system

Abstract

We provide the system. [Solution] Means of obtaining RFC documents, A means for formatting the aforementioned RFC document for summary generation, Means for transmitting the formatted RFC document to a summary generation system, A means of receiving a summary from a summary generation system, Means for transmitting the aforementioned summary to the user terminal, A system that includes this.

Description

Technical Field

[0001] The technology disclosed herein 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 modern corporate activities, RFC (Request for Comments) documents aimed at standardizing Internet technology are very important information sources. However, these documents are generally very long and use a lot of technical terms, making them not easy to understand. As a result, many engineers and business personnel hesitate to refer to RFC documents, and as a result, there is a problem that the correct standardized knowledge does not sufficiently penetrate within the company. There is a need for a means to solve this problem and efficiently grasp the content of RFCs.

Means for Solving the Problems

[0005] To solve the above-mentioned problems, the present invention provides a system having the following features. First, it includes means for obtaining a specified RFC document from a data repository on the internet. Next, it includes means for formatting the obtained RFC document for summary generation and means for transmitting the formatted RFC document to a summary generation system. Furthermore, the summary generation system analyzes the RFC document using an artificial intelligence model and generates a summary. Finally, it includes means for transmitting the generated summary to a user terminal. In this way, users can efficiently and easily understand lengthy RFC documents, enabling standardized and correct knowledge to spread throughout the company.

[0006] An "RFC document" is a technical document officially published for the purpose of standardizing internet technologies.

[0007] "Means of acquisition" refers to a function or method for downloading a specific document from an internet data repository.

[0008] "Formatting means" refers to a function or method for converting acquired documents into a format suitable for summary generation.

[0009] A "summary generation system" is a system that analyzes text data, extracts its key points, and generates a concise summary.

[0010] "Means of transmission" refers to a function or method for transferring specific data to another system or device.

[0011] An "artificial intelligence model" is an algorithm or technology that learns from large amounts of data to find patterns and rules and generates an appropriate output for a specific input.

[0012] A "user terminal" is a device (e.g., a PC or smartphone) that a user can directly operate and use to receive and display information.

[0013] A "summary" is a short text that concisely summarizes the main points of the original document. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine.

Embodiment for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention relates to a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easily understandable summaries. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0036] In this system, the server first retrieves the specified RFC document from an internet data repository. This retrieval is performed using an HTTP request. Because the retrieved RFC document is very long, the server then formats it into a format suitable for summary generation. This includes deleting unnecessary parts of the document and extracting the necessary text information.

[0037] The formatted document is sent from the server to a summary generation system. This system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract key points, and generate a summary. Specifically, it identifies the important parts of the RFC document (e.g., definitions, procedures, and notes) and summarizes them into a concise text.

[0038] Once summary generation is complete, the generated summary is sent back to the server. The server receives this summary and then sends it to the user's device. The user's device includes devices such as PCs and smartphones. The user can easily view the generated summary through these devices.

[0039] Specific example

[0040] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a summarization generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0041] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0042] This summary is sent to the user's terminal via the server, allowing the user to review and deepen their understanding. In this way, the system can provide specialized content in a concise format and efficiently transmit standardized knowledge.

[0043] The above describes specific embodiments for carrying out the present invention.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0047] Step 2:

[0048] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0049] Step 3:

[0050] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0051] Step 4:

[0052] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0053] Step 5:

[0054] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0055] Step 6:

[0056] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0057] Step 7:

[0058] The server temporarily stores the received summary. Next, it prepares to send the summary to the user's terminal.

[0059] Step 8:

[0060] The server sends the saved summary to the user's terminal. This process allows the user to view the summary. Specifically, it uses methods such as HTTP POST requests.

[0061] Step 9:

[0062] The user reviews the summary text received on their device (PC, smartphone, etc.). Based on this summary text, the user can quickly understand the main points of the RFC.

[0063] The above is a description of the specific processing steps of the program.

[0064] (Example 1)

[0065] 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."

[0066] Technical documents, especially RFC documents, are often very lengthy and contain highly specialized content. Therefore, summarizing these documents in an easily understandable format is crucial. However, manual summarization is time-consuming, creating a system that efficiently and accurately generates summaries is essential.

[0067] 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.

[0068] In this invention, the server includes means for acquiring a specified technical document, means for formatting the technical document for summary generation, means for transmitting the formatted technical document to a natural language processing algorithm, means for receiving a summary from the natural language processing algorithm, and means for transmitting the summary to a user terminal. This makes it possible to efficiently perform the entire process from acquiring a technical document to generating a summary and distributing it.

[0069] A "technical document" is a document that contains information about a specific technology, including specific protocols and specifications.

[0070] "Summary generation" refers to the process of concisely summarizing a lengthy document and extracting only the main points.

[0071] "Natural language processing algorithms" refer to algorithms used to understand, analyze, and generate human language, and include technologies such as generative AI models.

[0072] A "generative AI model" refers to a model that uses machine learning or deep learning to learn knowledge from large amounts of text data and has the ability to generate new text.

[0073] "Formatting" refers to the process of removing unnecessary parts of a document, extracting necessary information, and converting it into a format suitable for summary generation.

[0074] A "summary" refers to a concise text that contains only the main points of the original document.

[0075] "User terminal" refers to devices such as computers, smartphones, and tablets used by the user.

[0076] The "Internet" refers to a system that connects computer networks to each other and allows for the exchange of information.

[0077] A "data repository" refers to a database or storage system used to store, manage, and provide specific data.

[0078] Modes for carrying out the invention

[0079] This invention relates to a system for efficiently and accurately summarizing technical documents. This system consists of "means for summarizing technical documents" and "means for generating and distributing the summary to users." This document describes the specific hardware and software configurations, as well as the methods for data processing and calculation.

[0080] First, the server retrieves the specified technical document (e.g., an RFC document) from a data repository on the internet. This is done using an HTTP request, and the technical document is received as a response. The retrieved technical document is then formatted by the server and converted into an appropriate format for summary generation. This formatting process includes removing unnecessary sections and extracting necessary information.

[0081] The formatted technical document is sent from the server to a generative AI model. This generative AI model uses natural language processing algorithms, such as GPT-4(registered trademark). The generative AI model analyzes the document, extracts the main points, and generates a concise summary.

[0082] The generated summary is sent to the user's device via the server. This device includes PCs, smartphones, and tablets. Users can easily review the generated summary through these devices.

[0083] Specific example

[0084] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its data repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a generative AI model (using a natural language processing algorithm), which generates a summary like the following:

[0085] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0086] This summary is sent to the user's terminal via the server. The user can then review it and deepen their understanding.

[0087] Example of a prompt

[0088] The following are examples of prompts to input into a generative AI model:

[0089] "Please summarize the RFC 2616 document. The summary should include key points such as how to make an HTTP / 1.1 request, status codes, and entity header information."

[0090] The above describes specific embodiments for carrying out the present invention.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Program processing flow

[0093] Step 1: Obtain the RFC document

[0094] The server retrieves the specified technical document from an internet data repository. The server accesses the specified URL using an HTTP request and downloads the technical document.

[0095] Input: URL of the specified technical document

[0096] Process: Send an HTTP GET request and retrieve the technical document as a response.

[0097] Output: Acquired technical documents (text data)

[0098] Specifically, the server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" and receives text data as a response.

[0099] Step 2: Formatting the document

[0100] The server formats the acquired technical documents for summary generation. This includes removing unnecessary parts of the document and extracting necessary text information.

[0101] Input: Acquired technical documents (text data)

[0102] Processing: Remove unnecessary sections, extract key sections, and format.

[0103] Output: Formatted technical document suitable for summary generation.

[0104] Specifically, the server identifies and removes sections such as the preface and bibliography from the retrieved document, and extracts important parts such as the HTTP request method, status code, and header information.

[0105] Step 3: Document submission for summary generation

[0106] The server sends the formatted technical document to the AI ​​model. The server then sends the data to the corresponding API endpoint.

[0107] Input: Formatted technical document (text data)

[0108] Processing: Convert the formatted document to JSON format and send it to the generating AI model via a POST request.

[0109] Output: POST request response (summary)

[0110] Specifically, the server includes the formatted document in JSON format when sending the API request.

[0111] For example:

[0112] POST / summarize

[0113] Host: ai-model-service.com

[0114] Content-Type: application / json

[0115] {

[0116] "document": "HTTP / 1.1... (formatted text)"

[0117] }

[0118] Step 4: Summary Generation

[0119] The generative AI model analyzes the submitted technical document and generates a summary.

[0120] Input: Pre-formatted document (JSON data)

[0121] Processing: Analysis and summary generation using a generative AI model.

[0122] Output: Summary text (text data)

[0123] Specifically, the generative AI model divides the document, extracts the main points, and generates a concise summary.

[0124] Step 5: Receiving the summary

[0125] The server receives the summary text generated from the AI ​​model and prepares for the next processing step.

[0126] Input: Summary text (JSON data)

[0127] Processing: Receive and save the response.

[0128] Output: Summary text (text data)

[0129] Specifically, the server receives the response from the generated AI model and saves the summary text to a database or file system.

[0130] Step 6: Sending the summary to the user's terminal

[0131] The server sends a summary to the user's terminal. Protocols such as REST API and WebSocket are used.

[0132] Input: Summary text (text data)

[0133] Processing: Sending data to the user terminal

[0134] Output: Summary text displayed on the user's terminal

[0135] Specifically, the server sends a summary text to the user's device via the API and confirms that the transmission is complete.

[0136] Step 7: View the summary

[0137] Users view the summary on their own devices. These devices include PCs, smartphones, and tablets.

[0138] Input: Summary text sent to the user terminal

[0139] Processing: Display and view summary text

[0140] Output: User understanding and confirmation

[0141] In terms of specific operations, the user receives a summary text through a dedicated app or web browser and reads it by scrolling through the main text.

[0142] Specific example

[0143] For example, in the case of RFC 2616 (HTTP / 1.1), the specific process is as follows:

[0144] 1. The server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" to retrieve the text of RFC 2616.

[0145] 2. The server removes unnecessary sections (such as the preface and bibliography) and formats only the main text.

[0146] 3. Send the formatted document to the AI ​​model in JSON format.

[0147] 4. The generative AI model extracts the key points and generates a summary sentence such as, "HTTP / 1.1 describes the format of requests and responses and the role of headers."

[0148] 5. The server receives the generated summary and saves it to the log.

[0149] 6. The server sends the summary text to the user's device via API.

[0150] 7. Users view the summary text and confirm its content through a smartphone app.

[0151] The above is the specific processing flow of the system program.

[0152] (Application Example 1)

[0153] 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."

[0154] RFC documents are extremely lengthy, requiring considerable time and effort to understand. Therefore, a more efficient means of providing information is needed to enable factory maintenance staff to quickly grasp technical standard documents and resolve problems.

[0155] 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.

[0156] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, and means for transmitting the summary to a target device. This enables maintenance staff in the factory to quickly check summaries of technical standard documents and efficiently resolve problems.

[0157] An "RFC document" is an official technical document aimed at standardizing internet technologies.

[0158] "Means of acquisition" refers to the process of accessing and downloading a specified document from an internet data repository.

[0159] "Formatting" refers to the process of removing unnecessary parts from an acquired document and processing it into a format suitable for summary generation.

[0160] A "summary generation system" is a system that uses specific algorithms or generative AI models to extract important information from long documents and summarize it concisely.

[0161] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze text data and perform semantic summarization and generation.

[0162] "Means of transmission" refers to the process of transferring formatted documents or summaries to other systems or terminals.

[0163] "Means of receiving" refers to the process of receiving data sent from another system or server.

[0164] A "user terminal" refers to a device that a user can directly operate, and includes smartphones, tablets, and personal computers.

[0165] A "target device" refers to a device that receives a summary text and displays or utilizes its contents. Examples of applications include robots operating in factories.

[0166] This invention is a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easy-to-understand summaries. This system operates through the coordinated efforts of a server, terminal, and user.

[0167] The server first retrieves the specified RFC document from an internet data repository using an HTTP request. For example, to retrieve RFC 2616 (HTTP / 1.1), the server downloads the document from the repository. Because this document is very long, the server then formats it into a format suitable for summary generation. This process involves removing unnecessary parts of the document and extracting the necessary text information (e.g., definitions, procedures, notes).

[0168] The formatted document is sent from the server to the summary generation system. This summary generation system uses a generative AI model (e.g., a BART model) to analyze the document, extract key points, and generate a summary. The summary generation system uses a tokenizer to tokenize the document and inputs it into the generative model to generate the summary. The resulting summary is then sent back to the server.

[0169] The server receives this summary and sends it to the user's terminal and the target equipment. The user's terminal includes devices such as PCs and smartphones, through which the user can easily view the generated summary. Furthermore, the summary is also sent to target equipment, such as robots operating in the factory, allowing maintenance staff to quickly refer to it.

