] "Emphasis points" are input fields used to indicate content or elements that the user particularly values.

[0351] "Usage" is an input field that specifies the purpose and context in which the generated information should be used.

[0352] "Past data" refers to content or information that was previously created, used, or stored.

[0353] "Means of analysis" refers to the technology or device used by a system to understand and break down input data and extract necessary patterns or information.

[0354] An "AI model" is an algorithm or system that uses artificial intelligence technology to analyze data, learn patterns, and generate new data.

[0355] An "emotion recognition engine" is a technology or system that analyzes and detects a user's emotions from input data.

[0356] "Tone" refers to an element that indicates the emotional style and atmosphere of the generated content.

[0357] "Means of generating information" refers to technologies and devices that create new data and content based on user input or learned models.

[0358] "Means of provision" refers to technologies and devices for transmitting and displaying generated information to the user.

[0359] This invention relates to a system for content distribution services that automatically generates high-quality content using AI based on user-provided prompts, emphasis points, and intended use. Furthermore, by combining it with an emotion recognition engine, it is possible to analyze the user's emotions and adjust the tone of the content accordingly.

[0360] System Overview

[0361] 1. Obtaining user input

[0362] Users input prompts, emphasis points, and usage purposes using a smartphone application.

[0363] For example, enter "Features of the new product" as the theme for next week's blog post in the prompt, and specify "SEO optimization, improved user engagement" as the emphasis point.

[0364] 2. Recognition of emotions

[0365] While the user is inputting data, an emotion recognition engine (e.g., emotion analysis technology) is used to analyze the collected data and recognize the user's emotions.

[0366] For example, it can detect a "positive and excited tone" from the user's input.

[0367] 3. Uploading past data

[0368] Users upload data such as blog posts and email newsletters they have created in the past.

[0369] The server analyzes this historical data using natural language processing techniques and extracts patterns.

[0370] 4. Training the AI ​​model

[0371] The server trains an AI model based on the patterns and content it has analyzed.

[0372] For example, a model is trained to generate new content from an SEO optimization perspective, based on patterns extracted from past blog posts.

[0373] 5. Content Generation

[0374] Based on prompts, emphasis points, and sentiment data obtained from users, an AI model is used to generate content.

[0375] For example, generate a blog post for next week with information about the "features of the new product" in a positive tone.

[0376] 6. Tone adjustment

[0377] The generated content is tone-adjusted based on the user's emotions by an emotion recognition engine.

[0378] For example, positive and exciting phrases such as, "Let us tell you about the unique features of our new product!" are used.

[0379] 7. Content provision and verification

[0380] The generated content is delivered to the user's smartphone, allowing the user to review and modify the content.

[0381] Hardware and software to be used

[0382] Hardware: User's smartphone device, server

[0383] Software: Emotion recognition engine (e.g., emotion analysis technology), natural language processing technology (e.g., Transformers library), AI model (e.g., GPT-3)

[0384] Specific example

[0385] Example of a prompt:

[0386] Prompt: "Theme for next week's blog post - Features of the new product"

[0387] Key points: "SEO optimization, improved user engagement"

[0388] Emotion: "Positive and excited tone"

[0389] Thus, the present invention can improve user satisfaction and engagement by automatically and efficiently generating high-quality content based on user input and adjusting it based on user emotions. Furthermore, by effectively reusing past data, it is possible to continuously provide high-quality content.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The user uses a smartphone application to input prompts, emphasis points, and usage purposes. An example of an input prompt is "Theme for next week's blog post - New product features." The emphasis points are specified as "SEO optimization, improved user engagement." This input data becomes the base data for the next processing step.

[0393] Step 2:

[0394] The device sends the input prompt, emphasis points, and intended use to the emotion recognition engine. The emotion recognition engine uses emotion analysis techniques such as Natural Language Toolkit (NLTK) or TextBlob to analyze the user's emotion (e.g., positive, negative, neutral) from the input. The analysis result detects a "positive and excited tone."

[0395] Step 3:

[0396] Users upload previously created content (blog posts, newsletters, etc.) to the system. This past content data is sent to the server and stored in a database. This data serves as the basis for the next analysis step.

[0397] Step 4:

[0398] The server analyzes previously uploaded content. Using natural language processing techniques (e.g., the Transformers library), it extracts keywords and patterns from past content. For example, it identifies patterns related to "SEO optimization" and sentence structures related to "improving user engagement." This analysis result serves as training material for the AI ​​model.

[0399] Step 5:

[0400] The server trains an AI model based on the analysis results. A generative AI model, such as GPT-3, learns to generate new content based on the extracted patterns and content. Through this learning process, the model acquires parameters that will be used in the next content generation step.

[0401] Step 6:

[0402] Based on prompts, emphasis points, and sentiment data obtained from the user, the server uses a trained AI model to generate content. The generated content is about "Next Week's Blog Post - New Product Features" and is presented in a "positive and exciting tone." This content is then sent to the next refinement step.

[0403] Step 7:

[0404] The server adjusts the tone of the generated content. It is further refined by an emotion recognition engine, which makes fine adjustments to the tone if necessary. As a result, content that is more faithful to the user's emotions is generated. For example, more positive and engaging expressions are used, such as "Let us tell you about the unique features of our new product!"

[0405] Step 8:

[0406] The generated content is sent to the user's smartphone. The user reviews the generated content through the application and makes corrections as needed. The corrected content is then sent back to the system for final adjustments. In this way, high-quality content that matches the user's intentions and emotions is completed.

[0407] Through the above processing steps, the system of the present invention enables users to efficiently generate high-quality content and adjust the tone based on the user's emotions.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Second Embodiment]

[0412] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] The system of this invention automatically generates materials using AI based on prompts and emphasis points specified by the user. The specific implementation of this system is described below.

[0425] System Overview

[0426] The user accesses the system using a terminal. The terminal provides input forms for entering prompts, highlights, and usage details. The user uploads past materials, and the system initiates a process of generating new materials based on these uploads.

[0427] Program processing (explained in natural language)

[0428] 1. Obtaining user input

[0429] The terminal accepts input from the user. The user enters prompts, emphasis points, and intended use for generating the document into the terminal. For example, if the user wants to create a notice about an important meeting next week, they would enter that into the terminal.

[0430] 2. Sending input data

[0431] The terminal sends the data entered by the user to the server. This data serves as the basis for later document generation.

[0432] 3. Upload data

[0433] Users upload past documents to the system via their terminals. These documents include past reports, manuals, and other related documents.

[0434] 4. Learning Process

[0435] The server analyzes the uploaded materials and extracts patterns in format and content using natural language processing techniques. This allows the AI ​​model to learn patterns from past materials.

[0436] 5. Prompt Analysis

[0437] The server analyzes prompts and highlights sent by the user. In doing so, it also considers the intended use and determines the structure of the document based on those guidelines.

[0438] 6. Data generation

[0439] The server generates materials using a trained AI model. Specifically, it automatically generates the content of new materials based on user prompts and past materials. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a material like the following:

[0440] Subject: Notice regarding next week's important meeting

[0441] Content: A meeting (important) will be held next week. Please note that attendance is mandatory. Also, please review the materials in advance of the meeting.

[0442] 7. Application of the format

[0443] The server applies the appropriate format to the generated documents. Depending on the intended use, it prepares formats for internal announcements, external manuals, and other documents.

[0444] 8. Providing the results

[0445] Once the generated document is complete, the server sends it to the terminal for the user to review. The user can review the document and make corrections or regenerate it as needed.

[0446] This system allows users to efficiently generate high-quality documents with minimal effort. For example, using this system when creating internal announcements or external manuals can save time and effort while maintaining consistency and quality.

[0447] The above describes a specific embodiment for implementing the system of the present invention. This system aims to generate efficient and high-quality materials based on user input and utilizing AI technology.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] The user opens the input form.

[0451] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[0452] Step 2:

[0453] The user enters the required information.

[0454] The user enters a prompt (e.g., "Notice regarding an important meeting next week"), a key point (e.g., "Mandatory attendance, please review materials in advance"), and a purpose (e.g., "Internal announcement").

[0455] Step 3:

[0456] The terminal sends the input data to the server.

[0457] The terminal sends user-entered prompts, highlights, and usage data to the server. The data is transmitted using a secure communication protocol.

[0458] Step 4:

[0459] Users upload past documents.

[0460] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[0461] Step 5:

[0462] The device sends past data to the server.

[0463] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[0464] Step 6:

[0465] The server analyzes past data.

[0466] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[0467] Step 7:

[0468] The server trains the AI ​​model.

[0469] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0470] Step 8:

[0471] The server parses the prompt.

[0472] The server analyzes prompts and emphasis points sent by the user. It extracts keywords and context, and determines guidelines for creating the framework of the document.

[0473] Step 9:

[0474] The server generates the contents of the document.

[0475] The server uses a trained AI model to generate information based on prompts and past data. It generates specific content that reflects the information entered by the user.

[0476] Step 10:

[0477] The server formats the documents.

[0478] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0479] Step 11:

[0480] The server sends the generated data to the terminal.

[0481] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0482] Step 12:

[0483] The device displays the document.

[0484] The terminal displays the materials generated for each user. Users can review the materials and modify or regenerate them as needed.

[0485] (Example 1)

[0486] 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".

[0487] Conventional information generation systems require users to spend a significant amount of time and effort creating new documents, and in particular, it is difficult to generate consistent content that reflects past documents. Furthermore, standardizing the format and content of documents is also difficult, resulting in a user-unfriendly environment. This invention aims to solve these problems and provide a system that automatically generates efficient and high-quality documents.

[0488] 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.

[0489] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past documents; and means for training a generation AI model based on the prompts and past documents. This makes it possible to efficiently generate new documents based on prompts entered by the user and past documents. Furthermore, it is possible to maintain the consistency and quality of the generated documents.

[0490] A "prompt" is a set of instructions entered by the user to specify the content and purpose of the document they are generating.

[0491] "Emphasis points" are instructions that allow users to specify the parts or content they consider particularly important when creating a document.

[0492] "Intended use" refers to the context and purpose for which the generated materials will be used.

[0493] "Documents" refer to information including reports, manuals, and other related materials created in the past.

[0494] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate text data.

[0495] Natural Language Processing (NLP) is a technology that enables computers to understand and process human language.

[0496] A "pattern" refers to the regularity or similarity of structure and expression found in past documents.

[0497] A "server" is a centralized computing device that processes data sent by users and generates materials using generative AI models.

[0498] The system of the present invention automatically generates materials using a generative AI model based on prompts and emphasis points specified by the user. A specific embodiment of this system is described below.

[0499] First, a server, terminals, and a generative AI model are necessary to support the system's operation. The server can be a cloud server (e.g., Amazon Web Services, Google Cloud Platform). The terminals are devices that provide a user interface, such as PCs, smartphones, and tablets. For the generative AI model, natural language processing technologies such as GPT-3 and BERT are used.

[0500] Users access the system using a terminal. The terminal provides input forms for users to enter prompts, highlights, and intended use for generating documents. Users upload past documents, and the system initiates the process of generating documents based on these. For example, if a user wants to create an "Announcement Regarding an Important Meeting Next Week," they would enter this into the terminal. The terminal sends the data entered by the user to the server. This data serves as the basis for subsequent document generation.

[0501] Next, the user uploads existing documents, including past reports, manuals, and other relevant documents. The server analyzes the uploaded documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn patterns from past documents.

[0502] The server analyzes prompts and highlights sent by the user, and determines the structure of the document, taking into account its intended use. It then automatically generates new documents using a generative AI model. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a document like this:

[0503] Subject: Notice regarding next week's important meeting

[0504] Content: An important meeting will be held next week. Please note that attendance is mandatory. Also, please review the details before the meeting.

[0505] The server applies the appropriate format to the generated documents. Specifically, it formats internal announcements and external manuals according to their intended use. The completed documents are sent from the server to the terminal, where the user can review them. The user can review the documents and make corrections or regenerate them as needed.

[0506] As described above, the system of the present invention allows users to efficiently generate high-quality materials without requiring much effort. For example, by using this system when creating internal announcements or external manuals, it is possible to save time and effort while maintaining consistency and quality of the materials.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Obtaining user input

[0510] Input: The user uses the terminal to enter prompts, emphasis points, and usage details.

[0511] Specific operation: The terminal provides an input form and accepts the content specified by the user. For example, the user enters a prompt such as "Notice regarding an important meeting next week" and an emphasis such as "Mandatory attendance."

[0512] Output: The user data entered into the terminal is prepared.

[0513] Step 2:

[0514] Sending input data

[0515] Input: Data entered by the user into the device.

[0516] Specific operation: The terminal sends the entered prompts, highlights, and usage information to the server. The data is securely transferred over the network.

[0517] Output: The input data is sent to the server and used for subsequent processing.

[0518] Step 3:

[0519] Uploading past documents

[0520] Input: Past document files (PDF, text files, etc.) uploaded by the user.

[0521] Specific operation: The user uploads past reports and manuals to the server via a terminal. The documents are saved in the appropriate format.

[0522] Output: Uploaded documents are stored on the server and used for later analysis.

[0523] Step 4:

[0524] Learning process

[0525] Input: Uploaded past documents.

[0526] Specific operation: The server analyzes uploaded documents using natural language processing techniques. Specifically, it uses Python's NLTK library and spaCy to tokenize the text within the documents and extract patterns and formats. A generative AI model (such as GPT-3 or BERT) is then trained based on the extracted patterns.