[0170] As a concrete example, if a maintenance staff member in a factory instructs a robot to "show me the summary of RFC 2616," the robot will generate a summary and display it to the staff member. The following prompt is used for the generation AI model:

[0171] shell

[0172] Summarize the HTTP / 1.1 protocol as described in RFC 2616. Highlight the key points related to request methods, status codes, and header fields.

[0173] This allows maintenance staff within the factory to achieve faster work progress and problem resolution. The use of generative AI models enables highly advanced and efficient document analysis and summarization, facilitating the rapid dissemination of standardized knowledge.

[0174] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0175] Step 1:

[0176] The server retrieves a specified RFC document from an internet data repository using an HTTP request. The input is the URL of the RFC document, and the output is the text data of the retrieved RFC document. This text data is extremely long in its original form.

[0177] Step 2:

[0178] The server formats the retrieved RFC document into a format suitable for summary generation. This step removes unnecessary parts and extracts key text information. The input is the text data of the retrieved RFC document, and the output is the formatted text data. This formatting improves the efficiency of the summary generation system.

[0179] Step 3:

[0180] The server sends the formatted RFC document to the summarization generation system. The input is formatted text data, and the output is the formatted data that the summarization generation system receives. The server sends the data so that the summarization generation system can analyze it using a specified generative AI model.

[0181] Step 4:

[0182] The summary generation system analyzes formatted documents using a generative AI model and generates a summary. In this step, the model is input using specific prompt sentences. The input consists of formatted text data and prompt sentences, and the output is the generated summary. The data processing performed here involves tokenization of the text using a tokenizer and summary generation by the AI ​​model.

[0183] Step 5:

[0184] The summary generated by the summary generation system is sent to the server. The input is the generated summary, and the output is the summary received by the server. This completes the data needed for transfer to the user or target device.

[0185] Step 6:

[0186] The server sends a summary to the user terminal and the target device. The input is the generated summary, and the output is the summary received by the user terminal and the target device. This includes terminals that users access directly (smartphones, PCs) and robots operating in factories.

[0187] Step 7:

[0188] The user reviews the summary text generated through their terminal or the target device. The input is the summary text received by the terminal or device, and the output is the user's visual information. This allows factory maintenance staff to quickly obtain the necessary information and take appropriate action.

[0189] The above outlines the specific processing steps of the system required to realize the application examples.

[0190] 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.

[0191] This invention relates to a system for providing RFC documents, intended for the standardization of internet technologies, as efficient and easy-to-understand summaries. Furthermore, it relates to a system that facilitates user understanding by recognizing user emotions and adjusting the content or display method of the summary. This system operates in cooperation with a server, terminal, emotion engine, and user.

[0192] In this system, the server first retrieves the specified RFC document from an internet data repository. Retrieval is performed using an HTTP request, and the retrieved RFC document is saved in text format. Next, the server formats this document into a format suitable for summary generation. This formatting includes removing unnecessary metadata and extracting necessary text information.

[0193] The formatted document is sent from the server to the summary generation system. The summary generation system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The summary generated by the summary generation system is then sent back to the server.

[0194] The generated summary is then sent from the server to the user's terminal. The user's terminal is a device such as a PC or smartphone, and the summary is displayed to the user. It is worth noting that the user's terminal has the capability to collect emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice and collect emotional data.

[0195] The collected emotion data is sent to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state. For example, if the emotion engine determines that the user is confused, it processes the summary text to be more concise or to provide additional information. This regenerated summary text is then sent back from the server to the user's terminal.

[0196] Specific example

[0197] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0198] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0199] Once this summary is generated, the user's emotional state is then reflected. For example, if the user is perceived as confused, the emotion engine regenerates the summary in a more understandable form and resends it to the user's device. This additional step allows the user to receive information in a more easily comprehensible format.

[0200] The above describes specific embodiments for carrying out the present invention.

[0201] The following describes the processing flow.

[0202] Step 1:

[0203] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0204] Step 2:

[0205] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0206] Step 3:

[0207] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0208] Step 4:

[0209] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0210] Step 5:

[0211] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0212] Step 6:

[0213] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0214] Step 7:

[0215] The server temporarily stores the received summary text. Next, it prepares to send this summary text to the user's terminal in order to interact with the sentiment engine.

[0216] Step 8:

[0217] The user's device collects user emotion data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice, and generates emotion data from that analysis.

[0218] Step 9:

[0219] The user's device sends the collected emotional data to the emotion engine.

[0220] Step 10:

[0221] The emotion engine determines the user's current emotional state based on the emotional data it receives. For example, it analyzes whether the user is confused or not.

[0222] Step 11:

[0223] The emotion engine adjusts the summary to reflect the user's emotional state. If the user is confused, it regenerates the summary to be even more concise and provide additional information.

[0224] Step 12:

[0225] The emotion engine sends a summarized text, adjusted for emotional processing, to the server.

[0226] Step 13:

[0227] The server sends the revised summary to the user's terminal.

[0228] Step 14:

[0229] Users can view the regenerated summary on their own devices (PC, smartphone, etc.). This allows users to understand the summary in a way that is appropriate to their emotional state.

[0230] The above is a description of the specific processing steps of the program.

[0231] (Example 2)

[0232] 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".

[0233] RFC documents, aimed at standardizing internet technologies, are often difficult to understand due to their detailed nature, posing a burden to users. Furthermore, traditional summarization systems do not consider the user's level of understanding or emotional state, resulting in summaries that are not always easy for users to grasp. There is a need to address these challenges and enable users to understand RFC documents more efficiently.

[0234] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0235] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for the user terminal to collect user sentiment data, means for analyzing the sentiment data and adjusting the summary, and means for transmitting the regenerated summary back to the user terminal. This makes it possible for the user to view a summary optimized based on their sentiment state, thereby facilitating their understanding of the RFC document.

[0236] An "RFC document" is a technical document issued for the purpose of standardizing internet technologies.

[0237] A "summary generation system" is a system that analyzes RFC documents, extracts the main points, and generates a concise summary.

[0238] A "user terminal" refers to a device used by a user, including PCs and smartphones.

[0239] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, voice, and other factors.

[0240] An "emotion engine" is a system that analyzes emotional data to determine the user's current emotional state.

[0241] An "artificial intelligence model" is an algorithm that learns from large amounts of data and performs tasks such as text analysis and generation.

[0242] A "server" is a computer system that retrieves, processes, and transmits data.

[0243] This invention relates to a system for efficiently summarizing RFC documents aimed at standardizing internet technologies and adjusting the summary text based on the user's emotional state. This system operates in cooperation with a server, user terminal, emotion engine, and summary generation system. Specific embodiments are described below.

[0244] First, the server receives a request from the user for a specified RFC document. Next, the server retrieves the RFC document from an internet data repository using an HTTP request and saves it in text format. Specifically, it uses an HTTP client library such as "curl". The retrieved document is then converted to a specific format for text processing.

[0245] Next, the server formats the retrieved RFC document. Formatting includes removing unnecessary metadata and extracting important information (e.g., HTTP request method, status code, header information). Regular expressions are used, such as the Python "re" library, to remove unnecessary parts and extract the important parts.

[0246] The formatted document is sent by the server to the summary generation system. The summary generation system analyzes the document using a generative AI model (e.g., BERT or GPT), extracts the main points, and generates a summary. This summary generation can be performed using an external API request.

[0247] The summary generated by the summary generation system is sent back to the server, which then transmits it to the user's terminal. User terminals include PCs and smartphones, and these devices display the summary to the user.

[0248] Next, the user's device uses its camera and microphone to collect emotional data such as the user's facial expressions and voice. For example, emotional data is collected using technologies such as OpenCV (camera) or Google Cloud Speech-to-Text API (voice). The collected emotional data is then sent to the emotion engine.

[0249] The emotion engine analyzes received emotion data to determine the user's current emotional state. For example, it uses deep learning models such as TENSORFLOW® to perform image recognition and speech analysis. Based on the emotional state, the emotion engine regenerates the summary text in a more easily understandable format.

[0250] The regenerated summary is sent back to the user's terminal via the server and displayed to the user. This process allows the user to view a summary optimized based on their emotional state, thereby facilitating their understanding of the RFC document.

[0251] Specific example

[0252] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document and extracts and formats the important parts of the document (e.g., HTTP request methods, status codes, header information, etc.). The formatted document is sent to the summary generation system, which generates a summary sentence such as, "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST), and entity headers."

[0253] This summary is sent to the user's device and displayed. Furthermore, if the sentiment engine determines that the user is confused, it regenerates the summary to be more concise or to provide additional information, and sends it to the user's device again to facilitate user understanding.

[0254] Example of a prompt

[0255] "Please summarize the RFC 2616 (HTTP / 1.1) document. Include key points such as the HTTP request method, status codes, and header information."

[0256] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0257] Step 1: The server receives the RFC document request.

[0258] Specific operation: The server receives a request from the user. It retrieves the identification information (e.g., RFC number) of the RFC document requested by the user.

[0259] Input: User's RFC document request, RFC number

[0260] Output: Request information including RFC number

[0261] Step 2: The server retrieves the RFC document from an internet data repository.

[0262] Specific operation: The server generates an HTTP request and retrieves the RFC document corresponding to the specified RFC number from the data repository. The retrieved document is saved in text format.

[0263] Input: RFC number

[0264] Output: RFC document text

[0265] Step 3: The server formats the RFC document.

[0266] Specific operation: The server opens the retrieved RFC document and removes unnecessary metadata. Next, it extracts important parts such as the HTTP request method, status code, and header information. This is processed using regular expressions with Python's "re" library, for example.

[0267] Input: RFC document text

[0268] Output: Formatted text information

[0269] Step 4: The server sends the formatted document to the summary generation system.

[0270] Specific operation: The server sends the formatted document to the summarization generation system using an API request (e.g., an HTTP POST request).

[0271] Input: Formatted text information

[0272] Output: Summary generation request

[0273] Step 5: The summary generation system generates a summary.

[0274] Specific operation: The summarization generation system uses a generative AI model (e.g., BERT, GPT) to analyze documents, extract key points, and generate a summary.

[0275] Input: Formatted text information

[0276] Output: Generated summary

[0277] Step 6: The server receives the summary from the summary generation system.

[0278] Specific operation: The server receives the summary text generated from the summary generation system.

[0279] Input: Generated summary

[0280] Output: Abstract

[0281] Step 7: The server sends the abstract to the user terminal

[0282] Specific operation: The server uses WebSocket or HTTP requests to send the generated abstract to the user terminal.

[0283] Input: Abstract

[0284] Output: Abstract sent to the user terminal

[0285] Step 8: The user terminal receives and displays the abstract

[0286] Specific operation: The user terminal displays the received abstract on the screen.

[0287] Input: Abstract sent from the server

[0288] Output: Displayed abstract

[0289] Step 9: The user terminal collects the user's emotion data

[0290] Specific operation: The user terminal uses a camera or microphone to collect the user's expressions and voices. Specifically, it uses OpenCV, Google Cloud Speech-to-Text API, etc.

[0291] Input: The user's expression and voice data

[0292] Output: Emotion data

[0293] Step 10: The user terminal sends the emotion data to the emotion engine

[0294] Specific operation: The user terminal sends the collected emotion data to the emotion engine via the network.

[0295] Input: Emotional data

[0296] Output: Emotional data sent to the emotion engine

[0297] Step 11: The emotion engine analyzes the emotional data to determine the emotional state

[0298] Specific operation: The emotion engine analyzes the received emotional data and uses a deep learning model (e.g., TensorFlow) to determine the user's emotional state

[0299] Input: Emotional data

[0300] Output: The user's emotional state

[0301] Step 12: The emotion engine adjusts the summary text

[0302] Specific operation: The emotion engine regenerates or adjusts the summary text based on the user's emotional state

[0303] Input: The generated summary text, the user's emotional state

[0304] Output: The regenerated or adjusted summary text

[0305] Step 13: The server resends the regenerated summary text to the user terminal

[0306] Specific operation: The server resends the regenerated summary text sent from the emotion engine to the user terminal

[0307] Input: The regenerated summary text

[0308] Output: The regenerated summary text sent to the user terminal

[0309] Step 14: The user terminal receives and displays the regenerated summary text

[0310] Specific operation: The user terminal receives the regenerated summary and displays it on the screen.

[0311] Input: Regenerated summary

[0312] Output: Displayed regenerated summary

[0313] (Application Example 2)

[0314] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0315] Currently, there is a demand for quick and efficient understanding of technical documents and standards documents (e.g., RFC documents), but this is often difficult, especially for users unfamiliar with technology. In addition, there are very few systems that provide interactive feedback that responds to the user's emotional state. As a result, when users become confused, appropriate responses are not taken, making it even more difficult to understand the information.

[0316] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for collecting user emotion data from the user terminal, means for analyzing the emotion data to determine the user's emotional state, and means for adjusting the summary based on the user's emotional state. This makes it possible not only to make technical documents easier for the user to understand, but also to provide appropriate feedback that corresponds to the user's emotions.

[0317] An "RFC document" is a technical document aimed at standardizing internet technologies, describing specifications related to communication protocols and internet-related technologies.

[0318] "Summary generation" is the process of extracting the key points from a document and concisely describing the original information.

[0319] A "summary generation system" is a system that analyzes an original document, extracts the main points, and generates a summary.

[0320] A "server" is a computer system used for acquiring, formatting, transmitting, and receiving data.

[0321] A "user terminal" refers to a computer or mobile device operated by the user that displays the summary text.

[0322] "Emotional data" refers to data about a user's emotional state obtained by analyzing their facial expressions and voice.

[0323] An "emotion engine" is a software component that analyzes collected emotional data to determine the user's emotional state.

[0324] "Formatting" refers to the process of converting a document into a format suitable for summary generation, such as removing unnecessary metadata and extracting necessary text information.

[0325] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and has the ability to understand and generate documents like a human.

[0326] A "prompt" is an instruction or question given as input to a generative AI model.

[0327] This invention relates to a system for users to quickly and easily obtain RFC documents in an understandable format. For this purpose, a server, a user terminal, and a generative AI model work in conjunction.

[0328] First, the server retrieves the specified RFC document from a data repository on the internet. This is done using an HTTP request, and the document is stored in text format. Next, the server formats the retrieved document. This formatting includes removing unnecessary metadata and extracting the necessary text information.

[0329] The formatted RFC document is sent from the server to the summary generation system. The summary generation system uses a generative AI model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The generated summary is then sent back to the server.

[0330] Next, the server sends the generated summary to the user's terminal. The user terminal is equipped with devices such as a camera and microphone to collect user emotion data. For example, it analyzes the user's facial expressions and voice to determine their emotional state.

[0331] Emotional data is sent from the user's terminal to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state (e.g., confused, satisfied). Based on the determined emotional state, the server adjusts the summary text. For example, if the server determines that the user is confused, it will either make the summary text clearer or regenerate it by providing additional information.

[0332] The refined summary is then sent back from the server to the user's terminal and displayed to the user. This process allows users to easily understand the technical document and obtain additional information as needed.

[0333] Specific example

[0334] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where a generation AI model analyzes it to generate a summary like the following:

[0335] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0336] If the user's emotional state is detected as confused while reading this summary, the sentiment engine will regenerate the summary in a more understandable form and adjust it as follows:

[0337] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers. Additional information: A detailed explanation can be found at https: / / ."

[0338] Example of a prompt

[0339] "How can I generate a summary of RFC 2616 and provide additional information if the user is confused?"

[0340] In this way, the system can help users understand the situation and provide real-time, emotion-responsive feedback.

[0341] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0342] Step 1:

[0343] The server retrieves the specified RFC document from an internet data repository. It sends an HTTP request based on the RFC document number entered by the user and retrieves the corresponding document in text format. The input is the RFC document number, and the output is the text data of the retrieved RFC document.

[0344] Step 2:

[0345] The server formats the retrieved RFC document for summary generation. Unnecessary metadata is removed from the document, and important text information such as request method, status code, and header information is extracted. This process ensures efficient summary generation. The input is the text data of the retrieved RFC document, and the output is the formatted text data.

[0346] Step 3:

[0347] The server sends the formatted RFC document to the summarization generation system. The summarization generation system uses a generative AI model to analyze the document, extract key points, and generate a summary. In this process, the generative AI model is given prompt sentences as input and the summarization result is received as output. The input is the formatted text data and prompt sentences, and the output is the generated summary.

[0348] Step 4:

[0349] The server sends the generated summary to the user terminal. At this time, the user terminal prepares to display the received summary. The input is the summary, and the output is the completion of sending the summary to the user terminal.

[0350] Step 5:

[0351] The user's device uses its camera and microphone to collect user emotion data. While the user reads the summary, the device analyzes the user's facial expressions and voice to collect data for determining their emotional state. The input is the user's facial expressions and voice, and the output is the collected emotion data.

[0352] Step 6:

[0353] The user terminal sends collected emotional data to the emotion engine, which then analyzes it. Based on the emotional data, the emotion engine determines the user's emotional state and identifies whether it is confused, satisfied, focused, etc. The input is the collected emotional data, and the output is the determined emotional state.

[0354] Step 7:

[0355] The server adjusts the summary based on the user's emotional state, as determined by the emotion engine. For example, if the server determines that the user is confused, it regenerates the summary to make it clearer or provides additional information. This process makes the information easier for the user to understand. The input is the determined emotional state and the original summary, and the output is the adjusted summary.

[0356] Step 8:

[0357] The server sends the adjusted summary back to the user terminal, which then displays the summary. This allows the user to view a summary tailored to their situation in real time. The input is the adjusted summary, and the output is the transmission and display of the summary to the user terminal.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] [Second Embodiment]

[0362] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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".

[0374] This invention relates to a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easily understandable summaries. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0375] In this system, the server first retrieves the specified RFC document from an internet data repository. This retrieval is performed using an HTTP request. Because the retrieved RFC document is very long, the server then formats it into a format suitable for summary generation. This includes deleting unnecessary parts of the document and extracting the necessary text information.

[0376] The formatted document is sent from the server to a summary generation system. This system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract key points, and generate a summary. Specifically, it identifies the important parts of the RFC document (e.g., definitions, procedures, and notes) and summarizes them into a concise text.

[0377] Once summary generation is complete, the generated summary is sent back to the server. The server receives this summary and then sends it to the user's device. The user's device includes devices such as PCs and smartphones. The user can easily view the generated summary through these devices.

[0378] Specific example

[0379] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a summarization generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0380] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0381] This summary is sent to the user's terminal via the server, allowing the user to review and deepen their understanding. In this way, the system can provide specialized content in a concise format and efficiently transmit standardized knowledge.

[0382] The above describes specific embodiments for carrying out the present invention.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0386] Step 2:

[0387] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0388] Step 3:

[0389] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0390] Step 4:

[0391] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0392] Step 5:

[0393] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0394] Step 6:

[0395] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0396] Step 7:

[0397] The server temporarily stores the received summary. Next, it prepares to send the summary to the user's terminal.

[0398] Step 8:

[0399] The server sends the saved summary to the user's terminal. This process allows the user to view the summary. Specifically, it uses methods such as HTTP POST requests.

[0400] Step 9:

[0401] The user reviews the summary text received on their device (PC, smartphone, etc.). Based on this summary text, the user can quickly understand the main points of the RFC.

[0402] The above is a description of the specific processing steps of the program.

[0403] (Example 1)

[0404] 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."

[0405] Technical documents, especially RFC documents, are often very lengthy and contain highly specialized content. Therefore, summarizing these documents in an easily understandable format is crucial. However, manual summarization is time-consuming, creating a system that efficiently and accurately generates summaries is essential.

[0406] 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.

[0407] In this invention, the server includes means for acquiring a specified technical document, means for formatting the technical document for summary generation, means for transmitting the formatted technical document to a natural language processing algorithm, means for receiving a summary from the natural language processing algorithm, and means for transmitting the summary to a user terminal. This makes it possible to efficiently perform the entire process from acquiring a technical document to generating a summary and distributing it.

[0408] A "technical document" is a document that contains information about a specific technology, including specific protocols and specifications.

[0409] "Summary generation" refers to the process of concisely summarizing a lengthy document and extracting only the main points.

[0410] "Natural language processing algorithms" refer to algorithms used to understand, analyze, and generate human language, and include technologies such as generative AI models.

[0411] A "generative AI model" refers to a model that uses machine learning or deep learning to learn knowledge from large amounts of text data and has the ability to generate new text.

[0412] "Formatting" refers to the process of removing unnecessary parts of a document, extracting necessary information, and converting it into a format suitable for summary generation.

[0413] A "summary" refers to a concise text that contains only the main points of the original document.

[0414] "User terminal" refers to devices such as computers, smartphones, and tablets used by the user.

[0415] The "Internet" refers to a system that connects computer networks to each other and allows for the exchange of information.

[0416] A "data repository" refers to a database or storage system used to store, manage, and provide specific data.

[0417] Modes for carrying out the invention

[0418] This invention relates to a system for efficiently and accurately summarizing technical documents. This system consists of "means for summarizing technical documents" and "means for generating and distributing the summary to users." This document describes the specific hardware and software configurations, as well as the methods for data processing and calculation.

[0419] First, the server retrieves the specified technical document (e.g., an RFC document) from a data repository on the internet. This is done using an HTTP request, and the technical document is received as a response. The retrieved technical document is then formatted by the server and converted into an appropriate format for summary generation. This formatting process includes removing unnecessary sections and extracting necessary information.

[0420] The formatted technical document is sent from the server to a generative AI model. This generative AI model uses natural language processing algorithms, such as GPT-4. The generative AI model analyzes the document, extracts the main points, and generates a concise summary.

[0421] The generated summary is sent to the user's device via the server. This device includes PCs, smartphones, and tablets. Users can easily review the generated summary through these devices.

[0422] Specific example

[0423] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its data repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a generative AI model (using a natural language processing algorithm), which generates a summary like the following:

[0424] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0425] This summary is sent to the user's terminal via the server. The user can then review it and deepen their understanding.

[0426] Example of a prompt

[0427] The following are examples of prompts to input into a generative AI model:

[0428] "Please summarize the RFC 2616 document. The summary should include key points such as how to make an HTTP / 1.1 request, status codes, and entity header information."

[0429] The above describes specific embodiments for carrying out the present invention.

[0430] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0431] Program processing flow

[0432] Step 1: Obtain the RFC document

[0433] The server retrieves the specified technical document from an internet data repository. The server accesses the specified URL using an HTTP request and downloads the technical document.

[0434] Input: URL of the specified technical document

[0435] Process: Send an HTTP GET request and retrieve the technical document as a response.

[0436] Output: Acquired technical documents (text data)

[0437] Specifically, the server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" and receives text data as a response.

[0438] Step 2: Formatting the document

[0439] The server formats the acquired technical documents for summary generation. This includes removing unnecessary parts of the document and extracting necessary text information.

[0440] Input: Acquired technical documents (text data)

[0441] Processing: Remove unnecessary sections, extract key sections, and format.

[0442] Output: Formatted technical document suitable for summary generation.

[0443] Specifically, the server identifies and removes sections such as the preface and bibliography from the retrieved document, and extracts important parts such as the HTTP request method, status code, and header information.

[0444] Step 3: Document submission for summary generation

[0445] The server sends the formatted technical document to the AI ​​model. The server then sends the data to the corresponding API endpoint.

[0446] Input: Formatted technical document (text data)

[0447] Processing: Convert the formatted document to JSON format and send it to the generating AI model via a POST request.

[0448] Output: POST request response (summary)

[0449] Specifically, the server includes the formatted document in JSON format when sending the API request.

[0450] For example:

[0451] POST / summarize

[0452] Host: ai-model-service.com

[0453] Content-Type: application / json

[0454] {

[0455] "document": "HTTP / 1.1... (formatted text)"

[0456] }

[0457] Step 4: Summary Generation

[0458] The generative AI model analyzes the submitted technical document and generates a summary.

[0459] Input: Pre-formatted document (JSON data)

[0460] Processing: Analysis and summary generation using a generative AI model.

[0461] Output: Summary text (text data)

[0462] Specifically, the generative AI model divides the document, extracts the main points, and generates a concise summary.

[0463] Step 5: Receiving the summary

[0464] The server receives the summary text generated from the AI ​​model and prepares for the next processing step.

[0465] Input: Summary text (JSON data)

[0466] Processing: Receive and save the response.

[0467] Output: Summary text (text data)

[0468] Specifically, the server receives the response from the generated AI model and saves the summary text to a database or file system.

[0469] Step 6: Sending the summary to the user's terminal

[0470] The server sends a summary to the user's terminal. Protocols such as REST API and WebSocket are used.

[0471] Input: Summary text (text data)

[0472] Processing: Sending data to the user terminal

[0473] Output: Summary text displayed on the user's terminal

[0474] Specifically, the server sends a summary text to the user's device via the API and confirms that the transmission is complete.

[0475] Step 7: View the summary

[0476] Users view the summary on their own devices. These devices include PCs, smartphones, and tablets.

[0477] Input: Summary text sent to the user terminal

[0478] Processing: Display and view summary text

[0479] Output: User understanding and confirmation

[0480] In terms of specific operations, the user receives a summary text through a dedicated app or web browser and reads it by scrolling through the main text.

[0481] Specific example

[0482] For example, in the case of RFC 2616 (HTTP / 1.1), the specific process is as follows:

[0483] 1. The server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" to retrieve the text of RFC 2616.