[0527] Output: A trained generative AI model is generated, and the process proceeds to the next step.

[0528] Step 5:

[0529] Prompt analysis

[0530] Input: Prompts and emphasis points sent by the user.

[0531] Specific operation: The server analyzes prompts and highlights to determine the structure and organization of the document. The intended use is also considered. Natural language processing technology is used in this process, including keyword extraction and semantic analysis.

[0532] Output: Analysis data based on prompts and highlight points is generated.

[0533] Step 6:

[0534] Data generation

[0535] Input: A pre-trained generative AI model and analyzed prompt data.

[0536] Specific operation: The server generates documents using a trained generative AI model. Based on prompts and patterns derived from past documents, it automatically constructs the content of new documents. For example, it generates a document titled "Announcement regarding next week's important meeting."

[0537] Output: Automatically generated documents will be produced.

[0538] Step 7:

[0539] Application of format

[0540] Input: Automated document.

[0541] Specific operation: The server applies the appropriate format to the generated document. It adjusts fonts, layout, headers, footers, etc., according to the intended use.

[0542] Output: A completed document with the appropriate formatting applied will be generated.

[0543] Step 8:

[0544] Providing results

[0545] Input: Completed document.

[0546] Specific operation: The server sends the generated document to the terminal. The user can review the document through the terminal and make corrections or provide feedback as needed.

[0547] Output: The completed document will be provided in a format that the user can review.

[0548] (Application Example 1)

[0549] 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."

[0550] Traditional document generation systems struggled to maintain consistency in work manuals and quality control reports generated by factory workers, requiring significant time and effort. Furthermore, errors and inconsistencies in the generated documents could reduce on-site work efficiency and potentially impact safety. A solution to this problem is needed.

[0551] 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.

[0552] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past data; means for training an AI model based on the prompts and past data; means for generating data based on the prompts and emphasis points; means for applying a specified format to the generated data; and means for checking and correcting the generated data on the operating terminal of the machine. This makes it possible to quickly generate high-quality data while saving effort and time, while maintaining consistency in factory work manuals and quality control reports.

[0553] A "prompt" refers to the instructions or initial information that a user enters into the system.

[0554] "Emphasis points" refer to the parts or content in the generated document that should be given particular importance.

[0555] "Intended use" refers to the specific situations and purposes in which the generated materials will be used.

[0556] "Past documents" refer to documents and data created previously, and are used as reference material for generating new documents.

[0557] An "AI model" refers to a learning model that automatically generates the content and format of materials based on artificial intelligence technology.

[0558] "Natural language processing" is a technology that uses computers to process and understand human language.

[0559] "Mechanical equipment" refers to devices such as work terminals and robots used within a factory.

[0560] An "operating terminal" is an electronic device used by a user to access, operate, and verify a system.

[0561] "Specified format" refers to the predetermined document format or style to which the generated material will be applied.

[0562] "Review and correction" means that the user checks the generated document and corrects its accuracy and suitability as needed.

[0563] The system of the present invention automatically generates materials using AI based on prompts and emphasis points specified by the user. Specific embodiments of the system are described below.

[0564] Hardware and software to be used

[0565] 1. Hardware:

[0566] Operating terminal: A console terminal within the factory, or a device operated directly by the user.

[0567] Machinery and equipment: Dedicated terminals and robots for displaying documents within the factory.

[0568] 2. Software:

[0569] The overall programming language used is Python.

[0570] SpaCy and NLTK are used for natural language processing.

[0571] OpenAI's GPT-3 API is used for automatic document generation.

[0572] The server has the following functions:

[0573] Detailed processing

[0574] 1. Prompt input form:

[0575] The user inputs prompts and key points for the document they want to generate via the operating terminal. For example, if the user wants to create a document summarizing important points regarding the next scheduled maintenance, they would input that information.

[0576] 2. Uploading past documents:

[0577] Users upload past work manuals and quality control reports to the server via their operating terminals. The uploaded materials are used as training material for the AI ​​model.

[0578] 3. Analysis Process:

[0579] The server analyzes the uploaded historical documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn these patterns from past documents.

[0580] 4. Document generation process:

[0581] The server analyzes user prompts and emphasis points, and determines the structure of the document based on those guidelines. Using GPT-3, it generates new documents based on past documents and user input. For example, it might create a document summarizing important points regarding the next scheduled maintenance work, or a document specifically mentioning enhanced safety measures.

[0582] 5. Apply format:

[0583] The server applies a specified format within the factory to the generated documents. For example, it formats them to internal memos or quality control reports.

[0584] 6. Review and Correction:

[0585] The user reviews the generated documents on the operating terminal and corrects the accuracy and suitability of the content as needed.

[0586] Specific example

[0587] Example of a prompt:

[0588] 1. Prompt: Document summarizing important points regarding the next scheduled maintenance work.

[0589] 2. Emphasis: Please specifically mention the strengthened safety measures.

[0590] This allows for significant savings in labor and time while maintaining consistency in work manuals and quality control reports in factory operations. It also contributes to improved safety and operational efficiency.

[0591] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0592] Step 1:

[0593] The user uses a terminal to input prompts, emphasis points, and intended use of the document they wish to generate into an input form. This input form accepts prompts and emphasis points, and the user's input is saved as data on the terminal.

[0594] Step 2:

[0595] The terminal sends the entered prompts, highlights, and usage data to the server. This transmitted data serves as a guide for generating the document.

[0596] Step 3:

[0597] Users upload past documents (e.g., work manuals, quality control reports, etc.) to the system via an operating terminal. The uploaded documents are stored on the server.

[0598] Step 4:

[0599] The server analyzes the uploaded historical data. Using natural language processing techniques, it extracts patterns in the format and content of the uploaded data. Specifically, it uses SpaCy and NLTK to decompose and analyze the text, and applies important patterns and structures as data to a learning model.

[0600] Step 5:

[0601] The server analyzes prompts and highlights, and sets the framework of the document by comparing them with patterns extracted from past materials. In this process, an AI model (such as GPT-3) is used to perform data calculations based on user prompts, and initial data for the new document content is generated.

[0602] Step 6:

[0603] The server then applies patterns from past documents to the initial generated documents, smoothly formatting them. For example, it processes them into internal memo formats or quality report formats. During this process, the documents are restructured according to the specified format.

[0604] Step 7:

[0605] The server sends the final generated document to the terminal. The terminal displays the received document to the user, who then reviews the content and makes corrections as needed. At this stage, the data corrected by the user is fed back into the system, improving the quality of the document.

[0606] 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.

[0607] The system of the present invention automatically generates materials using AI based on prompts, emphasis points, and intended use specified by the user, and further incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials. The specific implementation of this system is described below.

[0608] System Overview

[0609] The user uses a terminal to input prompts, emphasis points, and intended use through an input form. During this process, an emotion engine recognizes the user's emotions from the input data and adjusts the content and tone of the material based on the results. The user can also upload past materials, which initiates a process where the system generates new materials.

[0610] Program processing (explained in natural language)

[0611] 1. Obtaining user input

[0612] The terminal provides a form for the user to input prompts, emphasis points, and usage purposes. For example, the user might enter "Notice regarding an important meeting next week."

[0613] 2. Recognition of emotions

[0614] While the user enters information, the emotion engine analyzes the input data and recognizes the user's emotions. For example, it can sense "urgency" or "importance" from the user's input.

[0615] 3. Sending input data

[0616] The device sends data regarding prompts, emphasis points, usage, and sentiment to the server. This data forms the basis for generating the document.

[0617] 4. Upload data

[0618] Users upload past documents to their devices. These documents include past reports, manuals, and other related materials.

[0619] 5. Sending past documents

[0620] The device sends previously uploaded data to the server.

[0621] 6. Analysis of historical documents

[0622] The server analyzes the received historical document data using natural language processing techniques to extract the content, format, and important patterns of the documents.

[0623] 7. Training the AI ​​model

[0624] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0625] 8. Analysis of prompts and emotions

[0626] The server analyzes user prompts, emphasis points, and sentiment data. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[0627] 9. Data generation

[0628] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, when creating a "Notice Regarding an Important Meeting Next Week," if the emotion engine detects "urgency," it will generate material such as:

[0629] Subject: Notice of a very important meeting

[0630] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[0631] 10. Formatting of documents

[0632] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0633] 11. Sending of generated materials

[0634] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0635] 12. Display of materials

[0636] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[0637] Specific example

[0638] Example 1: Creating an internal company announcement

[0639] The user enters "Notice regarding an important meeting next week" and adds "Mandatory attendance, please review materials in advance" as a point of emphasis. If the emotion engine detects urgency, the system adjusts the tone of the material and generates a notice that includes the emphasized content.

[0640] Example 2: Creating a manual for external use

[0641] The user enters "How to use the new product" and "Safety precautions" as a point of emphasis. If the sentiment engine detects that a "user-friendly" tone is desired, the system adjusts the content of the document to make it more approachable and generates the final document.

[0642] In this way, the present invention can efficiently generate materials that reflect the user's emotions and satisfy the user's requirements.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user opens the input form.

[0646] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[0647] Step 2:

[0648] The user enters the required information.

[0649] The user enters prompts (e.g., "Notice regarding an important meeting next week"), emphasis points (e.g., "Mandatory attendance, please review materials in advance"), and intended use (e.g., "Internal announcement"). As the user enters the information, the sentiment engine analyzes it and recognizes the user's emotions.

[0650] Step 3:

[0651] The terminal sends the input data to the server.

[0652] The device sends user-entered prompts, emphasis points, usage, and sentiment data to the server. The data is transmitted using a secure communication protocol.

[0653] Step 4:

[0654] Users upload past documents.

[0655] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[0656] Step 5:

[0657] The device sends past data to the server.

[0658] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[0659] Step 6:

[0660] The server analyzes past data.

[0661] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[0662] Step 7:

[0663] The server trains the AI ​​model.

[0664] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0665] Step 8:

[0666] The server analyzes the prompt and sentiment.

[0667] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[0668] Step 9:

[0669] The server generates the contents of the document.

[0670] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the emotion engine, sensing "urgency," will generate material like this:

[0671] Subject: Notice of a very important meeting

[0672] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[0673] Step 10:

[0674] The server formats the documents.

[0675] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0676] Step 11:

[0677] The server sends the generated data to the terminal.

[0678] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0679] Step 12:

[0680] The device displays the document.

[0681] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[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 smart glasses 214 will be referred to as the "terminal".

[0684] Conventional document generation systems have struggled to automatically generate documents based on specific tones and emotions desired by users. Furthermore, many systems have insufficient learning from past documents, requiring significant manual modifications to meet diverse user needs. Moreover, the tone and content of generated documents often do not align with user emotions, limiting their ability to accurately reflect user intent. This invention aims to solve these problems.

[0685] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past materials; means for recognizing the user's emotions using an emotion engine; means for training a generation AI model based on prompts and past materials; means for generating materials based on prompts and emphasis points; means for adjusting the content and tone of the generated materials based on the user's emotions; and means for providing the generated materials to the user. This makes it possible to automatically generate more accurate and intent-based materials based on the user's emotions and prompts.

[0686] A "user" is a person or organization that uses the system to provide data such as prompts, emphasis points, and usage purposes.

[0687] A "prompt" is a set of instructions regarding the subject or theme of the generated material that a user provides to the system.

[0688] "Emphasis points" are data that indicates elements or parts that the user wants to particularly emphasize in the document they are generating.

[0689] "Intended use" refers to information indicating the specific situations and purposes in which the generated materials will be used.

[0690] "Past documents" refer to text data such as documents and reports that the user has previously created or collected.

[0691] An "emotion engine" is a program that analyzes user input data and recognizes the emotions contained within it (e.g., urgency, importance, etc.).

[0692] A "generative AI model" is an artificial intelligence model trained using machine learning techniques to generate new information from user prompts and past data.

[0693] "Natural language processing" is a field of technology that uses computers to understand, analyze, and generate human language.

[0694] "Tone" refers to the writing style and emotional nuances of the generated material.

[0695] "Analysis" is the process of examining input data and past documents in detail to extract specific patterns and important information.

[0696] "Format" refers to the layout, structure, or visual style of a document.

[0697] A "secure protocol" is a communication standard designed to ensure the security of data transmission and reception, and common examples include TLS and HTTPS.

[0698] "Document generation" is the process of creating a new document based on user prompts and emphasis points.

[0699] The system of the present invention automatically generates materials using a generative AI model based on user-specified prompts, emphasis points, and intended use. Furthermore, it incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials accordingly. The specific method for implementing this system is described below.

[0700] First, the user uses a terminal to input prompts, emphasis points, and intended use through an input form. For example, the user might input prompts and emphasis points such as "Notification regarding next week's important meeting" or "Mandatory attendance, pre-review of materials." During this process, the sentiment engine analyzes the user's input data in real time, recognizing emotions such as "urgency" and "importance." The user can also upload past documents, including reports, manuals, and other relevant materials.

[0701] The device sends prompts, emphasis points, usage, and sentiment data received from the user to the server. This data is securely transmitted via a secure protocol such as HTTPS. In addition, past materials uploaded by the user are also sent from the device to the server.