[0484] 2. The server removes unnecessary sections (such as the preface and bibliography) and formats only the main text.

[0485] 3. Send the formatted document to the AI ​​model in JSON format.

[0486] 4. The generative AI model extracts the key points and generates a summary sentence such as, "HTTP / 1.1 describes the format of requests and responses and the role of headers."

[0487] 5. The server receives the generated summary and saves it to the log.

[0488] 6. The server sends the summary text to the user's device via API.

[0489] 7. Users view the summary text and confirm its content through a smartphone app.

[0490] The above is the specific processing flow of the system program.

[0491] (Application Example 1)

[0492] 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."

[0493] RFC documents are extremely lengthy, requiring considerable time and effort to understand. Therefore, a more efficient means of providing information is needed to enable factory maintenance staff to quickly grasp technical standard documents and resolve problems.

[0494] 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.

[0495] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, and means for transmitting the summary to a target device. This enables maintenance staff in the factory to quickly check summaries of technical standard documents and efficiently resolve problems.

[0496] An "RFC document" is an official technical document aimed at standardizing internet technologies.

[0497] "Means of acquisition" refers to the process of accessing and downloading a specified document from an internet data repository.

[0498] "Formatting" refers to the process of removing unnecessary parts from an acquired document and processing it into a format suitable for summary generation.

[0499] A "summary generation system" is a system that uses specific algorithms or generative AI models to extract important information from long documents and summarize it concisely.

[0500] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze text data and perform semantic summarization and generation.

[0501] "Means of transmission" refers to the process of transferring formatted documents or summaries to other systems or terminals.

[0502] "Means of receiving" refers to the process of receiving data sent from another system or server.

[0503] A "user terminal" refers to a device that a user can directly operate, and includes smartphones, tablets, and personal computers.

[0504] A "target device" refers to a device that receives a summary text and displays or utilizes its contents. Examples of applications include robots operating in factories.

[0505] This invention is a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easy-to-understand summaries. This system operates through the coordinated efforts of a server, terminal, and user.

[0506] The server first retrieves the specified RFC document from an internet data repository using an HTTP request. For example, to retrieve RFC 2616 (HTTP / 1.1), the server downloads the document from the repository. Because this document is very long, the server then formats it into a format suitable for summary generation. This process involves removing unnecessary parts of the document and extracting the necessary text information (e.g., definitions, procedures, notes).

[0507] The formatted document is sent from the server to the summary generation system. This summary generation system uses a generative AI model (e.g., a BART model) to analyze the document, extract key points, and generate a summary. The summary generation system uses a tokenizer to tokenize the document and inputs it into the generative model to generate the summary. The resulting summary is then sent back to the server.

[0508] The server receives this summary and sends it to the user's terminal and the target equipment. The user's terminal includes devices such as PCs and smartphones, through which the user can easily view the generated summary. Furthermore, the summary is also sent to target equipment, such as robots operating in the factory, allowing maintenance staff to quickly refer to it.

[0509] As a concrete example, if a maintenance staff member in a factory instructs a robot to "show me the summary of RFC 2616," the robot will generate a summary and display it to the staff member. The following prompt is used for the generation AI model:

[0510] shell

[0511] Summarize the HTTP / 1.1 protocol as described in RFC 2616. Highlight the key points related to request methods, status codes, and header fields.

[0512] This allows maintenance staff within the factory to achieve faster work progress and problem resolution. The use of generative AI models enables highly advanced and efficient document analysis and summarization, facilitating the rapid dissemination of standardized knowledge.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The server retrieves a specified RFC document from an internet data repository using an HTTP request. The input is the URL of the RFC document, and the output is the text data of the retrieved RFC document. This text data is extremely long in its original form.

[0516] Step 2:

[0517] The server formats the retrieved RFC document into a format suitable for summary generation. This step removes unnecessary parts and extracts key text information. The input is the text data of the retrieved RFC document, and the output is the formatted text data. This formatting improves the efficiency of the summary generation system.

[0518] Step 3:

[0519] The server sends the formatted RFC document to the summarization generation system. The input is formatted text data, and the output is the formatted data that the summarization generation system receives. The server sends the data so that the summarization generation system can analyze it using a specified generative AI model.

[0520] Step 4:

[0521] The summary generation system analyzes formatted documents using a generative AI model and generates a summary. In this step, the model is input using specific prompt sentences. The input consists of formatted text data and prompt sentences, and the output is the generated summary. The data processing performed here involves tokenization of the text using a tokenizer and summary generation by the AI ​​model.

[0522] Step 5:

[0523] The summary generated by the summary generation system is sent to the server. The input is the generated summary, and the output is the summary received by the server. This completes the data needed for transfer to the user or target device.

[0524] Step 6:

[0525] The server sends a summary to the user terminal and the target device. The input is the generated summary, and the output is the summary received by the user terminal and the target device. This includes terminals that users access directly (smartphones, PCs) and robots operating in factories.

[0526] Step 7:

[0527] The user reviews the summary text generated through their terminal or the target device. The input is the summary text received by the terminal or device, and the output is the user's visual information. This allows factory maintenance staff to quickly obtain the necessary information and take appropriate action.

[0528] The above outlines the specific processing steps of the system required to realize the application examples.

[0529] 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.

[0530] This invention relates to a system for providing RFC documents, intended for the standardization of internet technologies, as efficient and easy-to-understand summaries. Furthermore, it relates to a system that facilitates user understanding by recognizing user emotions and adjusting the content or display method of the summary. This system operates in cooperation with a server, terminal, emotion engine, and user.

[0531] In this system, the server first retrieves the specified RFC document from an internet data repository. Retrieval is performed using an HTTP request, and the retrieved RFC document is saved in text format. Next, the server formats this document into a format suitable for summary generation. This formatting includes removing unnecessary metadata and extracting necessary text information.

[0532] The formatted document is sent from the server to the summary generation system. The summary generation system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The summary generated by the summary generation system is then sent back to the server.

[0533] The generated summary is then sent from the server to the user's terminal. The user's terminal is a device such as a PC or smartphone, and the summary is displayed to the user. It is worth noting that the user's terminal has the capability to collect emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice and collect emotional data.

[0534] The collected emotion data is sent to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state. For example, if the emotion engine determines that the user is confused, it processes the summary text to be more concise or to provide additional information. This regenerated summary text is then sent back from the server to the user's terminal.

[0535] Specific example

[0536] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0537] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0538] Once this summary is generated, the user's emotional state is then reflected. For example, if the user is perceived as confused, the emotion engine regenerates the summary in a more understandable form and resends it to the user's device. This additional step allows the user to receive information in a more easily comprehensible format.

[0539] The above describes specific embodiments for carrying out the present invention.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0543] Step 2:

[0544] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0545] Step 3:

[0546] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0547] Step 4:

[0548] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0549] Step 5:

[0550] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0551] Step 6:

[0552] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0553] Step 7:

[0554] The server temporarily stores the received summary text. Next, it prepares to send this summary text to the user's terminal in order to interact with the sentiment engine.

[0555] Step 8:

[0556] The user's device collects user emotion data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice, and generates emotion data from that analysis.

[0557] Step 9:

[0558] The user's device sends the collected emotional data to the emotion engine.

[0559] Step 10:

[0560] The emotion engine determines the user's current emotional state based on the emotional data it receives. For example, it analyzes whether the user is confused or not.

[0561] Step 11:

[0562] The emotion engine adjusts the summary to reflect the user's emotional state. If the user is confused, it regenerates the summary to be even more concise and provide additional information.

[0563] Step 12:

[0564] The emotion engine sends a summarized text, adjusted for emotional processing, to the server.

[0565] Step 13:

[0566] The server sends the revised summary to the user's terminal.

[0567] Step 14:

[0568] Users can view the regenerated summary on their own devices (PC, smartphone, etc.). This allows users to understand the summary in a way that is appropriate to their emotional state.

[0569] The above is a description of the specific processing steps of the program.

[0570] (Example 2)

[0571] 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".

[0572] RFC documents, aimed at standardizing internet technologies, are often difficult to understand due to their detailed nature, posing a burden to users. Furthermore, traditional summarization systems do not consider the user's level of understanding or emotional state, resulting in summaries that are not always easy for users to grasp. There is a need to address these challenges and enable users to understand RFC documents more efficiently.

[0573] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0574] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for the user terminal to collect user sentiment data, means for analyzing the sentiment data and adjusting the summary, and means for transmitting the regenerated summary back to the user terminal. This makes it possible for the user to view a summary optimized based on their sentiment state, thereby facilitating their understanding of the RFC document.

[0575] An "RFC document" is a technical document issued for the purpose of standardizing internet technologies.

[0576] A "summary generation system" is a system that analyzes RFC documents, extracts the main points, and generates a concise summary.

[0577] A "user terminal" refers to a device used by a user, including PCs and smartphones.

[0578] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, voice, and other factors.

[0579] An "emotion engine" is a system that analyzes emotional data to determine the user's current emotional state.

[0580] An "artificial intelligence model" is an algorithm that learns from large amounts of data and performs tasks such as text analysis and generation.

[0581] A "server" is a computer system that retrieves, processes, and transmits data.

[0582] This invention relates to a system for efficiently summarizing RFC documents aimed at standardizing internet technologies and adjusting the summary text based on the user's emotional state. This system operates in cooperation with a server, user terminal, emotion engine, and summary generation system. Specific embodiments are described below.

[0583] First, the server receives a request from the user for a specified RFC document. Next, the server retrieves the RFC document from an internet data repository using an HTTP request and saves it in text format. Specifically, it uses an HTTP client library such as "curl". The retrieved document is then converted to a specific format for text processing.

[0584] Next, the server formats the retrieved RFC document. Formatting includes removing unnecessary metadata and extracting important information (e.g., HTTP request method, status code, header information). Regular expressions are used, such as the Python "re" library, to remove unnecessary parts and extract the important parts.

[0585] The formatted document is sent by the server to the summary generation system. The summary generation system analyzes the document using a generative AI model (e.g., BERT or GPT), extracts the main points, and generates a summary. This summary generation can be performed using an external API request.

[0586] The summary generated by the summary generation system is sent back to the server, which then transmits it to the user's terminal. User terminals include PCs and smartphones, and these devices display the summary to the user.

[0587] Next, the user's device uses its camera and microphone to collect emotional data such as the user's facial expressions and voice. For example, it uses technologies such as OpenCV (camera) or Google Cloud Speech-to-Text API (voice) to collect emotional data. The collected emotional data is then sent to the emotion engine.

[0588] The emotion engine analyzes received emotion data to determine the user's current emotional state. For example, it uses deep learning models such as TensorFlow to perform image recognition and speech analysis. Based on the emotional state, the emotion engine regenerates the summary text in a more easily understandable format.

[0589] The regenerated summary is sent back to the user's terminal via the server and displayed to the user. This process allows the user to view a summary optimized based on their emotional state, thereby facilitating their understanding of the RFC document.

[0590] Specific example

[0591] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document and extracts and formats the important parts of the document (e.g., HTTP request methods, status codes, header information, etc.). The formatted document is sent to the summary generation system, which generates a summary sentence such as, "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST), and entity headers."

[0592] This summary is sent to the user's device and displayed. Furthermore, if the sentiment engine determines that the user is confused, it regenerates the summary to be more concise or to provide additional information, and sends it to the user's device again to facilitate user understanding.

[0593] Example of a prompt

[0594] "Please summarize the RFC 2616 (HTTP / 1.1) document. Include key points such as the HTTP request method, status codes, and header information."

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1: The server receives the RFC document request.

[0597] Specific operation: The server receives a request from the user. It retrieves the identification information (e.g., RFC number) of the RFC document requested by the user.

[0598] Input: User's RFC document request, RFC number

[0599] Output: Request information including RFC number

[0600] Step 2: The server retrieves the RFC document from an internet data repository.

[0601] Specific operation: The server generates an HTTP request and retrieves the RFC document corresponding to the specified RFC number from the data repository. The retrieved document is saved in text format.

[0602] Input: RFC number

[0603] Output: RFC document text

[0604] Step 3: The server formats the RFC document.

[0605] Specific operation: The server opens the retrieved RFC document and removes unnecessary metadata. Next, it extracts important parts such as the HTTP request method, status code, and header information. This is processed using regular expressions with Python's "re" library, for example.

[0606] Input: RFC document text

[0607] Output: Formatted text information

[0608] Step 4: The server sends the formatted document to the summary generation system.

[0609] Specific operation: The server sends the formatted document to the summarization generation system using an API request (e.g., an HTTP POST request).

[0610] Input: Formatted text information

[0611] Output: Summary generation request

[0612] Step 5: The summary generation system generates a summary.

[0613] Specific operation: The summarization generation system uses a generative AI model (e.g., BERT, GPT) to analyze documents, extract key points, and generate a summary.

[0614] Input: Formatted text information

[0615] Output: Generated summary

[0616] Step 6: The server receives the summary from the summary generation system.

[0617] Specific operation: The server receives the summary text generated from the summary generation system.

[0618] Input: Generated summary

[0619] Output: Summary

[0620] Step 7: The server sends the summary to the user's terminal.

[0621] Specific operation: The server uses WebSocket or HTTP requests to send the generated summary text to the user's terminal.

[0622] Input: Summary

[0623] Output: Summary text sent to the user's terminal

[0624] Step 8: The user terminal receives and displays the summary.

[0625] Specific action: The user terminal displays the received summary text on the screen.

[0626] Input: Summary text sent from the server

[0627] Output: Displayed summary

[0628] Step 9: The user device collects user sentiment data.

[0629] Specific operation: The user's device uses its camera and microphone to collect the user's facial expressions and voice. Specifically, it uses tools such as OpenCV and the Google Cloud Speech-to-Text API.

[0630] Input: User's facial expressions and voice data

[0631] Output: Sentiment data

[0632] Step 10: The user device sends emotion data to the emotion engine.

[0633] Specific operation: The user terminal transmits the collected emotional data to the emotion engine via the network.

[0634] Input: Sentiment data

[0635] Output: Emotional data sent to the emotion engine

[0636] Step 11: The emotion engine analyzes emotional data to determine the emotional state.

[0637] Specific operation: The emotion engine analyzes the received emotion data and uses a deep learning model (e.g., TensorFlow) to determine the user's emotional state.

[0638] Input: Sentiment data

[0639] Output: User's emotional state

[0640] Step 12: The emotion engine adjusts the summary.

[0641] Specific operation: The emotion engine regenerates or adjusts the summary text based on the user's emotional state.

[0642] Input: Generated summary, user's emotional state

[0643] Output: Regenerated or adjusted summary

[0644] Step 13: The server resends the regenerated summary to the user terminal.

[0645] Specific operation: The server resends the regenerated summary text sent from the emotion engine to the user's terminal.

[0646] Input: Regenerated summary

[0647] Output: Regenerated summary sent to the user terminal

[0648] Step 14: The user terminal receives and displays the regenerated summary.

[0649] Specific operation: The user terminal receives the regenerated summary and displays it on the screen.

[0650] Input: Regenerated summary

[0651] Output: Displayed regenerated summary

[0652] (Application Example 2)

[0653] 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."

[0654] Currently, there is a demand for quick and efficient understanding of technical documents and standards documents (e.g., RFC documents), but this is often difficult, especially for users unfamiliar with technology. In addition, there are very few systems that provide interactive feedback that responds to the user's emotional state. As a result, when users become confused, appropriate responses are not taken, making it even more difficult to understand the information.

[0655] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for collecting user emotion data from the user terminal, means for analyzing the emotion data to determine the user's emotional state, and means for adjusting the summary based on the user's emotional state. This makes it possible not only to make technical documents easier for the user to understand, but also to provide appropriate feedback that corresponds to the user's emotions.

[0656] An "RFC document" is a technical document aimed at standardizing internet technologies, describing specifications related to communication protocols and internet-related technologies.

[0657] "Summary generation" is the process of extracting the key points from a document and concisely describing the original information.

[0658] A "summary generation system" is a system that analyzes an original document, extracts the main points, and generates a summary.

[0659] A "server" is a computer system used for acquiring, formatting, transmitting, and receiving data.

[0660] A "user terminal" refers to a computer or mobile device operated by the user that displays the summary text.

[0661] "Emotional data" refers to data about a user's emotional state obtained by analyzing their facial expressions and voice.

[0662] An "emotion engine" is a software component that analyzes collected emotional data to determine the user's emotional state.

[0663] "Formatting" refers to the process of converting a document into a format suitable for summary generation, such as removing unnecessary metadata and extracting necessary text information.

[0664] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and has the ability to understand and generate documents like a human.

[0665] A "prompt" is an instruction or question given as input to a generative AI model.

[0666] This invention relates to a system for users to quickly and easily obtain RFC documents in an understandable format. For this purpose, a server, a user terminal, and a generative AI model work in conjunction.

[0667] First, the server retrieves the specified RFC document from a data repository on the internet. This is done using an HTTP request, and the document is stored in text format. Next, the server formats the retrieved document. This formatting includes removing unnecessary metadata and extracting the necessary text information.

[0668] The formatted RFC document is sent from the server to the summary generation system. The summary generation system uses a generative AI model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The generated summary is then sent back to the server.

[0669] Next, the server sends the generated summary to the user's terminal. The user terminal is equipped with devices such as a camera and microphone to collect user emotion data. For example, it analyzes the user's facial expressions and voice to determine their emotional state.

[0670] Emotional data is sent from the user's terminal to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state (e.g., confused, satisfied). Based on the determined emotional state, the server adjusts the summary text. For example, if the server determines that the user is confused, it will either make the summary text clearer or regenerate it by providing additional information.

[0671] The refined summary is then sent back from the server to the user's terminal and displayed to the user. This process allows users to easily understand the technical document and obtain additional information as needed.

[0672] Specific example

[0673] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where a generation AI model analyzes it to generate a summary like the following:

[0674] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0675] If the user's emotional state is detected as confused while reading this summary, the sentiment engine will regenerate the summary in a more understandable form and adjust it as follows:

[0676] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers. Additional information: A detailed explanation can be found at https: / / ."

[0677] Example of a prompt

[0678] "How can I generate a summary of RFC 2616 and provide additional information if the user is confused?"

[0679] In this way, the system can help users understand the situation and provide real-time, emotion-responsive feedback.

[0680] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0681] Step 1:

[0682] The server retrieves the specified RFC document from an internet data repository. It sends an HTTP request based on the RFC document number entered by the user and retrieves the corresponding document in text format. The input is the RFC document number, and the output is the text data of the retrieved RFC document.

[0683] Step 2:

[0684] The server formats the retrieved RFC document for summary generation. Unnecessary metadata is removed from the document, and important text information such as request method, status code, and header information is extracted. This process ensures efficient summary generation. The input is the text data of the retrieved RFC document, and the output is the formatted text data.

[0685] Step 3:

[0686] The server sends the formatted RFC document to the summarization generation system. The summarization generation system uses a generative AI model to analyze the document, extract key points, and generate a summary. In this process, the generative AI model is given prompt sentences as input and the summarization result is received as output. The input is the formatted text data and prompt sentences, and the output is the generated summary.

[0687] Step 4:

[0688] The server sends the generated summary to the user terminal. At this time, the user terminal prepares to display the received summary. The input is the summary, and the output is the completion of sending the summary to the user terminal.

[0689] Step 5:

[0690] The user's device uses its camera and microphone to collect user emotion data. While the user reads the summary, the device analyzes the user's facial expressions and voice to collect data for determining their emotional state. The input is the user's facial expressions and voice, and the output is the collected emotion data.

[0691] Step 6:

[0692] The user terminal sends collected emotional data to the emotion engine, which then analyzes it. Based on the emotional data, the emotion engine determines the user's emotional state and identifies whether it is confused, satisfied, focused, etc. The input is the collected emotional data, and the output is the determined emotional state.

[0693] Step 7:

[0694] The server adjusts the summary based on the user's emotional state, as determined by the emotion engine. For example, if the server determines that the user is confused, it regenerates the summary to make it clearer or provides additional information. This process makes the information easier for the user to understand. The input is the determined emotional state and the original summary, and the output is the adjusted summary.

[0695] Step 8:

[0696] The server sends the adjusted summary back to the user terminal, which then displays the summary. This allows the user to view a summary tailored to their situation in real time. The input is the adjusted summary, and the output is the transmission and display of the summary to the user terminal.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] [Third Embodiment]

[0701] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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).

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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".

[0713] This invention relates to a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easily understandable summaries. This system operates through the coordinated efforts of a server, a terminal, and a user.

[0714] In this system, the server first retrieves the specified RFC document from an internet data repository. This retrieval is performed using an HTTP request. Because the retrieved RFC document is very long, the server then formats it into a format suitable for summary generation. This includes deleting unnecessary parts of the document and extracting the necessary text information.

[0715] The formatted document is sent from the server to a summary generation system. This system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract key points, and generate a summary. Specifically, it identifies the important parts of the RFC document (e.g., definitions, procedures, and notes) and summarizes them into a concise text.

[0716] Once summary generation is complete, the generated summary is sent back to the server. The server receives this summary and then sends it to the user's device. The user's device includes devices such as PCs and smartphones. The user can easily view the generated summary through these devices.

[0717] Specific example

[0718] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a summarization generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0719] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0720] This summary is sent to the user's terminal via the server, allowing the user to review and deepen their understanding. In this way, the system can provide specialized content in a concise format and efficiently transmit standardized knowledge.

[0721] The above describes specific embodiments for carrying out the present invention.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0725] Step 2:

[0726] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0727] Step 3:

[0728] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0729] Step 4:

[0730] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0731] Step 5:

[0732] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0733] Step 6:

[0734] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0735] Step 7:

[0736] The server temporarily stores the received summary. Next, it prepares to send the summary to the user's terminal.

[0737] Step 8:

[0738] The server sends the saved summary to the user's terminal. This process allows the user to view the summary. Specifically, it uses methods such as HTTP POST requests.

[0739] Step 9:

[0740] The user reviews the summary text received on their device (PC, smartphone, etc.). Based on this summary text, the user can quickly understand the main points of the RFC.

[0741] The above is a description of the specific processing steps of the program.

[0742] (Example 1)

[0743] 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."

[0744] Technical documents, especially RFC documents, are often very lengthy and contain highly specialized content. Therefore, summarizing these documents in an easily understandable format is crucial. However, manual summarization is time-consuming, creating a system that efficiently and accurately generates summaries is essential.

[0745] 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.

[0746] In this invention, the server includes means for acquiring a specified technical document, means for formatting the technical document for summary generation, means for transmitting the formatted technical document to a natural language processing algorithm, means for receiving a summary from the natural language processing algorithm, and means for transmitting the summary to a user terminal. This makes it possible to efficiently perform the entire process from acquiring a technical document to generating a summary and distributing it.

[0747] A "technical document" is a document that contains information about a specific technology, including specific protocols and specifications.

[0748] "Summary generation" refers to the process of concisely summarizing a lengthy document and extracting only the main points.

[0749] "Natural language processing algorithms" refer to algorithms used to understand, analyze, and generate human language, and include technologies such as generative AI models.

[0750] A "generative AI model" refers to a model that uses machine learning or deep learning to learn knowledge from large amounts of text data and has the ability to generate new text.

[0751] "Formatting" refers to the process of removing unnecessary parts of a document, extracting necessary information, and converting it into a format suitable for summary generation.

[0752] A "summary" refers to a concise text that contains only the main points of the original document.

[0753] "User terminal" refers to devices such as computers, smartphones, and tablets used by the user.

[0754] The "Internet" refers to a system that connects computer networks to each other and allows for the exchange of information.

[0755] A "data repository" refers to a database or storage system used to store, manage, and provide specific data.

[0756] Modes for carrying out the invention

[0757] This invention relates to a system for efficiently and accurately summarizing technical documents. This system consists of "means for summarizing technical documents" and "means for generating and distributing the summary to users." This document describes the specific hardware and software configurations, as well as the methods for data processing and calculation.

[0758] First, the server retrieves the specified technical document (e.g., an RFC document) from a data repository on the internet. This is done using an HTTP request, and the technical document is received as a response. The retrieved technical document is then formatted by the server and converted into an appropriate format for summary generation. This formatting process includes removing unnecessary sections and extracting necessary information.

[0759] The formatted technical document is sent from the server to a generative AI model. This generative AI model uses natural language processing algorithms, such as GPT-4. The generative AI model analyzes the document, extracts the main points, and generates a concise summary.

[0760] The generated summary is sent to the user's device via the server. This device includes PCs, smartphones, and tablets. Users can easily review the generated summary through these devices.

[0761] Specific example

[0762] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its data repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a generative AI model (using a natural language processing algorithm), which generates a summary like the following:

[0763] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0764] This summary is sent to the user's terminal via the server. The user can then review it and deepen their understanding.

[0765] Example of a prompt

[0766] The following are examples of prompts to input into a generative AI model:

[0767] "Please summarize the RFC 2616 document. The summary should include key points such as how to make an HTTP / 1.1 request, status codes, and entity header information."

[0768] The above describes specific embodiments for carrying out the present invention.

[0769] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0770] Program processing flow

[0771] Step 1: Obtain the RFC document

[0772] The server retrieves the specified technical document from an internet data repository. The server accesses the specified URL using an HTTP request and downloads the technical document.