[0702] The server uses natural language processing techniques and sentiment engines to analyze received prompts, sentiment data, and historical data. Specifically, it employs natural language processing libraries such as Python's nltk and spaCy, Google Cloud Natural Language API, and Microsoft Azure Text Analytics API. Using these techniques, the server extracts important patterns from historical data and trains a generative AI model. Libraries used for training include Scikit-learn, Keras, and TensorFlow.

[0703] The server updates the generative AI model to reflect the learning results and generates new materials based on prompts and sentiment data received from the user. This generation process reflects the emphasis points specified by the user and creates materials in an appropriate format and tone. For example, based on the prompt "Announcement regarding an important meeting next week" and the emphasis point "Mandatory attendance, please review the materials in advance," it will create materials such as "There is an important meeting next week. Attendance is mandatory for everyone, please review the materials in advance."

[0704] The generated document is sent from the server to the terminal. This transmission also uses a secure protocol. Finally, the terminal displays the generated document to the user. The user can review the displayed document and make corrections or regenerate it as needed.

[0705] This system allows users to efficiently generate documents that accurately reflect their emotions and intentions.

[0706] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0707] Step 1:

[0708] The user uses their device to enter prompts, emphasis points, and intended use into an input form. This form contains text fields where the user enters specific sentences, such as "Notice regarding next week's important meeting." The entered data includes prompts (subject or theme), emphasis points (information of particular importance), and intended use. The entered data is temporarily stored in the device's local storage.

[0709] Step 2:

[0710] While the user is entering information, the emotion engine analyzes it in real time. The emotion engine receives the input data and extracts emotions such as "urgency" and "importance" from it. Specifically, it uses natural language processing technology to tokenize the text data and applies an emotion analysis algorithm. As a result, the emotion engine can perceive "urgency" from the context of, for example, "an important meeting next week."

[0711] Step 3:

[0712] The device sends prompts, emphasis points, usage information, and recognized sentiment data obtained from the user to the server. Data transmission is performed using a secure protocol such as HTTPS. Specifically, the input data and sentiment data are packaged in JSON format and a POST request is sent to the server's API endpoint. This allows the server to receive the necessary data.

[0713] Step 4:

[0714] The user uploads past documents to the device. The document upload is done via a file selection dialog, where the user selects a file from local storage and clicks the upload button. The device reads this file and sends a POST request to the server's API endpoint as binary data in order to send it to the server.

[0715] Step 5:

[0716] The device sends previously uploaded data to the server. This is also done via a secure protocol such as HTTPS. A POST request containing file data and metadata is sent to the server, and the data is stored in the server's storage.

[0717] Step 6:

[0718] The server analyzes received historical data using natural language processing techniques. Specifically, it uses libraries such as Python's nltk and spaCy to tokenize the text data of the materials and calculate the frequency of important keywords and phrases. This analysis process grasps the content and format of the materials and extracts important patterns.

[0719] Step 7:

[0720] The server trains a generative AI model based on patterns and content obtained from the analyzed data. This training process utilizes machine learning libraries such as Scikit-learn, Keras, and TensorFlow. The server encodes features from past data and uses this as the model's training dataset. The model is then updated to apply newly learned knowledge.

[0721] Step 8:

[0722] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords using tools such as KeyBERT and TF-IDF and sets guidelines for generating materials. The server understands the context and topic of the received prompts and determines the tone and focus points of the content based on the user's sentiment.

[0723] Step 9:

[0724] The server generates materials using an updated generative AI model. Natural language generation technology is used in the process of automatically generating specific content based on prompts and sentiment data. For example, when creating a "Notice Regarding an Important Meeting Next Week," the system generates material content that reflects "urgency." This generation process automatically creates materials such as:

[0725] Subject: Notice of a very important meeting

[0726] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[0727] Step 10:

[0728] The server applies a format suitable for the intended use to the generated document. Specifically, it converts it to Markdown or HTML format and applies a specific layout template. It adjusts the appearance and structure of the document to match the user's requirements.

[0729] Step 11:

[0730] The server sends the generated data to the terminal. This transmission is also performed using a secure protocol. To ensure data consistency and security, the generated data is sent in an appropriate encoding format.

[0731] Step 12:

[0732] The device displays the generated document to the user. The user can review the displayed document and modify or regenerate it as needed. The document is displayed on a web browser or mobile app, making it easy for the user to access and interact with.

[0733] (Application Example 2)

[0734] 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 will be referred to as the "terminal."

[0735] Currently, content distribution services require users to efficiently generate large-scale and diverse content, but doing so manually is time-consuming and labor-intensive. Furthermore, generated content may not align with users' emotions or intentions, leading to decreased user satisfaction and engagement. Additionally, effectively reusing past content and continuously providing high-quality content is difficult.

[0736] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and usage purposes from the user, means for uploading and analyzing past data, and means for training an AI model based on the prompts and past data. This makes it possible to quickly and effectively generate content based on user input and adjust its content and tone to match the user's emotions. Furthermore, it becomes possible to effectively reuse past data and continuously provide high-quality content.

[0737] A "prompt" is a specific input statement used to provide instructions or topics to a system.

[0738] "Emphasis points" are input fields used to indicate content or elements that the user particularly values.

[0739] "Usage" is an input field that specifies the purpose and context in which the generated information should be used.

[0740] "Past data" refers to content or information that was previously created, used, or stored.

[0741] "Means of analysis" refers to the technology or device used by a system to understand and break down input data and extract necessary patterns or information.

[0742] An "AI model" is an algorithm or system that uses artificial intelligence technology to analyze data, learn patterns, and generate new data.

[0743] An "emotion recognition engine" is a technology or system that analyzes and detects a user's emotions from input data.

[0744] "Tone" refers to an element that indicates the emotional style and atmosphere of the generated content.

[0745] "Means of generating information" refers to technologies and devices that create new data and content based on user input or learned models.

[0746] "Means of provision" refers to technologies and devices for transmitting and displaying generated information to the user.

[0747] This invention relates to a system for content distribution services that automatically generates high-quality content using AI based on user-provided prompts, emphasis points, and intended use. Furthermore, by combining it with an emotion recognition engine, it is possible to analyze the user's emotions and adjust the tone of the content accordingly.

[0748] System Overview

[0749] 1. Obtaining user input

[0750] Users input prompts, emphasis points, and usage purposes using a smartphone application.

[0751] For example, enter "Features of the new product" as the theme for next week's blog post in the prompt, and specify "SEO optimization, improved user engagement" as the emphasis point.

[0752] 2. Recognition of emotions

[0753] While the user is inputting data, an emotion recognition engine (e.g., emotion analysis technology) is used to analyze the collected data and recognize the user's emotions.

[0754] For example, it can detect a "positive and excited tone" from the user's input.

[0755] 3. Uploading past data

[0756] Users upload data such as blog posts and email newsletters they have created in the past.

[0757] The server analyzes this historical data using natural language processing techniques and extracts patterns.

[0758] 4. Training the AI ​​model

[0759] The server trains an AI model based on the patterns and content it has analyzed.

[0760] For example, a model is trained to generate new content from an SEO optimization perspective, based on patterns extracted from past blog posts.

[0761] 5. Content Generation

[0762] Based on prompts, emphasis points, and sentiment data obtained from users, an AI model is used to generate content.

[0763] For example, generate a blog post for next week with information about the "features of the new product" in a positive tone.

[0764] 6. Tone adjustment

[0765] The generated content is tone-adjusted based on the user's emotions by an emotion recognition engine.

[0766] For example, positive and exciting phrases such as, "Let us tell you about the unique features of our new product!" are used.

[0767] 7. Content provision and verification

[0768] The generated content is delivered to the user's smartphone, allowing the user to review and modify the content.

[0769] Hardware and software to be used

[0770] Hardware: User's smartphone device, server

[0771] Software: Emotion recognition engine (e.g., emotion analysis technology), natural language processing technology (e.g., Transformers library), AI model (e.g., GPT-3)

[0772] Specific example

[0773] Example of a prompt:

[0774] Prompt: "Theme for next week's blog post - Features of the new product"

[0775] Key points: "SEO optimization, improved user engagement"

[0776] Emotion: "Positive and excited tone"

[0777] Thus, the present invention can improve user satisfaction and engagement by automatically and efficiently generating high-quality content based on user input and adjusting it based on user emotions. Furthermore, by effectively reusing past data, it is possible to continuously provide high-quality content.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user uses a smartphone application to input prompts, emphasis points, and usage purposes. An example of an input prompt is "Theme for next week's blog post - New product features." The emphasis points are specified as "SEO optimization, improved user engagement." This input data becomes the base data for the next processing step.

[0781] Step 2:

[0782] The device sends the input prompt, emphasis points, and intended use to the emotion recognition engine. The emotion recognition engine uses emotion analysis techniques such as Natural Language Toolkit (NLTK) or TextBlob to analyze the user's emotion (e.g., positive, negative, neutral) from the input. The analysis result detects a "positive and excited tone."

[0783] Step 3:

[0784] Users upload previously created content (blog posts, newsletters, etc.) to the system. This past content data is sent to the server and stored in a database. This data serves as the basis for the next analysis step.

[0785] Step 4:

[0786] The server analyzes previously uploaded content. Using natural language processing techniques (e.g., the Transformers library), it extracts keywords and patterns from past content. For example, it identifies patterns related to "SEO optimization" and sentence structures related to "improving user engagement." This analysis result serves as training material for the AI ​​model.

[0787] Step 5:

[0788] The server trains an AI model based on the analysis results. A generative AI model, such as GPT-3, learns to generate new content based on the extracted patterns and content. Through this learning process, the model acquires parameters that will be used in the next content generation step.

[0789] Step 6:

[0790] Based on prompts, emphasis points, and sentiment data obtained from the user, the server uses a trained AI model to generate content. The generated content is about "Next Week's Blog Post - New Product Features" and is presented in a "positive and exciting tone." This content is then sent to the next refinement step.

[0791] Step 7:

[0792] The server adjusts the tone of the generated content. It is further refined by an emotion recognition engine, which makes fine adjustments to the tone if necessary. As a result, content that is more faithful to the user's emotions is generated. For example, more positive and engaging expressions are used, such as "Let us tell you about the unique features of our new product!"

[0793] Step 8:

[0794] The generated content is sent to the user's smartphone. The user reviews the generated content through the application and makes corrections as needed. The corrected content is then sent back to the system for final adjustments. In this way, high-quality content that matches the user's intentions and emotions is completed.

[0795] Through the above processing steps, the system of the present invention enables users to efficiently generate high-quality content and adjust the tone based on the user's emotions.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] [Third Embodiment]

[0800] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0801] 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.

[0802] 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).

[0803] 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.

[0804] 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.

[0805] 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).

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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".

[0812] The system of this invention automatically generates materials using AI based on prompts and emphasis points specified by the user. The specific implementation of this system is described below.

[0813] System Overview

[0814] The user accesses the system using a terminal. The terminal provides input forms for entering prompts, highlights, and usage details. The user uploads past materials, and the system initiates a process of generating new materials based on these uploads.

[0815] Program processing (explained in natural language)

[0816] 1. Obtaining user input

[0817] The terminal accepts input from the user. The user enters prompts, emphasis points, and intended use for generating the document into the terminal. For example, if the user wants to create a notice about an important meeting next week, they would enter that into the terminal.

[0818] 2. Sending input data

[0819] The terminal sends the data entered by the user to the server. This data serves as the basis for later document generation.

[0820] 3. Upload data

[0821] Users upload past documents to the system via their terminals. These documents include past reports, manuals, and other related documents.

[0822] 4. Learning Process

[0823] The server analyzes the uploaded materials and extracts patterns in format and content using natural language processing techniques. This allows the AI ​​model to learn patterns from past materials.

[0824] 5. Prompt Analysis

[0825] The server analyzes prompts and highlights sent by the user. In doing so, it also considers the intended use and determines the structure of the document based on those guidelines.

[0826] 6. Data generation

[0827] The server generates materials using a trained AI model. Specifically, it automatically generates the content of new materials based on user prompts and past materials. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a material like the following:

[0828] Subject: Notice regarding next week's important meeting

[0829] Content: A meeting (important) will be held next week. Please note that attendance is mandatory. Also, please review the materials in advance of the meeting.

[0830] 7. Application of the format

[0831] The server applies the appropriate format to the generated documents. Depending on the intended use, it prepares formats for internal announcements, external manuals, and other documents.

[0832] 8. Providing the results

[0833] Once the generated document is complete, the server sends it to the terminal for the user to review. The user can review the document and make corrections or regenerate it as needed.

[0834] This system allows users to efficiently generate high-quality documents with minimal effort. For example, using this system when creating internal announcements or external manuals can save time and effort while maintaining consistency and quality.

[0835] The above describes a specific embodiment for implementing the system of the present invention. This system aims to generate efficient and high-quality materials based on user input and utilizing AI technology.

[0836] The following describes the processing flow.

[0837] Step 1:

[0838] The user opens the input form.

[0839] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[0840] Step 2:

[0841] The user enters the required information.

[0842] The user enters a prompt (e.g., "Notice regarding an important meeting next week"), a key point (e.g., "Mandatory attendance, please review materials in advance"), and a purpose (e.g., "Internal announcement").

[0843] Step 3:

[0844] The terminal sends the input data to the server.

[0845] The terminal sends user-entered prompts, highlights, and usage data to the server. The data is transmitted using a secure communication protocol.

[0846] Step 4:

[0847] Users upload past documents.