[0773] Input: URL of the specified technical document

[0774] Process: Send an HTTP GET request and retrieve the technical document as a response.

[0775] Output: Acquired technical documents (text data)

[0776] Specifically, the server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" and receives text data as a response.

[0777] Step 2: Formatting the document

[0778] The server formats the acquired technical documents for summary generation. This includes removing unnecessary parts of the document and extracting necessary text information.

[0779] Input: Acquired technical documents (text data)

[0780] Processing: Remove unnecessary sections, extract key sections, and format.

[0781] Output: Formatted technical document suitable for summary generation.

[0782] Specifically, the server identifies and removes sections such as the preface and bibliography from the retrieved document, and extracts important parts such as the HTTP request method, status code, and header information.

[0783] Step 3: Document submission for summary generation

[0784] The server sends the formatted technical document to the AI ​​model. The server then sends the data to the corresponding API endpoint.

[0785] Input: Formatted technical document (text data)

[0786] Processing: Convert the formatted document to JSON format and send it to the generating AI model via a POST request.

[0787] Output: POST request response (summary)

[0788] Specifically, the server includes the formatted document in JSON format when sending the API request.

[0789] For example:

[0790] POST / summarize

[0791] Host: ai-model-service.com

[0792] Content-Type: application / json

[0793] {

[0794] "document": "HTTP / 1.1... (formatted text)"

[0795] }

[0796] Step 4: Summary Generation

[0797] The generative AI model analyzes the submitted technical document and generates a summary.

[0798] Input: Pre-formatted document (JSON data)

[0799] Processing: Analysis and summary generation using a generative AI model.

[0800] Output: Summary text (text data)

[0801] Specifically, the generative AI model divides the document, extracts the main points, and generates a concise summary.

[0802] Step 5: Receiving the summary

[0803] The server receives the summary text generated from the AI ​​model and prepares for the next processing step.

[0804] Input: Summary text (JSON data)

[0805] Processing: Receive and save the response.

[0806] Output: Summary text (text data)

[0807] Specifically, the server receives the response from the generated AI model and saves the summary text to a database or file system.

[0808] Step 6: Sending the summary to the user's terminal

[0809] The server sends a summary to the user's terminal. Protocols such as REST API and WebSocket are used.

[0810] Input: Summary text (text data)

[0811] Processing: Sending data to the user terminal

[0812] Output: Summary text displayed on the user's terminal

[0813] Specifically, the server sends a summary text to the user's device via the API and confirms that the transmission is complete.

[0814] Step 7: View the summary

[0815] Users view the summary on their own devices. These devices include PCs, smartphones, and tablets.

[0816] Input: Summary text sent to the user terminal

[0817] Processing: Display and view summary text

[0818] Output: User understanding and confirmation

[0819] In terms of specific operations, the user receives a summary text through a dedicated app or web browser and reads it by scrolling through the main text.

[0820] Specific example

[0821] For example, in the case of RFC 2616 (HTTP / 1.1), the specific process is as follows:

[0822] 1. The server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" to retrieve the text of RFC 2616.

[0823] 2. The server removes unnecessary sections (such as the preface and bibliography) and formats only the main text.

[0824] 3. Send the formatted document to the AI ​​model in JSON format.

[0825] 4. The generative AI model extracts the key points and generates a summary sentence such as, "HTTP / 1.1 describes the format of requests and responses and the role of headers."

[0826] 5. The server receives the generated summary and saves it to the log.

[0827] 6. The server sends the summary text to the user's device via API.

[0828] 7. Users view the summary text and confirm its content through a smartphone app.

[0829] The above is the specific processing flow of the system program.

[0830] (Application Example 1)

[0831] 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."

[0832] RFC documents are extremely lengthy, requiring considerable time and effort to understand. Therefore, a more efficient means of providing information is needed to enable factory maintenance staff to quickly grasp technical standard documents and resolve problems.

[0833] 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.

[0834] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, and means for transmitting the summary to a target device. This enables maintenance staff in the factory to quickly check summaries of technical standard documents and efficiently resolve problems.

[0835] An "RFC document" is an official technical document aimed at standardizing internet technologies.

[0836] "Means of acquisition" refers to the process of accessing and downloading a specified document from an internet data repository.

[0837] "Formatting" refers to the process of removing unnecessary parts from an acquired document and processing it into a format suitable for summary generation.

[0838] A "summary generation system" is a system that uses specific algorithms or generative AI models to extract important information from long documents and summarize it concisely.

[0839] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze text data and perform semantic summarization and generation.

[0840] "Means of transmission" refers to the process of transferring formatted documents or summaries to other systems or terminals.

[0841] "Means of receiving" refers to the process of receiving data sent from another system or server.

[0842] A "user terminal" refers to a device that a user can directly operate, and includes smartphones, tablets, and personal computers.

[0843] A "target device" refers to a device that receives a summary text and displays or utilizes its contents. Examples of applications include robots operating in factories.

[0844] This invention is a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easy-to-understand summaries. This system operates through the coordinated efforts of a server, terminal, and user.

[0845] The server first retrieves the specified RFC document from an internet data repository using an HTTP request. For example, to retrieve RFC 2616 (HTTP / 1.1), the server downloads the document from the repository. Because this document is very long, the server then formats it into a format suitable for summary generation. This process involves removing unnecessary parts of the document and extracting the necessary text information (e.g., definitions, procedures, notes).

[0846] The formatted document is sent from the server to the summary generation system. This summary generation system uses a generative AI model (e.g., a BART model) to analyze the document, extract key points, and generate a summary. The summary generation system uses a tokenizer to tokenize the document and inputs it into the generative model to generate the summary. The resulting summary is then sent back to the server.

[0847] The server receives this summary and sends it to the user's terminal and the target equipment. The user's terminal includes devices such as PCs and smartphones, through which the user can easily view the generated summary. Furthermore, the summary is also sent to target equipment, such as robots operating in the factory, allowing maintenance staff to quickly refer to it.

[0848] As a concrete example, if a maintenance staff member in a factory instructs a robot to "show me the summary of RFC 2616," the robot will generate a summary and display it to the staff member. The following prompt is used for the generation AI model:

[0849] shell

[0850] Summarize the HTTP / 1.1 protocol as described in RFC 2616. Highlight the key points related to request methods, status codes, and header fields.

[0851] This allows maintenance staff within the factory to achieve faster work progress and problem resolution. The use of generative AI models enables highly advanced and efficient document analysis and summarization, facilitating the rapid dissemination of standardized knowledge.

[0852] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0853] Step 1:

[0854] The server retrieves a specified RFC document from an internet data repository using an HTTP request. The input is the URL of the RFC document, and the output is the text data of the retrieved RFC document. This text data is extremely long in its original form.

[0855] Step 2:

[0856] The server formats the retrieved RFC document into a format suitable for summary generation. This step removes unnecessary parts and extracts key text information. The input is the text data of the retrieved RFC document, and the output is the formatted text data. This formatting improves the efficiency of the summary generation system.

[0857] Step 3:

[0858] The server sends the formatted RFC document to the summarization generation system. The input is formatted text data, and the output is the formatted data that the summarization generation system receives. The server sends the data so that the summarization generation system can analyze it using a specified generative AI model.

[0859] Step 4:

[0860] The summary generation system analyzes formatted documents using a generative AI model and generates a summary. In this step, the model is input using specific prompt sentences. The input consists of formatted text data and prompt sentences, and the output is the generated summary. The data processing performed here involves tokenization of the text using a tokenizer and summary generation by the AI ​​model.

[0861] Step 5:

[0862] The summary generated by the summary generation system is sent to the server. The input is the generated summary, and the output is the summary received by the server. This completes the data needed for transfer to the user or target device.

[0863] Step 6:

[0864] The server sends a summary to the user terminal and the target device. The input is the generated summary, and the output is the summary received by the user terminal and the target device. This includes terminals that users access directly (smartphones, PCs) and robots operating in factories.

[0865] Step 7:

[0866] The user reviews the summary text generated through their terminal or the target device. The input is the summary text received by the terminal or device, and the output is the user's visual information. This allows factory maintenance staff to quickly obtain the necessary information and take appropriate action.

[0867] The above outlines the specific processing steps of the system required to realize the application examples.

[0868] 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.

[0869] This invention relates to a system for providing RFC documents, intended for the standardization of internet technologies, as efficient and easy-to-understand summaries. Furthermore, it relates to a system that facilitates user understanding by recognizing user emotions and adjusting the content or display method of the summary. This system operates in cooperation with a server, terminal, emotion engine, and user.

[0870] In this system, the server first retrieves the specified RFC document from an internet data repository. Retrieval is performed using an HTTP request, and the retrieved RFC document is saved in text format. Next, the server formats this document into a format suitable for summary generation. This formatting includes removing unnecessary metadata and extracting necessary text information.

[0871] The formatted document is sent from the server to the summary generation system. The summary generation system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The summary generated by the summary generation system is then sent back to the server.

[0872] The generated summary is then sent from the server to the user's terminal. The user's terminal is a device such as a PC or smartphone, and the summary is displayed to the user. It is worth noting that the user's terminal has the capability to collect emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice and collect emotional data.

[0873] The collected emotion data is sent to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state. For example, if the emotion engine determines that the user is confused, it processes the summary text to be more concise or to provide additional information. This regenerated summary text is then sent back from the server to the user's terminal.

[0874] Specific example

[0875] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[0876] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[0877] Once this summary is generated, the user's emotional state is then reflected. For example, if the user is perceived as confused, the emotion engine regenerates the summary in a more understandable form and resends it to the user's device. This additional step allows the user to receive information in a more easily comprehensible format.

[0878] The above describes specific embodiments for carrying out the present invention.

[0879] The following describes the processing flow.

[0880] Step 1:

[0881] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[0882] Step 2:

[0883] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[0884] Step 3:

[0885] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[0886] Step 4:

[0887] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[0888] Step 5:

[0889] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[0890] Step 6:

[0891] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[0892] Step 7:

[0893] The server temporarily stores the received summary text. Next, it prepares to send this summary text to the user's terminal in order to interact with the sentiment engine.

[0894] Step 8:

[0895] The user's device collects user emotion data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice, and generates emotion data from that analysis.

[0896] Step 9:

[0897] The user's device sends the collected emotional data to the emotion engine.

[0898] Step 10:

[0899] The emotion engine determines the user's current emotional state based on the emotional data it receives. For example, it analyzes whether the user is confused or not.

[0900] Step 11:

[0901] The emotion engine adjusts the summary to reflect the user's emotional state. If the user is confused, it regenerates the summary to be even more concise and provide additional information.

[0902] Step 12:

[0903] The emotion engine sends a summarized text, adjusted for emotional processing, to the server.

[0904] Step 13:

[0905] The server sends the revised summary to the user's terminal.

[0906] Step 14:

[0907] Users can view the regenerated summary on their own devices (PC, smartphone, etc.). This allows users to understand the summary in a way that is appropriate to their emotional state.

[0908] The above is a description of the specific processing steps of the program.

[0909] (Example 2)

[0910] 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."

[0911] RFC documents, aimed at standardizing internet technologies, are often difficult to understand due to their detailed nature, posing a burden to users. Furthermore, traditional summarization systems do not consider the user's level of understanding or emotional state, resulting in summaries that are not always easy for users to grasp. There is a need to address these challenges and enable users to understand RFC documents more efficiently.

[0912] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0913] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for the user terminal to collect user sentiment data, means for analyzing the sentiment data and adjusting the summary, and means for transmitting the regenerated summary back to the user terminal. This makes it possible for the user to view a summary optimized based on their sentiment state, thereby facilitating their understanding of the RFC document.

[0914] An "RFC document" is a technical document issued for the purpose of standardizing internet technologies.

[0915] A "summary generation system" is a system that analyzes RFC documents, extracts the main points, and generates a concise summary.

[0916] A "user terminal" refers to a device used by a user, including PCs and smartphones.

[0917] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, voice, and other factors.

[0918] An "emotion engine" is a system that analyzes emotional data to determine the user's current emotional state.

[0919] An "artificial intelligence model" is an algorithm that learns from large amounts of data and performs tasks such as text analysis and generation.

[0920] A "server" is a computer system that retrieves, processes, and transmits data.

[0921] This invention relates to a system for efficiently summarizing RFC documents aimed at standardizing internet technologies and adjusting the summary text based on the user's emotional state. This system operates in cooperation with a server, user terminal, emotion engine, and summary generation system. Specific embodiments are described below.

[0922] First, the server receives a request from the user for a specified RFC document. Next, the server retrieves the RFC document from an internet data repository using an HTTP request and saves it in text format. Specifically, it uses an HTTP client library such as "curl". The retrieved document is then converted to a specific format for text processing.

[0923] Next, the server formats the retrieved RFC document. Formatting includes removing unnecessary metadata and extracting important information (e.g., HTTP request method, status code, header information). Regular expressions are used, such as the Python "re" library, to remove unnecessary parts and extract the important parts.

[0924] The formatted document is sent by the server to the summary generation system. The summary generation system analyzes the document using a generative AI model (e.g., BERT or GPT), extracts the main points, and generates a summary. This summary generation can be performed using an external API request.

[0925] The summary generated by the summary generation system is sent back to the server, which then transmits it to the user's terminal. User terminals include PCs and smartphones, and these devices display the summary to the user.

[0926] Next, the user's device uses its camera and microphone to collect emotional data such as the user's facial expressions and voice. For example, it uses technologies such as OpenCV (camera) or Google Cloud Speech-to-Text API (voice) to collect emotional data. The collected emotional data is then sent to the emotion engine.

[0927] The emotion engine analyzes received emotion data to determine the user's current emotional state. For example, it uses deep learning models such as TensorFlow to perform image recognition and speech analysis. Based on the emotional state, the emotion engine regenerates the summary text in a more easily understandable format.

[0928] The regenerated summary is sent back to the user's terminal via the server and displayed to the user. This process allows the user to view a summary optimized based on their emotional state, thereby facilitating their understanding of the RFC document.

[0929] Specific example

[0930] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document and extracts and formats the important parts of the document (e.g., HTTP request methods, status codes, header information, etc.). The formatted document is sent to the summary generation system, which generates a summary sentence such as, "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST), and entity headers."

[0931] This summary is sent to the user's device and displayed. Furthermore, if the sentiment engine determines that the user is confused, it regenerates the summary to be more concise or to provide additional information, and sends it to the user's device again to facilitate user understanding.

[0932] Example of a prompt

[0933] "Please summarize the RFC 2616 (HTTP / 1.1) document. Include key points such as the HTTP request method, status codes, and header information."

[0934] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0935] Step 1: The server receives the RFC document request.

[0936] Specific operation: The server receives a request from the user. It retrieves the identification information (e.g., RFC number) of the RFC document requested by the user.

[0937] Input: User's RFC document request, RFC number

[0938] Output: Request information including RFC number

[0939] Step 2: The server retrieves the RFC document from an internet data repository.

[0940] Specific operation: The server generates an HTTP request and retrieves the RFC document corresponding to the specified RFC number from the data repository. The retrieved document is saved in text format.

[0941] Input: RFC number

[0942] Output: RFC document text

[0943] Step 3: The server formats the RFC document.

[0944] Specific operation: The server opens the retrieved RFC document and removes unnecessary metadata. Next, it extracts important parts such as the HTTP request method, status code, and header information. This is processed using regular expressions with Python's "re" library, for example.

[0945] Input: RFC document text

[0946] Output: Formatted text information

[0947] Step 4: The server sends the formatted document to the summary generation system.

[0948] Specific operation: The server sends the formatted document to the summarization generation system using an API request (e.g., an HTTP POST request).

[0949] Input: Formatted text information

[0950] Output: Summary generation request

[0951] Step 5: The summary generation system generates a summary.

[0952] Specific operation: The summarization generation system uses a generative AI model (e.g., BERT, GPT) to analyze documents, extract key points, and generate a summary.

[0953] Input: Formatted text information

[0954] Output: Generated summary

[0955] Step 6: The server receives the summary from the summary generation system.

[0956] Specific operation: The server receives the summary text generated from the summary generation system.

[0957] Input: Generated summary

[0958] Output: Summary

[0959] Step 7: The server sends the summary to the user's terminal.

[0960] Specific operation: The server uses WebSocket or HTTP requests to send the generated summary text to the user's terminal.

[0961] Input: Summary

[0962] Output: Summary text sent to the user's terminal

[0963] Step 8: The user terminal receives and displays the summary.

[0964] Specific action: The user terminal displays the received summary text on the screen.

[0965] Input: Summary text sent from the server

[0966] Output: Displayed summary

[0967] Step 9: The user device collects user sentiment data.

[0968] Specific operation: The user's device uses its camera and microphone to collect the user's facial expressions and voice. Specifically, it uses tools such as OpenCV and the Google Cloud Speech-to-Text API.

[0969] Input: User's facial expressions and voice data

[0970] Output: Sentiment data

[0971] Step 10: The user device sends emotion data to the emotion engine.

[0972] Specific operation: The user terminal transmits the collected emotional data to the emotion engine via the network.

[0973] Input: Sentiment data

[0974] Output: Emotional data sent to the emotion engine

[0975] Step 11: The emotion engine analyzes emotional data to determine the emotional state.

[0976] Specific operation: The emotion engine analyzes the received emotion data and uses a deep learning model (e.g., TensorFlow) to determine the user's emotional state.

[0977] Input: Sentiment data

[0978] Output: User's emotional state

[0979] Step 12: The emotion engine adjusts the summary.

[0980] Specific operation: The emotion engine regenerates or adjusts the summary text based on the user's emotional state.

[0981] Input: Generated summary, user's emotional state

[0982] Output: Regenerated or adjusted summary

[0983] Step 13: The server resends the regenerated summary to the user terminal.

[0984] Specific operation: The server resends the regenerated summary text sent from the emotion engine to the user's terminal.

[0985] Input: Regenerated summary

[0986] Output: Regenerated summary sent to the user terminal

[0987] Step 14: The user terminal receives and displays the regenerated summary.

[0988] Specific operation: The user terminal receives the regenerated summary and displays it on the screen.

[0989] Input: Regenerated summary

[0990] Output: Displayed regenerated summary

[0991] (Application Example 2)

[0992] 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."

[0993] Currently, there is a demand for quick and efficient understanding of technical documents and standards documents (e.g., RFC documents), but this is often difficult, especially for users unfamiliar with technology. In addition, there are very few systems that provide interactive feedback that responds to the user's emotional state. As a result, when users become confused, appropriate responses are not taken, making it even more difficult to understand the information.

[0994] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for collecting user emotion data from the user terminal, means for analyzing the emotion data to determine the user's emotional state, and means for adjusting the summary based on the user's emotional state. This makes it possible not only to make technical documents easier for the user to understand, but also to provide appropriate feedback that corresponds to the user's emotions.

[0995] An "RFC document" is a technical document aimed at standardizing internet technologies, describing specifications related to communication protocols and internet-related technologies.

[0996] "Summary generation" is the process of extracting the key points from a document and concisely describing the original information.

[0997] A "summary generation system" is a system that analyzes an original document, extracts the main points, and generates a summary.

[0998] A "server" is a computer system used for acquiring, formatting, transmitting, and receiving data.

[0999] A "user terminal" refers to a computer or mobile device operated by the user that displays the summary text.

[1000] "Emotional data" refers to data about a user's emotional state obtained by analyzing their facial expressions and voice.

[1001] An "emotion engine" is a software component that analyzes collected emotional data to determine the user's emotional state.

[1002] "Formatting" refers to the process of converting a document into a format suitable for summary generation, such as removing unnecessary metadata and extracting necessary text information.

[1003] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and has the ability to understand and generate documents like a human.

[1004] A "prompt" is an instruction or question given as input to a generative AI model.

[1005] This invention relates to a system for users to quickly and easily obtain RFC documents in an understandable format. For this purpose, a server, a user terminal, and a generative AI model work in conjunction.

[1006] First, the server retrieves the specified RFC document from a data repository on the internet. This is done using an HTTP request, and the document is stored in text format. Next, the server formats the retrieved document. This formatting includes removing unnecessary metadata and extracting the necessary text information.

[1007] The formatted RFC document is sent from the server to the summary generation system. The summary generation system uses a generative AI model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The generated summary is then sent back to the server.

[1008] Next, the server sends the generated summary to the user's terminal. The user terminal is equipped with devices such as a camera and microphone to collect user emotion data. For example, it analyzes the user's facial expressions and voice to determine their emotional state.

[1009] Emotional data is sent from the user's terminal to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state (e.g., confused, satisfied). Based on the determined emotional state, the server adjusts the summary text. For example, if the server determines that the user is confused, it will either make the summary text clearer or regenerate it by providing additional information.

[1010] The refined summary is then sent back from the server to the user's terminal and displayed to the user. This process allows users to easily understand the technical document and obtain additional information as needed.

[1011] Specific example

[1012] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where a generation AI model analyzes it to generate a summary like the following:

[1013] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[1014] If the user's emotional state is detected as confused while reading this summary, the sentiment engine will regenerate the summary in a more understandable form and adjust it as follows:

[1015] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers. Additional information: A detailed explanation can be found at https: / / ."

[1016] Example of a prompt

[1017] "How can I generate a summary of RFC 2616 and provide additional information if the user is confused?"

[1018] In this way, the system can help users understand the situation and provide real-time, emotion-responsive feedback.

[1019] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1020] Step 1:

[1021] The server retrieves the specified RFC document from an internet data repository. It sends an HTTP request based on the RFC document number entered by the user and retrieves the corresponding document in text format. The input is the RFC document number, and the output is the text data of the retrieved RFC document.

[1022] Step 2:

[1023] The server formats the retrieved RFC document for summary generation. Unnecessary metadata is removed from the document, and important text information such as request method, status code, and header information is extracted. This process ensures efficient summary generation. The input is the text data of the retrieved RFC document, and the output is the formatted text data.

[1024] Step 3:

[1025] The server sends the formatted RFC document to the summarization generation system. The summarization generation system uses a generative AI model to analyze the document, extract key points, and generate a summary. In this process, the generative AI model is given prompt sentences as input and the summarization result is received as output. The input is the formatted text data and prompt sentences, and the output is the generated summary.

[1026] Step 4:

[1027] The server sends the generated summary to the user terminal. At this time, the user terminal prepares to display the received summary. The input is the summary, and the output is the completion of sending the summary to the user terminal.

[1028] Step 5:

[1029] The user's device uses its camera and microphone to collect user emotion data. While the user reads the summary, the device analyzes the user's facial expressions and voice to collect data for determining their emotional state. The input is the user's facial expressions and voice, and the output is the collected emotion data.

[1030] Step 6:

[1031] The user terminal sends collected emotional data to the emotion engine, which then analyzes it. Based on the emotional data, the emotion engine determines the user's emotional state and identifies whether it is confused, satisfied, focused, etc. The input is the collected emotional data, and the output is the determined emotional state.

[1032] Step 7:

[1033] The server adjusts the summary based on the user's emotional state, as determined by the emotion engine. For example, if the server determines that the user is confused, it regenerates the summary to make it clearer or provides additional information. This process makes the information easier for the user to understand. The input is the determined emotional state and the original summary, and the output is the adjusted summary.

[1034] Step 8:

[1035] The server sends the adjusted summary back to the user terminal, which then displays the summary. This allows the user to view a summary tailored to their situation in real time. The input is the adjusted summary, and the output is the transmission and display of the summary to the user terminal.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] [Fourth Embodiment]

[1040] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1041] 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.

[1042] 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).

[1043] 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.

[1044] 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.

[1045] 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).

[1046] 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.

[1047] 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.

[1048] 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.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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".

[1053] This invention relates to a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easily understandable summaries. This system operates through the coordinated efforts of a server, a terminal, and a user.

[1054] In this system, the server first retrieves the specified RFC document from an internet data repository. This retrieval is performed using an HTTP request. Because the retrieved RFC document is very long, the server then formats it into a format suitable for summary generation. This includes deleting unnecessary parts of the document and extracting the necessary text information.

[1055] The formatted document is sent from the server to a summary generation system. This system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract key points, and generate a summary. Specifically, it identifies the important parts of the RFC document (e.g., definitions, procedures, and notes) and summarizes them into a concise text.

[1056] Once summary generation is complete, the generated summary is sent back to the server. The server receives this summary and then sends it to the user's device. The user's device includes devices such as PCs and smartphones. The user can easily view the generated summary through these devices.

[1057] Specific example

[1058] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a summarization generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[1059] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[1060] This summary is sent to the user's terminal via the server, allowing the user to review and deepen their understanding. In this way, the system can provide specialized content in a concise format and efficiently transmit standardized knowledge.

[1061] The above describes specific embodiments for carrying out the present invention.

[1062] The following describes the processing flow.

[1063] Step 1:

[1064] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[1065] Step 2:

[1066] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[1067] Step 3:

[1068] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[1069] Step 4:

[1070] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[1071] Step 5:

[1072] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[1073] Step 6:

[1074] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[1075] Step 7:

[1076] The server temporarily stores the received summary. Next, it prepares to send the summary to the user's terminal.

[1077] Step 8:

[1078] The server sends the saved summary to the user's terminal. This process allows the user to view the summary. Specifically, it uses methods such as HTTP POST requests.

[1079] Step 9:

[1080] The user reviews the summary text received on their device (PC, smartphone, etc.). Based on this summary text, the user can quickly understand the main points of the RFC.

[1081] The above is a description of the specific processing steps of the program.