[0848] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[0849] Step 5:

[0850] The device sends past data to the server.

[0851] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[0852] Step 6:

[0853] The server analyzes past data.

[0854] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[0855] Step 7:

[0856] The server trains the AI ​​model.

[0857] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[0858] Step 8:

[0859] The server parses the prompt.

[0860] The server analyzes prompts and emphasis points sent by the user. It extracts keywords and context, and determines guidelines for creating the framework of the document.

[0861] Step 9:

[0862] The server generates the contents of the document.

[0863] The server uses a trained AI model to generate information based on prompts and past data. It generates specific content that reflects the information entered by the user.

[0864] Step 10:

[0865] The server formats the documents.

[0866] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[0867] Step 11:

[0868] The server sends the generated data to the terminal.

[0869] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[0870] Step 12:

[0871] The device displays the document.

[0872] The terminal displays the materials generated for each user. Users can review the materials and modify or regenerate them as needed.

[0873] (Example 1)

[0874] 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."

[0875] Conventional information generation systems require users to spend a significant amount of time and effort creating new documents, and in particular, it is difficult to generate consistent content that reflects past documents. Furthermore, standardizing the format and content of documents is also difficult, resulting in a user-unfriendly environment. This invention aims to solve these problems and provide a system that automatically generates efficient and high-quality documents.

[0876] 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.

[0877] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past documents; and means for training a generation AI model based on the prompts and past documents. This makes it possible to efficiently generate new documents based on prompts entered by the user and past documents. Furthermore, it is possible to maintain the consistency and quality of the generated documents.

[0878] A "prompt" is a set of instructions entered by the user to specify the content and purpose of the document they are generating.

[0879] "Emphasis points" are instructions that allow users to specify the parts or content they consider particularly important when creating a document.

[0880] "Intended use" refers to the context and purpose for which the generated materials will be used.

[0881] "Documents" refer to information including reports, manuals, and other related materials created in the past.

[0882] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate text data.

[0883] Natural Language Processing (NLP) is a technology that enables computers to understand and process human language.

[0884] A "pattern" refers to the regularity or similarity of structure and expression found in past documents.

[0885] A "server" is a centralized computing device that processes data sent by users and generates materials using generative AI models.

[0886] The system of the present invention automatically generates materials using a generative AI model based on prompts and emphasis points specified by the user. A specific embodiment of this system is described below.

[0887] First, a server, terminals, and a generative AI model are necessary to support the system's operation. The server can be a cloud server (e.g., Amazon Web Services, Google Cloud Platform). The terminals are devices that provide a user interface, such as PCs, smartphones, and tablets. For the generative AI model, natural language processing technologies such as GPT-3 and BERT are used.

[0888] Users access the system using a terminal. The terminal provides input forms for users to enter prompts, highlights, and intended use for generating documents. Users upload past documents, and the system initiates the process of generating documents based on these. For example, if a user wants to create an "Announcement Regarding an Important Meeting Next Week," they would enter this into the terminal. The terminal sends the data entered by the user to the server. This data serves as the basis for subsequent document generation.

[0889] Next, the user uploads existing documents, including past reports, manuals, and other relevant documents. The server analyzes the uploaded documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn patterns from past documents.

[0890] The server analyzes prompts and highlights sent by the user, and determines the structure of the document, taking into account its intended use. It then automatically generates new documents using a generative AI model. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a document like this:

[0891] Subject: Notice regarding next week's important meeting

[0892] Content: An important meeting will be held next week. Please note that attendance is mandatory. Also, please review the details before the meeting.

[0893] The server applies the appropriate format to the generated documents. Specifically, it formats internal announcements and external manuals according to their intended use. The completed documents are sent from the server to the terminal, where the user can review them. The user can review the documents and make corrections or regenerate them as needed.

[0894] As described above, the system of the present invention allows users to efficiently generate high-quality materials without requiring much effort. For example, by using this system when creating internal announcements or external manuals, it is possible to save time and effort while maintaining consistency and quality of the materials.

[0895] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0896] Step 1:

[0897] Obtaining user input

[0898] Input: The user uses the terminal to enter prompts, emphasis points, and usage details.

[0899] Specific operation: The terminal provides an input form and accepts the content specified by the user. For example, the user enters a prompt such as "Notice regarding an important meeting next week" and an emphasis such as "Mandatory attendance."

[0900] Output: The user data entered into the terminal is prepared.

[0901] Step 2:

[0902] Sending input data

[0903] Input: Data entered by the user into the device.

[0904] Specific operation: The terminal sends the entered prompts, highlights, and usage information to the server. The data is securely transferred over the network.

[0905] Output: The input data is sent to the server and used for subsequent processing.

[0906] Step 3:

[0907] Uploading past documents

[0908] Input: Past document files (PDF, text files, etc.) uploaded by the user.

[0909] Specific operation: The user uploads past reports and manuals to the server via a terminal. The documents are saved in the appropriate format.

[0910] Output: Uploaded documents are stored on the server and used for later analysis.

[0911] Step 4:

[0912] Learning process

[0913] Input: Uploaded past documents.

[0914] Specific operation: The server analyzes uploaded documents using natural language processing techniques. Specifically, it uses Python's NLTK library and spaCy to tokenize the text within the documents and extract patterns and formats. A generative AI model (such as GPT-3 or BERT) is then trained based on the extracted patterns.

[0915] Output: A trained generative AI model is generated, and the process proceeds to the next step.

[0916] Step 5:

[0917] Prompt analysis

[0918] Input: Prompts and emphasis points sent by the user.

[0919] Specific operation: The server analyzes prompts and highlights to determine the structure and organization of the document. The intended use is also considered. Natural language processing technology is used in this process, including keyword extraction and semantic analysis.

[0920] Output: Analysis data based on prompts and highlight points is generated.

[0921] Step 6:

[0922] Data generation

[0923] Input: A pre-trained generative AI model and analyzed prompt data.

[0924] Specific operation: The server generates documents using a trained generative AI model. Based on prompts and patterns derived from past documents, it automatically constructs the content of new documents. For example, it generates a document titled "Announcement regarding next week's important meeting."

[0925] Output: Automatically generated documents will be produced.

[0926] Step 7:

[0927] Application of format

[0928] Input: Automated document.

[0929] Specific operation: The server applies the appropriate format to the generated document. It adjusts fonts, layout, headers, footers, etc., according to the intended use.

[0930] Output: A completed document with the appropriate formatting applied will be generated.

[0931] Step 8:

[0932] Providing results

[0933] Input: Completed document.

[0934] Specific operation: The server sends the generated document to the terminal. The user can review the document through the terminal and make corrections or provide feedback as needed.

[0935] Output: The completed document will be provided in a format that the user can review.

[0936] (Application Example 1)

[0937] 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."

[0938] Traditional document generation systems struggled to maintain consistency in work manuals and quality control reports generated by factory workers, requiring significant time and effort. Furthermore, errors and inconsistencies in the generated documents could reduce on-site work efficiency and potentially impact safety. A solution to this problem is needed.

[0939] 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.

[0940] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past data; means for training an AI model based on the prompts and past data; means for generating data based on the prompts and emphasis points; means for applying a specified format to the generated data; and means for checking and correcting the generated data on the operating terminal of the machine. This makes it possible to quickly generate high-quality data while saving effort and time, while maintaining consistency in factory work manuals and quality control reports.

[0941] A "prompt" refers to the instructions or initial information that a user enters into the system.

[0942] "Emphasis points" refer to the parts or content in the generated document that should be given particular importance.

[0943] "Intended use" refers to the specific situations and purposes in which the generated materials will be used.

[0944] "Past documents" refer to documents and data created previously, and are used as reference material for generating new documents.

[0945] An "AI model" refers to a learning model that automatically generates the content and format of materials based on artificial intelligence technology.

[0946] "Natural language processing" is a technology that uses computers to process and understand human language.

[0947] "Mechanical equipment" refers to devices such as work terminals and robots used within a factory.

[0948] An "operating terminal" is an electronic device used by a user to access, operate, and verify a system.

[0949] "Specified format" refers to the predetermined document format or style to which the generated material will be applied.

[0950] "Review and correction" means that the user checks the generated document and corrects its accuracy and suitability as needed.

[0951] The system of the present invention automatically generates materials using AI based on prompts and emphasis points specified by the user. Specific embodiments of the system are described below.

[0952] Hardware and software to be used

[0953] 1. Hardware:

[0954] Operating terminal: A console terminal within the factory, or a device operated directly by the user.

[0955] Machinery and equipment: Dedicated terminals and robots for displaying documents within the factory.

[0956] 2. Software:

[0957] The overall programming language used is Python.

[0958] SpaCy and NLTK are used for natural language processing.

[0959] OpenAI's GPT-3 API is used for automatic document generation.

[0960] The server has the following functions:

[0961] Detailed processing

[0962] 1. Prompt input form:

[0963] The user inputs prompts and key points for the document they want to generate via the operating terminal. For example, if the user wants to create a document summarizing important points regarding the next scheduled maintenance, they would input that information.

[0964] 2. Uploading past documents:

[0965] Users upload past work manuals and quality control reports to the server via their operating terminals. The uploaded materials are used as training material for the AI ​​model.

[0966] 3. Analysis Process:

[0967] The server analyzes the uploaded historical documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn these patterns from past documents.

[0968] 4. Document generation process:

[0969] The server analyzes user prompts and emphasis points, and determines the structure of the document based on those guidelines. Using GPT-3, it generates new documents based on past documents and user input. For example, it might create a document summarizing important points regarding the next scheduled maintenance work, or a document specifically mentioning enhanced safety measures.

[0970] 5. Apply format:

[0971] The server applies a specified format within the factory to the generated documents. For example, it formats them to internal memos or quality control reports.

[0972] 6. Review and Correction:

[0973] The user reviews the generated documents on the operating terminal and corrects the accuracy and suitability of the content as needed.

[0974] Specific example

[0975] Example of a prompt:

[0976] 1. Prompt: Document summarizing important points regarding the next scheduled maintenance work.

[0977] 2. Emphasis: Please specifically mention the strengthened safety measures.

[0978] This allows for significant savings in labor and time while maintaining consistency in work manuals and quality control reports in factory operations. It also contributes to improved safety and operational efficiency.

[0979] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0980] Step 1:

[0981] The user uses a terminal to input prompts, emphasis points, and intended use of the document they wish to generate into an input form. This input form accepts prompts and emphasis points, and the user's input is saved as data on the terminal.

[0982] Step 2:

[0983] The terminal sends the entered prompts, highlights, and usage data to the server. This transmitted data serves as a guide for generating the document.

[0984] Step 3:

[0985] Users upload past documents (e.g., work manuals, quality control reports, etc.) to the system via an operating terminal. The uploaded documents are stored on the server.

[0986] Step 4:

[0987] The server analyzes the uploaded historical data. Using natural language processing techniques, it extracts patterns in the format and content of the uploaded data. Specifically, it uses SpaCy and NLTK to decompose and analyze the text, and applies important patterns and structures as data to a learning model.

[0988] Step 5:

[0989] The server analyzes prompts and highlights, and sets the framework of the document by comparing them with patterns extracted from past materials. In this process, an AI model (such as GPT-3) is used to perform data calculations based on user prompts, and initial data for the new document content is generated.

[0990] Step 6:

[0991] The server then applies patterns from past documents to the initial generated documents, smoothly formatting them. For example, it processes them into internal memo formats or quality report formats. During this process, the documents are restructured according to the specified format.

[0992] Step 7:

[0993] The server sends the final generated document to the terminal. The terminal displays the received document to the user, who then reviews the content and makes corrections as needed. At this stage, the data corrected by the user is fed back into the system, improving the quality of the document.

[0994] 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.

[0995] The system of the present invention automatically generates materials using AI based on prompts, emphasis points, and intended use specified by the user, and further incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials. The specific implementation of this system is described below.

[0996] System Overview

[0997] The user uses a terminal to input prompts, emphasis points, and intended use through an input form. During this process, an emotion engine recognizes the user's emotions from the input data and adjusts the content and tone of the material based on the results. The user can also upload past materials, which initiates a process where the system generates new materials.

[0998] Program processing (explained in natural language)

[0999] 1. Obtaining user input

[1000] The terminal provides a form for the user to input prompts, emphasis points, and usage purposes. For example, the user might enter "Notice regarding an important meeting next week."

[1001] 2. Recognition of emotions

[1002] While the user enters information, the emotion engine analyzes the input data and recognizes the user's emotions. For example, it can sense "urgency" or "importance" from the user's input.

[1003] 3. Sending input data

[1004] The device sends data regarding prompts, emphasis points, usage, and sentiment to the server. This data forms the basis for generating the document.

[1005] 4. Upload data

[1006] Users upload past documents to their devices. These documents include past reports, manuals, and other related materials.

[1007] 5. Sending past documents

[1008] The device sends previously uploaded data to the server.

[1009] 6. Analysis of historical documents

[1010] The server analyzes the received historical document data using natural language processing techniques to extract the content, format, and important patterns of the documents.

[1011] 7. Training the AI ​​model

[1012] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[1013] 8. Analysis of prompts and emotions

[1014] The server analyzes user prompts, emphasis points, and sentiment data. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[1015] 9. Data generation

[1016] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, when creating a "Notice Regarding an Important Meeting Next Week," if the emotion engine detects "urgency," it will generate material such as:

[1017] Subject: Notice of a very important meeting

[1018] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1019] 10. Formatting of documents

[1020] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[1021] 11. Sending of generated materials

[1022] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[1023] 12. Display of materials

[1024] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[1025] Specific example

[1026] Example 1: Creating an internal company announcement

[1027] The user enters "Notice regarding an important meeting next week" and adds "Mandatory attendance, please review materials in advance" as a point of emphasis. If the emotion engine detects urgency, the system adjusts the tone of the material and generates a notice that includes the emphasized content.

[1028] Example 2: Creating a manual for external use

[1029] The user enters "How to use the new product" and "Safety precautions" as a point of emphasis. If the sentiment engine detects that a "user-friendly" tone is desired, the system adjusts the content of the document to make it more approachable and generates the final document.

[1030] In this way, the present invention can efficiently generate materials that reflect the user's emotions and satisfy the user's requirements.

[1031] The following describes the processing flow.

[1032] Step 1:

[1033] The user opens the input form.

[1034] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[1035] Step 2:

[1036] The user enters the required information.

[1037] The user enters prompts (e.g., "Notice regarding an important meeting next week"), emphasis points (e.g., "Mandatory attendance, please review materials in advance"), and intended use (e.g., "Internal announcement"). As the user enters the information, the sentiment engine analyzes it and recognizes the user's emotions.

[1038] Step 3:

[1039] The terminal sends the input data to the server.

[1040] The device sends user-entered prompts, emphasis points, usage, and sentiment data to the server. The data is transmitted using a secure communication protocol.

[1041] Step 4:

[1042] Users upload past documents.

[1043] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[1044] Step 5:

[1045] The device sends past data to the server.

[1046] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[1047] Step 6:

[1048] The server analyzes past data.

[1049] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[1050] Step 7:

[1051] The server trains the AI ​​model.

[1052] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[1053] Step 8:

[1054] The server analyzes the prompt and sentiment.

[1055] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[1056] Step 9:

[1057] The server generates the contents of the document.

[1058] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the emotion engine, sensing "urgency," will generate material like this:

[1059] Subject: Notice of a very important meeting

[1060] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1061] Step 10:

[1062] The server formats the documents.

[1063] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[1064] Step 11:

[1065] The server sends the generated data to the terminal.

[1066] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[1067] Step 12:

[1068] The device displays the document.

[1069] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[1070] (Example 2)

[1071] 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."

[1072] Conventional document generation systems have struggled to automatically generate documents based on specific tones and emotions desired by users. Furthermore, many systems have insufficient learning from past documents, requiring significant manual modifications to meet diverse user needs. Moreover, the tone and content of generated documents often do not align with user emotions, limiting their ability to accurately reflect user intent. This invention aims to solve these problems.

[1073] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past materials; means for recognizing the user's emotions using an emotion engine; means for training a generation AI model based on prompts and past materials; means for generating materials based on prompts and emphasis points; means for adjusting the content and tone of the generated materials based on the user's emotions; and means for providing the generated materials to the user. This makes it possible to automatically generate more accurate and intent-based materials based on the user's emotions and prompts.

[1074] A "user" is a person or organization that uses the system to provide data such as prompts, emphasis points, and usage purposes.

[1075] A "prompt" is a set of instructions regarding the subject or theme of the generated material that a user provides to the system.

[1076] "Emphasis points" are data that indicates elements or parts that the user wants to particularly emphasize in the document they are generating.

[1077] "Intended use" refers to information indicating the specific situations and purposes in which the generated materials will be used.

[1078] "Past documents" refer to text data such as documents and reports that the user has previously created or collected.

[1079] An "emotion engine" is a program that analyzes user input data and recognizes the emotions contained within it (e.g., urgency, importance, etc.).

[1080] A "generative AI model" is an artificial intelligence model trained using machine learning techniques to generate new information from user prompts and past data.

[1081] "Natural language processing" is a field of technology that uses computers to understand, analyze, and generate human language.

[1082] "Tone" refers to the writing style and emotional nuances of the generated material.

[1083] "Analysis" is the process of examining input data and past documents in detail to extract specific patterns and important information.

[1084] "Format" refers to the layout, structure, or visual style of a document.

[1085] A "secure protocol" is a communication standard designed to ensure the security of data transmission and reception, and common examples include TLS and HTTPS.

[1086] "Document generation" is the process of creating a new document based on user prompts and emphasis points.

[1087] The system of the present invention automatically generates materials using a generative AI model based on user-specified prompts, emphasis points, and intended use. Furthermore, it incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials accordingly. The specific method for implementing this system is described below.

[1088] First, the user uses a terminal to input prompts, emphasis points, and intended use through an input form. For example, the user might input prompts and emphasis points such as "Notification regarding next week's important meeting" or "Mandatory attendance, pre-review of materials." During this process, the sentiment engine analyzes the user's input data in real time, recognizing emotions such as "urgency" and "importance." The user can also upload past documents, including reports, manuals, and other relevant materials.

[1089] The device sends prompts, emphasis points, usage, and sentiment data received from the user to the server. This data is securely transmitted via a secure protocol such as HTTPS. In addition, past materials uploaded by the user are also sent from the device to the server.

[1090] The server uses natural language processing techniques and sentiment engines to analyze received prompts, sentiment data, and historical data. Specifically, it employs natural language processing libraries such as Python's nltk and spaCy, Google Cloud Natural Language API, and Microsoft Azure Text Analytics API. Using these techniques, the server extracts important patterns from historical data and trains a generative AI model. Libraries used for training include Scikit-learn, Keras, and TensorFlow.

[1091] The server updates the generative AI model to reflect the learning results and generates new materials based on prompts and sentiment data received from the user. This generation process reflects the emphasis points specified by the user and creates materials in an appropriate format and tone. For example, based on the prompt "Announcement regarding an important meeting next week" and the emphasis point "Mandatory attendance, please review the materials in advance," it will create materials such as "There is an important meeting next week. Attendance is mandatory for everyone, please review the materials in advance."

[1092] The generated document is sent from the server to the terminal. This transmission also uses a secure protocol. Finally, the terminal displays the generated document to the user. The user can review the displayed document and make corrections or regenerate it as needed.

[1093] This system allows users to efficiently generate documents that accurately reflect their emotions and intentions.

[1094] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1095] Step 1:

[1096] The user uses their device to enter prompts, emphasis points, and intended use into an input form. This form contains text fields where the user enters specific sentences, such as "Notice regarding next week's important meeting." The entered data includes prompts (subject or theme), emphasis points (information of particular importance), and intended use. The entered data is temporarily stored in the device's local storage.

[1097] Step 2:

[1098] While the user is entering information, the emotion engine analyzes it in real time. The emotion engine receives the input data and extracts emotions such as "urgency" and "importance" from it. Specifically, it uses natural language processing technology to tokenize the text data and applies an emotion analysis algorithm. As a result, the emotion engine can perceive "urgency" from the context of, for example, "an important meeting next week."

[1099] Step 3:

[1100] The device sends prompts, emphasis points, usage information, and recognized sentiment data obtained from the user to the server. Data transmission is performed using a secure protocol such as HTTPS. Specifically, the input data and sentiment data are packaged in JSON format and a POST request is sent to the server's API endpoint. This allows the server to receive the necessary data.

[1101] Step 4:

[1102] The user uploads past documents to the device. The document upload is done via a file selection dialog, where the user selects a file from local storage and clicks the upload button. The device reads this file and sends a POST request to the server's API endpoint as binary data in order to send it to the server.

[1103] Step 5:

[1104] The device sends previously uploaded data to the server. This is also done via a secure protocol such as HTTPS. A POST request containing file data and metadata is sent to the server, and the data is stored in the server's storage.

[1105] Step 6:

[1106] The server analyzes received historical data using natural language processing techniques. Specifically, it uses libraries such as Python's nltk and spaCy to tokenize the text data of the materials and calculate the frequency of important keywords and phrases. This analysis process grasps the content and format of the materials and extracts important patterns.

[1107] Step 7:

[1108] The server trains a generative AI model based on patterns and content obtained from the analyzed data. This training process utilizes machine learning libraries such as Scikit-learn, Keras, and TensorFlow. The server encodes features from past data and uses this as the model's training dataset. The model is then updated to apply newly learned knowledge.

[1109] Step 8:

[1110] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords using tools such as KeyBERT and TF-IDF and sets guidelines for generating materials. The server understands the context and topic of the received prompts and determines the tone and focus points of the content based on the user's sentiment.

[1111] Step 9:

[1112] The server generates materials using an updated generative AI model. Natural language generation technology is used in the process of automatically generating specific content based on prompts and sentiment data. For example, when creating a "Notice Regarding an Important Meeting Next Week," the system generates material content that reflects "urgency." This generation process automatically creates materials such as:

[1113] Subject: Notice of a very important meeting

[1114] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1115] Step 10:

[1116] The server applies a format suitable for the intended use to the generated document. Specifically, it converts it to Markdown or HTML format and applies a specific layout template. It adjusts the appearance and structure of the document to match the user's requirements.

[1117] Step 11:

[1118] The server sends the generated data to the terminal. This transmission is also performed using a secure protocol. To ensure data consistency and security, the generated data is sent in an appropriate encoding format.

[1119] Step 12:

[1120] The device displays the generated document to the user. The user can review the displayed document and modify or regenerate it as needed. The document is displayed on a web browser or mobile app, making it easy for the user to access and interact with.

[1121] (Application Example 2)

[1122] 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."

[1123] Currently, content distribution services require users to efficiently generate large-scale and diverse content, but doing so manually is time-consuming and labor-intensive. Furthermore, generated content may not align with users' emotions or intentions, leading to decreased user satisfaction and engagement. Additionally, effectively reusing past content and continuously providing high-quality content is difficult.

[1124] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and usage purposes from the user, means for uploading and analyzing past data, and means for training an AI model based on the prompts and past data. This makes it possible to quickly and effectively generate content based on user input and adjust its content and tone to match the user's emotions. Furthermore, it becomes possible to effectively reuse past data and continuously provide high-quality content.

[1125] A "prompt" is a specific input statement used to provide instructions or topics to a system.

[1126] "Emphasis points" are input fields used to indicate content or elements that the user particularly values.

[1127] "Usage" is an input field that specifies the purpose and context in which the generated information should be used.

[1128] "Past data" refers to content or information that was previously created, used, or stored.

[1129] "Means of analysis" refers to the technology or device used by a system to understand and break down input data and extract necessary patterns or information.

[1130] An "AI model" is an algorithm or system that uses artificial intelligence technology to analyze data, learn patterns, and generate new data.

[1131] An "emotion recognition engine" is a technology or system that analyzes and detects a user's emotions from input data.

[1132] "Tone" refers to an element that indicates the emotional style and atmosphere of the generated content.

[1133] "Means of generating information" refers to technologies and devices that create new data and content based on user input or learned models.

[1134] "Means of provision" refers to technologies and devices for transmitting and displaying generated information to the user.

[1135] This invention relates to a system for content distribution services that automatically generates high-quality content using AI based on user-provided prompts, emphasis points, and intended use. Furthermore, by combining it with an emotion recognition engine, it is possible to analyze the user's emotions and adjust the tone of the content accordingly.

[1136] System Overview

[1137] 1. Obtaining user input

[1138] Users input prompts, emphasis points, and usage purposes using a smartphone application.

[1139] For example, enter "Features of the new product" as the theme for next week's blog post in the prompt, and specify "SEO optimization, improved user engagement" as the emphasis point.

[1140] 2. Recognition of emotions

[1141] While the user is inputting data, an emotion recognition engine (e.g., emotion analysis technology) is used to analyze the collected data and recognize the user's emotions.

[1142] For example, it can detect a "positive and excited tone" from the user's input.

[1143] 3. Uploading past data

[1144] Users upload data such as blog posts and email newsletters they have created in the past.

[1145] The server analyzes this historical data using natural language processing techniques and extracts patterns.

[1146] 4. Training the AI ​​model

[1147] The server trains an AI model based on the patterns and content it has analyzed.

[1148] For example, a model is trained to generate new content from an SEO optimization perspective, based on patterns extracted from past blog posts.

[1149] 5. Content Generation

[1150] Based on prompts, emphasis points, and sentiment data obtained from users, an AI model is used to generate content.

[1151] For example, generate a blog post for next week with information about the "features of the new product" in a positive tone.

[1152] 6. Tone adjustment

[1153] The generated content is tone-adjusted based on the user's emotions by an emotion recognition engine.

[1154] For example, positive and exciting phrases such as, "Let us tell you about the unique features of our new product!" are used.

[1155] 7. Content provision and verification

[1156] The generated content is delivered to the user's smartphone, allowing the user to review and modify the content.

[1157] Hardware and software to be used

[1158] Hardware: User's smartphone device, server

[1159] Software: Emotion recognition engine (e.g., emotion analysis technology), natural language processing technology (e.g., Transformers library), AI model (e.g., GPT-3)

[1160] Specific example

[1161] Example of a prompt:

[1162] Prompt: "Theme for next week's blog post - Features of the new product"

[1163] Key points: "SEO optimization, improved user engagement"

[1164] Emotion: "Positive and excited tone"

[1165] Thus, the present invention can improve user satisfaction and engagement by automatically and efficiently generating high-quality content based on user input and adjusting it based on user emotions. Furthermore, by effectively reusing past data, it is possible to continuously provide high-quality content.

[1166] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1167] Step 1:

[1168] The user uses a smartphone application to input prompts, emphasis points, and usage purposes. An example of an input prompt is "Theme for next week's blog post - New product features." The emphasis points are specified as "SEO optimization, improved user engagement." This input data becomes the base data for the next processing step.

[1169] Step 2:

[1170] The device sends the input prompt, emphasis points, and intended use to the emotion recognition engine. The emotion recognition engine uses emotion analysis techniques such as Natural Language Toolkit (NLTK) or TextBlob to analyze the user's emotion (e.g., positive, negative, neutral) from the input. The analysis result detects a "positive and excited tone."

[1171] Step 3:

[1172] Users upload previously created content (blog posts, newsletters, etc.) to the system. This past content data is sent to the server and stored in a database. This data serves as the basis for the next analysis step.

[1173] Step 4:

[1174] The server analyzes previously uploaded content. Using natural language processing techniques (e.g., the Transformers library), it extracts keywords and patterns from past content. For example, it identifies patterns related to "SEO optimization" and sentence structures related to "improving user engagement." This analysis result serves as training material for the AI ​​model.

[1175] Step 5:

[1176] The server trains an AI model based on the analysis results. A generative AI model, such as GPT-3, learns to generate new content based on the extracted patterns and content. Through this learning process, the model acquires parameters that will be used in the next content generation step.

[1177] Step 6:

[1178] Based on prompts, emphasis points, and sentiment data obtained from the user, the server uses a trained AI model to generate content. The generated content is about "Next Week's Blog Post - New Product Features" and is presented in a "positive and exciting tone." This content is then sent to the next refinement step.

[1179] Step 7:

[1180] The server adjusts the tone of the generated content. It is further refined by an emotion recognition engine, which makes fine adjustments to the tone if necessary. As a result, content that is more faithful to the user's emotions is generated. For example, more positive and engaging expressions are used, such as "Let us tell you about the unique features of our new product!"

[1181] Step 8:

[1182] The generated content is sent to the user's smartphone. The user reviews the generated content through the application and makes corrections as needed. The corrected content is then sent back to the system for final adjustments. In this way, high-quality content that matches the user's intentions and emotions is completed.

[1183] Through the above processing steps, the system of the present invention enables users to efficiently generate high-quality content and adjust the tone based on the user's emotions.

[1184] 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.

[1185] 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.

[1186] 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.

[1187] [Fourth Embodiment]

[1188] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1189] 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.

[1190] 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).

[1191] 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.

[1192] 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.

[1193] 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).

[1194] 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.

[1195] 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.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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".

[1201] The system of this invention automatically generates materials using AI based on prompts and emphasis points specified by the user. The specific implementation of this system is described below.

[1202] System Overview

[1203] The user accesses the system using a terminal. The terminal provides input forms for entering prompts, highlights, and usage details. The user uploads past materials, and the system initiates a process of generating new materials based on these uploads.

[1204] Program processing (explained in natural language)

[1205] 1. Obtaining user input

[1206] The terminal accepts input from the user. The user enters prompts, emphasis points, and intended use for generating the document into the terminal. For example, if the user wants to create a notice about an important meeting next week, they would enter that into the terminal.

[1207] 2. Sending input data

[1208] The terminal sends the data entered by the user to the server. This data serves as the basis for later document generation.

[1209] 3. Upload data

[1210] Users upload past documents to the system via their terminals. These documents include past reports, manuals, and other related documents.

[1211] 4. Learning Process

[1212] The server analyzes the uploaded materials and extracts patterns in format and content using natural language processing techniques. This allows the AI ​​model to learn patterns from past materials.

[1213] 5. Prompt Analysis

[1214] The server analyzes prompts and highlights sent by the user. In doing so, it also considers the intended use and determines the structure of the document based on those guidelines.

[1215] 6. Data generation

[1216] The server generates materials using a trained AI model. Specifically, it automatically generates the content of new materials based on user prompts and past materials. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a material like the following:

[1217] Subject: Notice regarding next week's important meeting

[1218] Content: A meeting (important) will be held next week. Please note that attendance is mandatory. Also, please review the materials in advance of the meeting.

[1219] 7. Application of the format

[1220] The server applies the appropriate format to the generated documents. Depending on the intended use, it prepares formats for internal announcements, external manuals, and other documents.

[1221] 8. Providing the results

[1222] Once the generated document is complete, the server sends it to the terminal for the user to review. The user can review the document and make corrections or regenerate it as needed.

[1223] This system allows users to efficiently generate high-quality documents with minimal effort. For example, using this system when creating internal announcements or external manuals can save time and effort while maintaining consistency and quality.

[1224] The above describes a specific embodiment for implementing the system of the present invention. This system aims to generate efficient and high-quality materials based on user input and utilizing AI technology.

[1225] The following describes the processing flow.

[1226] Step 1:

[1227] The user opens the input form.

[1228] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[1229] Step 2:

[1230] The user enters the required information.

[1231] The user enters a prompt (e.g., "Notice regarding an important meeting next week"), a key point (e.g., "Mandatory attendance, please review materials in advance"), and a purpose (e.g., "Internal announcement").

[1232] Step 3:

[1233] The terminal sends the input data to the server.

[1234] The terminal sends user-entered prompts, highlights, and usage data to the server. The data is transmitted using a secure communication protocol.

[1235] Step 4:

[1236] Users upload past documents.

[1237] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[1238] Step 5:

[1239] The device sends past data to the server.

[1240] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[1241] Step 6:

[1242] The server analyzes past data.

[1243] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[1244] Step 7:

[1245] The server trains the AI ​​model.

[1246] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[1247] Step 8:

[1248] The server parses the prompt.

[1249] The server analyzes prompts and emphasis points sent by the user. It extracts keywords and context, and determines guidelines for creating the framework of the document.

[1250] Step 9:

[1251] The server generates the contents of the document.

[1252] The server uses a trained AI model to generate information based on prompts and past data. It generates specific content that reflects the information entered by the user.

[1253] Step 10:

[1254] The server formats the documents.

[1255] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[1256] Step 11:

[1257] The server sends the generated data to the terminal.

[1258] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[1259] Step 12:

[1260] The device displays the document.

[1261] The terminal displays the materials generated for each user. Users can review the materials and modify or regenerate them as needed.

[1262] (Example 1)

[1263] 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".

[1264] Conventional information generation systems require users to spend a significant amount of time and effort creating new documents, and in particular, it is difficult to generate consistent content that reflects past documents. Furthermore, standardizing the format and content of documents is also difficult, resulting in a user-unfriendly environment. This invention aims to solve these problems and provide a system that automatically generates efficient and high-quality documents.

[1265] 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.

[1266] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past documents; and means for training a generation AI model based on the prompts and past documents. This makes it possible to efficiently generate new documents based on prompts entered by the user and past documents. Furthermore, it is possible to maintain the consistency and quality of the generated documents.

[1267] A "prompt" is a set of instructions entered by the user to specify the content and purpose of the document they are generating.

[1268] "Emphasis points" are instructions that allow users to specify the parts or content they consider particularly important when creating a document.

[1269] "Intended use" refers to the context and purpose for which the generated materials will be used.

[1270] "Documents" refer to information including reports, manuals, and other related materials created in the past.

[1271] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate text data.

[1272] Natural Language Processing (NLP) is a technology that enables computers to understand and process human language.

[1273] A "pattern" refers to the regularity or similarity of structure and expression found in past documents.

[1274] A "server" is a centralized computing device that processes data sent by users and generates materials using generative AI models.

[1275] The system of the present invention automatically generates materials using a generative AI model based on prompts and emphasis points specified by the user. A specific embodiment of this system is described below.

[1276] First, a server, terminals, and a generative AI model are necessary to support the system's operation. The server can be a cloud server (e.g., Amazon Web Services, Google Cloud Platform). The terminals are devices that provide a user interface, such as PCs, smartphones, and tablets. For the generative AI model, natural language processing technologies such as GPT-3 and BERT are used.

[1277] Users access the system using a terminal. The terminal provides input forms for users to enter prompts, highlights, and intended use for generating documents. Users upload past documents, and the system initiates the process of generating documents based on these. For example, if a user wants to create an "Announcement Regarding an Important Meeting Next Week," they would enter this into the terminal. The terminal sends the data entered by the user to the server. This data serves as the basis for subsequent document generation.

[1278] Next, the user uploads existing documents, including past reports, manuals, and other relevant documents. The server analyzes the uploaded documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn patterns from past documents.

[1279] The server analyzes prompts and highlights sent by the user, and determines the structure of the document, taking into account its intended use. It then automatically generates new documents using a generative AI model. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the server will generate a document like this:

[1280] Subject: Notice regarding next week's important meeting

[1281] Content: An important meeting will be held next week. Please note that attendance is mandatory. Also, please review the details before the meeting.

[1282] The server applies the appropriate format to the generated documents. Specifically, it formats internal announcements and external manuals according to their intended use. The completed documents are sent from the server to the terminal, where the user can review them. The user can review the documents and make corrections or regenerate them as needed.

[1283] As described above, the system of the present invention allows users to efficiently generate high-quality materials without requiring much effort. For example, by using this system when creating internal announcements or external manuals, it is possible to save time and effort while maintaining consistency and quality of the materials.

[1284] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1285] Step 1:

[1286] Obtaining user input

[1287] Input: The user uses the terminal to enter prompts, emphasis points, and usage details.

[1288] Specific operation: The terminal provides an input form and accepts the content specified by the user. For example, the user enters a prompt such as "Notice regarding an important meeting next week" and an emphasis such as "Mandatory attendance."

[1289] Output: The user data entered into the terminal is prepared.

[1290] Step 2:

[1291] Sending input data

[1292] Input: Data entered by the user into the device.

[1293] Specific operation: The terminal sends the entered prompts, highlights, and usage information to the server. The data is securely transferred over the network.

[1294] Output: The input data is sent to the server and used for subsequent processing.

[1295] Step 3:

[1296] Uploading past documents

[1297] Input: Past document files (PDF, text files, etc.) uploaded by the user.

[1298] Specific operation: The user uploads past reports and manuals to the server via a terminal. The documents are saved in the appropriate format.

[1299] Output: Uploaded documents are stored on the server and used for later analysis.

[1300] Step 4:

[1301] Learning process

[1302] Input: Uploaded past documents.

[1303] Specific operation: The server analyzes uploaded documents using natural language processing techniques. Specifically, it uses Python's NLTK library and spaCy to tokenize the text within the documents and extract patterns and formats. A generative AI model (such as GPT-3 or BERT) is then trained based on the extracted patterns.

[1304] Output: A trained generative AI model is generated, and the process proceeds to the next step.

[1305] Step 5:

[1306] Prompt analysis

[1307] Input: Prompts and emphasis points sent by the user.

[1308] Specific operation: The server analyzes prompts and highlights to determine the structure and organization of the document. The intended use is also considered. Natural language processing technology is used in this process, including keyword extraction and semantic analysis.

[1309] Output: Analysis data based on prompts and highlight points is generated.

[1310] Step 6:

[1311] Data generation

[1312] Input: A pre-trained generative AI model and analyzed prompt data.

[1313] Specific operation: The server generates documents using a trained generative AI model. Based on prompts and patterns derived from past documents, it automatically constructs the content of new documents. For example, it generates a document titled "Announcement regarding next week's important meeting."

[1314] Output: Automatically generated documents will be produced.

[1315] Step 7:

[1316] Application of format

[1317] Input: Automated document.

[1318] Specific operation: The server applies the appropriate format to the generated document. It adjusts fonts, layout, headers, footers, etc., according to the intended use.

[1319] Output: A completed document with the appropriate formatting applied will be generated.

[1320] Step 8:

[1321] Providing results

[1322] Input: Completed document.

[1323] Specific operation: The server sends the generated document to the terminal. The user can review the document through the terminal and make corrections or provide feedback as needed.

[1324] Output: The completed document will be provided in a format that the user can review.

[1325] (Application Example 1)

[1326] 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".

[1327] Traditional document generation systems struggled to maintain consistency in work manuals and quality control reports generated by factory workers, requiring significant time and effort. Furthermore, errors and inconsistencies in the generated documents could reduce on-site work efficiency and potentially impact safety. A solution to this problem is needed.

[1328] 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.

[1329] In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past data; means for training an AI model based on the prompts and past data; means for generating data based on the prompts and emphasis points; means for applying a specified format to the generated data; and means for checking and correcting the generated data on the operating terminal of the machine. This makes it possible to quickly generate high-quality data while saving effort and time, while maintaining consistency in factory work manuals and quality control reports.

[1330] A "prompt" refers to the instructions or initial information that a user enters into the system.

[1331] "Emphasis points" refer to the parts or content in the generated document that should be given particular importance.

[1332] "Intended use" refers to the specific situations and purposes in which the generated materials will be used.

[1333] "Past documents" refer to documents and data created previously, and are used as reference material for generating new documents.

[1334] An "AI model" refers to a learning model that automatically generates the content and format of materials based on artificial intelligence technology.

[1335] "Natural language processing" is a technology that uses computers to process and understand human language.

[1336] "Mechanical equipment" refers to devices such as work terminals and robots used within a factory.

[1337] An "operating terminal" is an electronic device used by a user to access, operate, and verify a system.

[1338] "Specified format" refers to the predetermined document format or style to which the generated material will be applied.

[1339] "Review and correction" means that the user checks the generated document and corrects its accuracy and suitability as needed.

[1340] The system of the present invention automatically generates materials using AI based on prompts and emphasis points specified by the user. Specific embodiments of the system are described below.

[1341] Hardware and software to be used

[1342] 1. Hardware:

[1343] Operating terminal: A console terminal within the factory, or a device operated directly by the user.

[1344] Machinery and equipment: Dedicated terminals and robots for displaying documents within the factory.

[1345] 2. Software:

[1346] The overall programming language used is Python.

[1347] SpaCy and NLTK are used for natural language processing.

[1348] OpenAI's GPT-3 API is used for automatic document generation.

[1349] The server has the following functions:

[1350] Detailed processing

[1351] 1. Prompt input form:

[1352] The user inputs prompts and key points for the document they want to generate via the operating terminal. For example, if the user wants to create a document summarizing important points regarding the next scheduled maintenance, they would input that information.

[1353] 2. Uploading past documents:

[1354] Users upload past work manuals and quality control reports to the server via their operating terminals. The uploaded materials are used as training material for the AI ​​model.

[1355] 3. Analysis Process:

[1356] The server analyzes the uploaded historical documents and uses natural language processing techniques to extract patterns in their format and content. This allows the AI ​​model to learn these patterns from past documents.

[1357] 4. Document generation process:

[1358] The server analyzes user prompts and emphasis points, and determines the structure of the document based on those guidelines. Using GPT-3, it generates new documents based on past documents and user input. For example, it might create a document summarizing important points regarding the next scheduled maintenance work, or a document specifically mentioning enhanced safety measures.

[1359] 5. Apply format:

[1360] The server applies a specified format within the factory to the generated documents. For example, it formats them to internal memos or quality control reports.

[1361] 6. Review and Correction:

[1362] The user reviews the generated documents on the operating terminal and corrects the accuracy and suitability of the content as needed.

[1363] Specific example

[1364] Example of a prompt:

[1365] 1. Prompt: Document summarizing important points regarding the next scheduled maintenance work.

[1366] 2. Emphasis: Please specifically mention the strengthened safety measures.

[1367] This allows for significant savings in labor and time while maintaining consistency in work manuals and quality control reports in factory operations. It also contributes to improved safety and operational efficiency.

[1368] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1369] Step 1:

[1370] The user uses a terminal to input prompts, emphasis points, and intended use of the document they wish to generate into an input form. This input form accepts prompts and emphasis points, and the user's input is saved as data on the terminal.

[1371] Step 2:

[1372] The terminal sends the entered prompts, highlights, and usage data to the server. This transmitted data serves as a guide for generating the document.

[1373] Step 3:

[1374] Users upload past documents (e.g., work manuals, quality control reports, etc.) to the system via an operating terminal. The uploaded documents are stored on the server.

[1375] Step 4:

[1376] The server analyzes the uploaded historical data. Using natural language processing techniques, it extracts patterns in the format and content of the uploaded data. Specifically, it uses SpaCy and NLTK to decompose and analyze the text, and applies important patterns and structures as data to a learning model.

[1377] Step 5:

[1378] The server analyzes prompts and highlights, and sets the framework of the document by comparing them with patterns extracted from past materials. In this process, an AI model (such as GPT-3) is used to perform data calculations based on user prompts, and initial data for the new document content is generated.

[1379] Step 6:

[1380] The server then applies patterns from past documents to the initial generated documents, smoothly formatting them. For example, it processes them into internal memo formats or quality report formats. During this process, the documents are restructured according to the specified format.

[1381] Step 7:

[1382] The server sends the final generated document to the terminal. The terminal displays the received document to the user, who then reviews the content and makes corrections as needed. At this stage, the data corrected by the user is fed back into the system, improving the quality of the document.

[1383] 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.

[1384] The system of the present invention automatically generates materials using AI based on prompts, emphasis points, and intended use specified by the user, and further incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials. The specific implementation of this system is described below.

[1385] System Overview

[1386] The user uses a terminal to input prompts, emphasis points, and intended use through an input form. During this process, an emotion engine recognizes the user's emotions from the input data and adjusts the content and tone of the material based on the results. The user can also upload past materials, which initiates a process where the system generates new materials.

[1387] Program processing (explained in natural language)

[1388] 1. Obtaining user input

[1389] The terminal provides a form for the user to input prompts, emphasis points, and usage purposes. For example, the user might enter "Notice regarding an important meeting next week."

[1390] 2. Recognition of emotions

[1391] While the user enters information, the emotion engine analyzes the input data and recognizes the user's emotions. For example, it can sense "urgency" or "importance" from the user's input.

[1392] 3. Sending input data

[1393] The device sends data regarding prompts, emphasis points, usage, and sentiment to the server. This data forms the basis for generating the document.

[1394] 4. Upload data

[1395] Users upload past documents to their devices. These documents include past reports, manuals, and other related materials.

[1396] 5. Sending past documents

[1397] The device sends previously uploaded data to the server.

[1398] 6. Analysis of historical documents

[1399] The server analyzes the received historical document data using natural language processing techniques to extract the content, format, and important patterns of the documents.

[1400] 7. Training the AI ​​model

[1401] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[1402] 8. Analysis of prompts and emotions

[1403] The server analyzes user prompts, emphasis points, and sentiment data. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[1404] 9. Data generation

[1405] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, when creating a "Notice Regarding an Important Meeting Next Week," if the emotion engine detects "urgency," it will generate material such as:

[1406] Subject: Notice of a very important meeting

[1407] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1408] 10. Formatting of documents

[1409] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[1410] 11. Sending of generated materials

[1411] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[1412] 12. Display of materials

[1413] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[1414] Specific example

[1415] Example 1: Creating an internal company announcement

[1416] The user enters "Notice regarding an important meeting next week" and adds "Mandatory attendance, please review materials in advance" as a point of emphasis. If the emotion engine detects urgency, the system adjusts the tone of the material and generates a notice that includes the emphasized content.

[1417] Example 2: Creating a manual for external use

[1418] The user enters "How to use the new product" and "Safety precautions" as a point of emphasis. If the sentiment engine detects that a "user-friendly" tone is desired, the system adjusts the content of the document to make it more approachable and generates the final document.

[1419] In this way, the present invention can efficiently generate materials that reflect the user's emotions and satisfy the user's requirements.

[1420] The following describes the processing flow.

[1421] Step 1:

[1422] The user opens the input form.

[1423] The user uses their terminal to open an input form for generating the document. The input form includes fields for entering prompts, emphasis points, and intended use.

[1424] Step 2:

[1425] The user enters the required information.

[1426] The user enters prompts (e.g., "Notice regarding an important meeting next week"), emphasis points (e.g., "Mandatory attendance, please review materials in advance"), and intended use (e.g., "Internal announcement"). As the user enters the information, the sentiment engine analyzes it and recognizes the user's emotions.

[1427] Step 3:

[1428] The terminal sends the input data to the server.

[1429] The device sends user-entered prompts, emphasis points, usage, and sentiment data to the server. The data is transmitted using a secure communication protocol.

[1430] Step 4:

[1431] Users upload past documents.

[1432] The user selects and uploads past documents (e.g., past company announcements) using their device. The format and content of these documents are then analyzed in the next step.

[1433] Step 5:

[1434] The device sends past data to the server.

[1435] The device sends data of past materials uploaded by the user to the server. The data is transmitted using a secure communication protocol.

[1436] Step 6:

[1437] The server analyzes past data.

[1438] The server analyzes the received historical data using natural language processing techniques. Through this analysis, it extracts the content, format, and important patterns of the data.

[1439] Step 7:

[1440] The server trains the AI ​​model.

[1441] The server updates and trains the AI ​​model based on the analyzed patterns and content. Through this learning process, the system acquires the knowledge necessary to generate new material.

[1442] Step 8:

[1443] The server analyzes the prompt and sentiment.

[1444] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords, context, and user sentiment to determine guidelines for creating the framework of the document.

[1445] Step 9:

[1446] The server generates the contents of the document.

[1447] The server generates materials using a trained AI model. It generates specific content based on user prompts and emotions. For example, if a user wants to create a "Notice Regarding an Important Meeting Next Week," the emotion engine, sensing "urgency," will generate material like this:

[1448] Subject: Notice of a very important meeting

[1449] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1450] Step 10:

[1451] The server formats the documents.

[1452] The server applies a format suitable for the intended use (e.g., layout for internal company announcements) to the generated document, finalizing the document.

[1453] Step 11:

[1454] The server sends the generated data to the terminal.

[1455] The server sends the generated data to the terminal. The data is transmitted using a secure communication protocol.

[1456] Step 12:

[1457] The device displays the document.

[1458] The terminal displays the generated document to the user. The user can review the document and modify or regenerate it as needed.

[1459] (Example 2)

[1460] 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".

[1461] Conventional document generation systems have struggled to automatically generate documents based on specific tones and emotions desired by users. Furthermore, many systems have insufficient learning from past documents, requiring significant manual modifications to meet diverse user needs. Moreover, the tone and content of generated documents often do not align with user emotions, limiting their ability to accurately reflect user intent. This invention aims to solve these problems.

[1462] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and intended use from the user; means for uploading and analyzing past materials; means for recognizing the user's emotions using an emotion engine; means for training a generation AI model based on prompts and past materials; means for generating materials based on prompts and emphasis points; means for adjusting the content and tone of the generated materials based on the user's emotions; and means for providing the generated materials to the user. This makes it possible to automatically generate more accurate and intent-based materials based on the user's emotions and prompts.

[1463] A "user" is a person or organization that uses the system to provide data such as prompts, emphasis points, and usage purposes.

[1464] A "prompt" is a set of instructions regarding the subject or theme of the generated material that a user provides to the system.

[1465] "Emphasis points" are data that indicates elements or parts that the user wants to particularly emphasize in the document they are generating.

[1466] "Intended use" refers to information indicating the specific situations and purposes in which the generated materials will be used.

[1467] "Past documents" refer to text data such as documents and reports that the user has previously created or collected.

[1468] An "emotion engine" is a program that analyzes user input data and recognizes the emotions contained within it (e.g., urgency, importance, etc.).

[1469] A "generative AI model" is an artificial intelligence model trained using machine learning techniques to generate new information from user prompts and past data.

[1470] "Natural language processing" is a field of technology that uses computers to understand, analyze, and generate human language.

[1471] "Tone" refers to the writing style and emotional nuances of the generated material.

[1472] "Analysis" is the process of examining input data and past documents in detail to extract specific patterns and important information.

[1473] "Format" refers to the layout, structure, or visual style of a document.

[1474] A "secure protocol" is a communication standard designed to ensure the security of data transmission and reception, and common examples include TLS and HTTPS.

[1475] "Document generation" is the process of creating a new document based on user prompts and emphasis points.

[1476] The system of the present invention automatically generates materials using a generative AI model based on user-specified prompts, emphasis points, and intended use. Furthermore, it incorporates an emotion engine to recognize the user's emotions and adjust the content and tone of the materials accordingly. The specific method for implementing this system is described below.

[1477] First, the user uses a terminal to input prompts, emphasis points, and intended use through an input form. For example, the user might input prompts and emphasis points such as "Notification regarding next week's important meeting" or "Mandatory attendance, pre-review of materials." During this process, the sentiment engine analyzes the user's input data in real time, recognizing emotions such as "urgency" and "importance." The user can also upload past documents, including reports, manuals, and other relevant materials.

[1478] The device sends prompts, emphasis points, usage, and sentiment data received from the user to the server. This data is securely transmitted via a secure protocol such as HTTPS. In addition, past materials uploaded by the user are also sent from the device to the server.

[1479] The server uses natural language processing techniques and sentiment engines to analyze received prompts, sentiment data, and historical data. Specifically, it employs natural language processing libraries such as Python's nltk and spaCy, Google Cloud Natural Language API, and Microsoft Azure Text Analytics API. Using these techniques, the server extracts important patterns from historical data and trains a generative AI model. Libraries used for training include Scikit-learn, Keras, and TensorFlow.

[1480] The server updates the generative AI model to reflect the learning results and generates new materials based on prompts and sentiment data received from the user. This generation process reflects the emphasis points specified by the user and creates materials in an appropriate format and tone. For example, based on the prompt "Announcement regarding an important meeting next week" and the emphasis point "Mandatory attendance, please review the materials in advance," it will create materials such as "There is an important meeting next week. Attendance is mandatory for everyone, please review the materials in advance."

[1481] The generated document is sent from the server to the terminal. This transmission also uses a secure protocol. Finally, the terminal displays the generated document to the user. The user can review the displayed document and make corrections or regenerate it as needed.

[1482] This system allows users to efficiently generate documents that accurately reflect their emotions and intentions.

[1483] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1484] Step 1:

[1485] The user uses their device to enter prompts, emphasis points, and intended use into an input form. This form contains text fields where the user enters specific sentences, such as "Notice regarding next week's important meeting." The entered data includes prompts (subject or theme), emphasis points (information of particular importance), and intended use. The entered data is temporarily stored in the device's local storage.

[1486] Step 2:

[1487] While the user is entering information, the emotion engine analyzes it in real time. The emotion engine receives the input data and extracts emotions such as "urgency" and "importance" from it. Specifically, it uses natural language processing technology to tokenize the text data and applies an emotion analysis algorithm. As a result, the emotion engine can perceive "urgency" from the context of, for example, "an important meeting next week."

[1488] Step 3:

[1489] The device sends prompts, emphasis points, usage information, and recognized sentiment data obtained from the user to the server. Data transmission is performed using a secure protocol such as HTTPS. Specifically, the input data and sentiment data are packaged in JSON format and a POST request is sent to the server's API endpoint. This allows the server to receive the necessary data.

[1490] Step 4:

[1491] The user uploads past documents to the device. The document upload is done via a file selection dialog, where the user selects a file from local storage and clicks the upload button. The device reads this file and sends a POST request to the server's API endpoint as binary data in order to send it to the server.

[1492] Step 5:

[1493] The device sends previously uploaded data to the server. This is also done via a secure protocol such as HTTPS. A POST request containing file data and metadata is sent to the server, and the data is stored in the server's storage.

[1494] Step 6:

[1495] The server analyzes received historical data using natural language processing techniques. Specifically, it uses libraries such as Python's nltk and spaCy to tokenize the text data of the materials and calculate the frequency of important keywords and phrases. This analysis process grasps the content and format of the materials and extracts important patterns.

[1496] Step 7:

[1497] The server trains a generative AI model based on patterns and content obtained from the analyzed data. This training process utilizes machine learning libraries such as Scikit-learn, Keras, and TensorFlow. The server encodes features from past data and uses this as the model's training dataset. The model is then updated to apply newly learned knowledge.

[1498] Step 8:

[1499] The server analyzes prompts, emphasis points, and sentiment data sent by the user. It extracts keywords using tools such as KeyBERT and TF-IDF and sets guidelines for generating materials. The server understands the context and topic of the received prompts and determines the tone and focus points of the content based on the user's sentiment.

[1500] Step 9:

[1501] The server generates materials using an updated generative AI model. Natural language generation technology is used in the process of automatically generating specific content based on prompts and sentiment data. For example, when creating a "Notice Regarding an Important Meeting Next Week," the system generates material content that reflects "urgency." This generation process automatically creates materials such as:

[1502] Subject: Notice of a very important meeting

[1503] Content: There is an important meeting next week. Attendance is mandatory for everyone, so please review the materials in advance.

[1504] Step 10:

[1505] The server applies a format suitable for the intended use to the generated document. Specifically, it converts it to Markdown or HTML format and applies a specific layout template. It adjusts the appearance and structure of the document to match the user's requirements.

[1506] Step 11:

[1507] The server sends the generated data to the terminal. This transmission is also performed using a secure protocol. To ensure data consistency and security, the generated data is sent in an appropriate encoding format.

[1508] Step 12:

[1509] The device displays the generated document to the user. The user can review the displayed document and modify or regenerate it as needed. The document is displayed on a web browser or mobile app, making it easy for the user to access and interact with.

[1510] (Application Example 2)

[1511] 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".

[1512] Currently, content distribution services require users to efficiently generate large-scale and diverse content, but doing so manually is time-consuming and labor-intensive. Furthermore, generated content may not align with users' emotions or intentions, leading to decreased user satisfaction and engagement. Additionally, effectively reusing past content and continuously providing high-quality content is difficult.

[1513] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving prompts, emphasis points, and usage purposes from the user, means for uploading and analyzing past data, and means for training an AI model based on the prompts and past data. This makes it possible to quickly and effectively generate content based on user input and adjust its content and tone to match the user's emotions. Furthermore, it becomes possible to effectively reuse past data and continuously provide high-quality content.

[1514] A "prompt" is a specific input statement used to provide instructions or topics to a system.

[1515] "Emphasis points" are input fields used to indicate content or elements that the user particularly values.

[1516] "Usage" is an input field that specifies the purpose and context in which the generated information should be used.

[1517] "Past data" refers to content or information that was previously created, used, or stored.

[1518] "Means of analysis" refers to the technology or device used by a system to understand and break down input data and extract necessary patterns or information.

[1519] An "AI model" is an algorithm or system that uses artificial intelligence technology to analyze data, learn patterns, and generate new data.

[1520] An "emotion recognition engine" is a technology or system that analyzes and detects a user's emotions from input data.

[1521] "Tone" refers to an element that indicates the emotional style and atmosphere of the generated content.

[1522] "Means of generating information" refers to technologies and devices that create new data and content based on user input or learned models.

[1523] "Means of provision" refers to technologies and devices for transmitting and displaying generated information to the user.

[1524] This invention relates to a system for content distribution services that automatically generates high-quality content using AI based on user-provided prompts, emphasis points, and intended use. Furthermore, by combining it with an emotion recognition engine, it is possible to analyze the user's emotions and adjust the tone of the content accordingly.

[1525] System Overview

[1526] 1. Obtaining user input

[1527] Users input prompts, emphasis points, and usage purposes using a smartphone application.

[1528] For example, enter "Features of the new product" as the theme for next week's blog post in the prompt, and specify "SEO optimization, improved user engagement" as the emphasis point.

[1529] 2. Recognition of emotions

[1530] While the user is inputting data, an emotion recognition engine (e.g., emotion analysis technology) is used to analyze the collected data and recognize the user's emotions.

[1531] For example, it can detect a "positive and excited tone" from the user's input.

[1532] 3. Uploading past data

[1533] Users upload data such as blog posts and email newsletters they have created in the past.

[1534] The server analyzes this historical data using natural language processing techniques and extracts patterns.

[1535] 4. Training the AI ​​model

[1536] The server trains an AI model based on the patterns and content it has analyzed.

[1537] For example, a model is trained to generate new content from an SEO optimization perspective, based on patterns extracted from past blog posts.

[1538] 5. Content Generation

[1539] Based on prompts, emphasis points, and sentiment data obtained from users, an AI model is used to generate content.

[1540] For example, generate a blog post for next week with information about the "features of the new product" in a positive tone.

[1541] 6. Tone adjustment

[1542] The generated content is tone-adjusted based on the user's emotions by an emotion recognition engine.

[1543] For example, positive and exciting phrases such as, "Let us tell you about the unique features of our new product!" are used.

[1544] 7. Content provision and verification

[1545] The generated content is delivered to the user's smartphone, allowing the user to review and modify the content.

[1546] Hardware and software to be used

[1547] Hardware: User's smartphone device, server

[1548] Software: Emotion recognition engine (e.g., emotion analysis technology), natural language processing technology (e.g., Transformers library), AI model (e.g., GPT-3)

[1549] Specific example

[1550] Example of a prompt:

[1551] Prompt: "Theme for next week's blog post - Features of the new product"

[1552] Key points: "SEO optimization, improved user engagement"

[1553] Emotion: "Positive and excited tone"

[1554] Thus, the present invention can improve user satisfaction and engagement by automatically and efficiently generating high-quality content based on user input and adjusting it based on user emotions. Furthermore, by effectively reusing past data, it is possible to continuously provide high-quality content.

[1555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1556] Step 1:

[1557] The user uses a smartphone application to input prompts, emphasis points, and usage purposes. An example of an input prompt is "Theme for next week's blog post - New product features." The emphasis points are specified as "SEO optimization, improved user engagement." This input data becomes the base data for the next processing step.

[1558] Step 2:

[1559] The device sends the input prompt, emphasis points, and intended use to the emotion recognition engine. The emotion recognition engine uses emotion analysis techniques such as Natural Language Toolkit (NLTK) or TextBlob to analyze the user's emotion (e.g., positive, negative, neutral) from the input. The analysis result detects a "positive and excited tone."

[1560] Step 3:

[1561] Users upload previously created content (blog posts, newsletters, etc.) to the system. This past content data is sent to the server and stored in a database. This data serves as the basis for the next analysis step.

[1562] Step 4:

[1563] The server analyzes previously uploaded content. Using natural language processing techniques (e.g., the Transformers library), it extracts keywords and patterns from past content. For example, it identifies patterns related to "SEO optimization" and sentence structures related to "improving user engagement." This analysis result serves as training material for the AI ​​model.

[1564] Step 5:

[1565] The server trains an AI model based on the analysis results. A generative AI model, such as GPT-3, learns to generate new content based on the extracted patterns and content. Through this learning process, the model acquires parameters that will be used in the next content generation step.

[1566] Step 6:

[1567] Based on prompts, emphasis points, and sentiment data obtained from the user, the server uses a trained AI model to generate content. The generated content is about "Next Week's Blog Post - New Product Features" and is presented in a "positive and exciting tone." This content is then sent to the next refinement step.

[1568] Step 7:

[1569] The server adjusts the tone of the generated content. It is further refined by an emotion recognition engine, which makes fine adjustments to the tone if necessary. As a result, content that is more faithful to the user's emotions is generated. For example, more positive and engaging expressions are used, such as "Let us tell you about the unique features of our new product!"

[1570] Step 8:

[1571] The generated content is sent to the user's smartphone. The user reviews the generated content through the application and makes corrections as needed. The corrected content is then sent back to the system for final adjustments. In this way, high-quality content that matches the user's intentions and emotions is completed.

[1572] Through the above processing steps, the system of the present invention enables users to efficiently generate high-quality content and adjust the tone based on the user's emotions.

[1573] 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.

[1574] 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.

[1575] 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 robot 414.

[1576] 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.

[1577] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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."

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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 as being incorporated by reference.

[1594] The following is further disclosed regarding the embodiments described above.

[1595] (Claim 1)

[1596] A means of receiving prompts, emphasis points, and usage instructions from the user,

[1597] A means of uploading and analyzing past documents,

[1598] A means of training an AI model based on prompts and past data,

[1599] A means for generating materials based on prompts and emphasis points,

[1600] A means of providing the generated materials to the user,

[1601] A system that includes this.

[1602] (Claim 2)

[1603] The system according to claim 1, characterized in that the AI ​​model generates data using natural language processing.

[1604] (Claim 3)

[1605] The system according to claim 1, further comprising means for formatting documents based on patterns extracted from the aforementioned past documents.

[1606] "Example 1"

[1607] (Claim 1)

[1608] A means of receiving prompts, emphasis points, and usage instructions from the user,

[1609] A means of uploading and analyzing past documents,

[1610] A means of training a generative AI model based on prompts and past documents,

[1611] A means for generating materials based on prompts and emphasis points,

[1612] A means of providing the generated materials to the user,

[1613] A system that includes this.

[1614] (Claim 2)

[1615] The system according to claim 1, characterized in that the generative AI model generates documents using natural language processing.

[1616] (Claim 3)

[1617] The system according to claim 1, further comprising means for formatting a document based on patterns extracted from past documents.

[1618] "Application Example 1"

[1619] (Claim 1)

[1620] A means of receiving prompts, emphasis points, and usage instructions from the user,

[1621] A means of uploading and analyzing past documents,

[1622] A means of training an AI model based on prompts and past data,

[1623] A means for generating materials based on prompts and emphasis points,

[1624] A means of providing the generated materials to the user,

[1625] A means installed on the operating terminal of a machine,

[1626] A means of applying a specified format to the generated document,

[1627] A means for checking and correcting the generated data on the operating terminal of the machine,

[1628] A system that includes this.

[1629] (Claim 2)

[1630] The system according to claim 1, characterized in that the AI ​​model generates data using natural language processing.

[1631] (Claim 3)

[1632] The system according to claim 1, further comprising means for formatting documents based on patterns extracted from the aforementioned past documents.

[1633] "Example 2 of combining an emotion engine"

[1634] (Claim 1)

[1635] A means of receiving prompts, emphasis points, and usage instructions from the user,

[1636] A means of uploading and analyzing past documents,

[1637] A means of recognizing a user's emotions using an emotion engine,

[1638] A means of training a generative AI model based on prompts and past data,

[1639] A means for generating materials based on prompts and emphasis points,

[1640] A means of adjusting the content and tone of materials generated based on user emotions,

[1641] A means of providing the generated materials to the user,

[1642] A system that includes this.

[1643] (Claim 2)

[1644] The system according to claim 1, characterized in that the generation AI model generates data using natural language processing.

[1645] (Claim 3)

[1646] The system according to claim 1, further comprising means for formatting documents based on patterns extracted from the aforementioned past documents.

[1647] "Application example 2 when combining with an emotional engine"

[1648] (Claim 1)

[1649] A means of receiving prompts, emphasis points, and usage instructions from the user,

[1650] A means of uploading and analyzing past data,

[1651] A means of training an AI model based on prompts and historical data,

[1652] Means for generating information based on prompts and emphasis points,

[1653] A means for analyzing the user's emotions using an emotion recognition engine and adjusting the tone of the generated information,

[1654] A means of providing the generated information to the user and enabling them to review and correct it,

[1655] A system that includes this.

[1656] (Claim 2)

[1657] The system according to claim 1, characterized in that the AI ​​model generates information using natural language processing.

[1658] (Claim 3)

[1659] The system according to claim 1, further comprising means for formatting information based on patterns extracted from the aforementioned past data. [Explanation of Symbols]

[1660] 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 prompts, emphasis points, and usage instructions from the user, A means of uploading and analyzing past documents, A means of training an AI model based on prompts and past data, A means for generating materials based on prompts and emphasis points, A means of providing the generated materials to the user, A system that includes this.

2. The system according to claim 1, characterized in that the AI ​​model generates data using natural language processing.

3. The system according to claim 1, further comprising means for formatting documents based on patterns extracted from the aforementioned past documents.

Images