[1082] (Example 1)

[1083] 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".

[1084] Technical documents, especially RFC documents, are often very lengthy and contain highly specialized content. Therefore, summarizing these documents in an easily understandable format is crucial. However, manual summarization is time-consuming, creating a system that efficiently and accurately generates summaries is essential.

[1085] 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.

[1086] In this invention, the server includes means for acquiring a specified technical document, means for formatting the technical document for summary generation, means for transmitting the formatted technical document to a natural language processing algorithm, means for receiving a summary from the natural language processing algorithm, and means for transmitting the summary to a user terminal. This makes it possible to efficiently perform the entire process from acquiring a technical document to generating a summary and distributing it.

[1087] A "technical document" is a document that contains information about a specific technology, including specific protocols and specifications.

[1088] "Summary generation" refers to the process of concisely summarizing a lengthy document and extracting only the main points.

[1089] "Natural language processing algorithms" refer to algorithms used to understand, analyze, and generate human language, and include technologies such as generative AI models.

[1090] A "generative AI model" refers to a model that uses machine learning or deep learning to learn knowledge from large amounts of text data and has the ability to generate new text.

[1091] "Formatting" refers to the process of removing unnecessary parts of a document, extracting necessary information, and converting it into a format suitable for summary generation.

[1092] A "summary" refers to a concise text that contains only the main points of the original document.

[1093] "User terminal" refers to devices such as computers, smartphones, and tablets used by the user.

[1094] The "Internet" refers to a system that connects computer networks to each other and allows for the exchange of information.

[1095] A "data repository" refers to a database or storage system used to store, manage, and provide specific data.

[1096] Modes for carrying out the invention

[1097] This invention relates to a system for efficiently and accurately summarizing technical documents. This system consists of "means for summarizing technical documents" and "means for generating and distributing the summary to users." This document describes the specific hardware and software configurations, as well as the methods for data processing and calculation.

[1098] First, the server retrieves the specified technical document (e.g., an RFC document) from a data repository on the internet. This is done using an HTTP request, and the technical document is received as a response. The retrieved technical document is then formatted by the server and converted into an appropriate format for summary generation. This formatting process includes removing unnecessary sections and extracting necessary information.

[1099] The formatted technical document is sent from the server to a generative AI model. This generative AI model uses natural language processing algorithms, such as GPT-4. The generative AI model analyzes the document, extracts the main points, and generates a concise summary.

[1100] The generated summary is sent to the user's device via the server. This device includes PCs, smartphones, and tablets. Users can easily review the generated summary through these devices.

[1101] Specific example

[1102] For example, consider the case of summarizing RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its data repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to a generative AI model (using a natural language processing algorithm), which generates a summary like the following:

[1103] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[1104] This summary is sent to the user's terminal via the server. The user can then review it and deepen their understanding.

[1105] Example of a prompt

[1106] The following are examples of prompts to input into a generative AI model:

[1107] "Please summarize the RFC 2616 document. The summary should include key points such as how to make an HTTP / 1.1 request, status codes, and entity header information."

[1108] The above describes specific embodiments for carrying out the present invention.

[1109] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1110] Program processing flow

[1111] Step 1: Obtain the RFC document

[1112] The server retrieves the specified technical document from an internet data repository. The server accesses the specified URL using an HTTP request and downloads the technical document.

[1113] Input: URL of the specified technical document

[1114] Process: Send an HTTP GET request and retrieve the technical document as a response.

[1115] Output: Acquired technical documents (text data)

[1116] Specifically, the server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" and receives text data as a response.

[1117] Step 2: Formatting the document

[1118] The server formats the acquired technical documents for summary generation. This includes removing unnecessary parts of the document and extracting necessary text information.

[1119] Input: Acquired technical documents (text data)

[1120] Processing: Remove unnecessary sections, extract key sections, and format.

[1121] Output: Formatted technical document suitable for summary generation.

[1122] Specifically, the server identifies and removes sections such as the preface and bibliography from the retrieved document, and extracts important parts such as the HTTP request method, status code, and header information.

[1123] Step 3: Document submission for summary generation

[1124] The server sends the formatted technical document to the AI ​​model. The server then sends the data to the corresponding API endpoint.

[1125] Input: Formatted technical document (text data)

[1126] Processing: Convert the formatted document to JSON format and send it to the generating AI model via a POST request.

[1127] Output: POST request response (summary)

[1128] Specifically, the server includes the formatted document in JSON format when sending the API request.

[1129] For example:

[1130] POST / summarize

[1131] Host: ai-model-service.com

[1132] Content-Type: application / json

[1133] {

[1134] "document": "HTTP / 1.1... (formatted text)"

[1135] }

[1136] Step 4: Summary Generation

[1137] The generative AI model analyzes the submitted technical document and generates a summary.

[1138] Input: Pre-formatted document (JSON data)

[1139] Processing: Analysis and summary generation using a generative AI model.

[1140] Output: Summary text (text data)

[1141] Specifically, the generative AI model divides the document, extracts the main points, and generates a concise summary.

[1142] Step 5: Receiving the summary

[1143] The server receives the summary text generated from the AI ​​model and prepares for the next processing step.

[1144] Input: Summary text (JSON data)

[1145] Processing: Receive and save the response.

[1146] Output: Summary text (text data)

[1147] Specifically, the server receives the response from the generated AI model and saves the summary text to a database or file system.

[1148] Step 6: Sending the summary to the user's terminal

[1149] The server sends a summary to the user's terminal. Protocols such as REST API and WebSocket are used.

[1150] Input: Summary text (text data)

[1151] Processing: Sending data to the user terminal

[1152] Output: Summary text displayed on the user's terminal

[1153] Specifically, the server sends a summary text to the user's device via the API and confirms that the transmission is complete.

[1154] Step 7: View the summary

[1155] Users view the summary on their own devices. These devices include PCs, smartphones, and tablets.

[1156] Input: Summary text sent to the user terminal

[1157] Processing: Display and view summary text

[1158] Output: User understanding and confirmation

[1159] In terms of specific operations, the user receives a summary text through a dedicated app or web browser and reads it by scrolling through the main text.

[1160] Specific example

[1161] For example, in the case of RFC 2616 (HTTP / 1.1), the specific process is as follows:

[1162] 1. The server sends an HTTP GET request to "https: / / example.com / rfc / rfc2616.txt" to retrieve the text of RFC 2616.

[1163] 2. The server removes unnecessary sections (such as the preface and bibliography) and formats only the main text.

[1164] 3. Send the formatted document to the AI ​​model in JSON format.

[1165] 4. The generative AI model extracts the key points and generates a summary sentence such as, "HTTP / 1.1 describes the format of requests and responses and the role of headers."

[1166] 5. The server receives the generated summary and saves it to the log.

[1167] 6. The server sends the summary text to the user's device via API.

[1168] 7. Users view the summary text and confirm its content through a smartphone app.

[1169] The above is the specific processing flow of the system program.

[1170] (Application Example 1)

[1171] 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".

[1172] RFC documents are extremely lengthy, requiring considerable time and effort to understand. Therefore, a more efficient means of providing information is needed to enable factory maintenance staff to quickly grasp technical standard documents and resolve problems.

[1173] 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.

[1174] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, and means for transmitting the summary to a target device. This enables maintenance staff in the factory to quickly check summaries of technical standard documents and efficiently resolve problems.

[1175] An "RFC document" is an official technical document aimed at standardizing internet technologies.

[1176] "Means of acquisition" refers to the process of accessing and downloading a specified document from an internet data repository.

[1177] "Formatting" refers to the process of removing unnecessary parts from an acquired document and processing it into a format suitable for summary generation.

[1178] A "summary generation system" is a system that uses specific algorithms or generative AI models to extract important information from long documents and summarize it concisely.

[1179] A "generative AI model" is an artificial intelligence model that uses natural language processing to analyze text data and perform semantic summarization and generation.

[1180] "Means of transmission" refers to the process of transferring formatted documents or summaries to other systems or terminals.

[1181] "Means of receiving" refers to the process of receiving data sent from another system or server.

[1182] A "user terminal" refers to a device that a user can directly operate, and includes smartphones, tablets, and personal computers.

[1183] A "target device" refers to a device that receives a summary text and displays or utilizes its contents. Examples of applications include robots operating in factories.

[1184] This invention is a system for providing RFC documents, which are intended to standardize internet technologies, as efficient and easy-to-understand summaries. This system operates through the coordinated efforts of a server, terminal, and user.

[1185] The server first retrieves the specified RFC document from an internet data repository using an HTTP request. For example, to retrieve RFC 2616 (HTTP / 1.1), the server downloads the document from the repository. Because this document is very long, the server then formats it into a format suitable for summary generation. This process involves removing unnecessary parts of the document and extracting the necessary text information (e.g., definitions, procedures, notes).

[1186] The formatted document is sent from the server to the summary generation system. This summary generation system uses a generative AI model (e.g., a BART model) to analyze the document, extract key points, and generate a summary. The summary generation system uses a tokenizer to tokenize the document and inputs it into the generative model to generate the summary. The resulting summary is then sent back to the server.

[1187] The server receives this summary and sends it to the user's terminal and the target equipment. The user's terminal includes devices such as PCs and smartphones, through which the user can easily view the generated summary. Furthermore, the summary is also sent to target equipment, such as robots operating in the factory, allowing maintenance staff to quickly refer to it.

[1188] As a concrete example, if a maintenance staff member in a factory instructs a robot to "show me the summary of RFC 2616," the robot will generate a summary and display it to the staff member. The following prompt is used for the generation AI model:

[1189] shell

[1190] Summarize the HTTP / 1.1 protocol as described in RFC 2616. Highlight the key points related to request methods, status codes, and header fields.

[1191] This allows maintenance staff within the factory to achieve faster work progress and problem resolution. The use of generative AI models enables highly advanced and efficient document analysis and summarization, facilitating the rapid dissemination of standardized knowledge.

[1192] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1193] Step 1:

[1194] The server retrieves a specified RFC document from an internet data repository using an HTTP request. The input is the URL of the RFC document, and the output is the text data of the retrieved RFC document. This text data is extremely long in its original form.

[1195] Step 2:

[1196] The server formats the retrieved RFC document into a format suitable for summary generation. This step removes unnecessary parts and extracts key text information. The input is the text data of the retrieved RFC document, and the output is the formatted text data. This formatting improves the efficiency of the summary generation system.

[1197] Step 3:

[1198] The server sends the formatted RFC document to the summarization generation system. The input is formatted text data, and the output is the formatted data that the summarization generation system receives. The server sends the data so that the summarization generation system can analyze it using a specified generative AI model.

[1199] Step 4:

[1200] The summary generation system analyzes formatted documents using a generative AI model and generates a summary. In this step, the model is input using specific prompt sentences. The input consists of formatted text data and prompt sentences, and the output is the generated summary. The data processing performed here involves tokenization of the text using a tokenizer and summary generation by the AI ​​model.

[1201] Step 5:

[1202] The summary generated by the summary generation system is sent to the server. The input is the generated summary, and the output is the summary received by the server. This completes the data needed for transfer to the user or target device.

[1203] Step 6:

[1204] The server sends a summary to the user terminal and the target device. The input is the generated summary, and the output is the summary received by the user terminal and the target device. This includes terminals that users access directly (smartphones, PCs) and robots operating in factories.

[1205] Step 7:

[1206] The user reviews the summary text generated through their terminal or the target device. The input is the summary text received by the terminal or device, and the output is the user's visual information. This allows factory maintenance staff to quickly obtain the necessary information and take appropriate action.

[1207] The above outlines the specific processing steps of the system required to realize the application examples.

[1208] 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.

[1209] This invention relates to a system for providing RFC documents, intended for the standardization of internet technologies, as efficient and easy-to-understand summaries. Furthermore, it relates to a system that facilitates user understanding by recognizing user emotions and adjusting the content or display method of the summary. This system operates in cooperation with a server, terminal, emotion engine, and user.

[1210] In this system, the server first retrieves the specified RFC document from an internet data repository. Retrieval is performed using an HTTP request, and the retrieved RFC document is saved in text format. Next, the server formats this document into a format suitable for summary generation. This formatting includes removing unnecessary metadata and extracting necessary text information.

[1211] The formatted document is sent from the server to the summary generation system. The summary generation system uses an artificial intelligence model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The summary generated by the summary generation system is then sent back to the server.

[1212] The generated summary is then sent from the server to the user's terminal. The user's terminal is a device such as a PC or smartphone, and the summary is displayed to the user. It is worth noting that the user's terminal has the capability to collect emotional data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice and collect emotional data.

[1213] The collected emotion data is sent to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state. For example, if the emotion engine determines that the user is confused, it processes the summary text to be more concise or to provide additional information. This regenerated summary text is then sent back from the server to the user's terminal.

[1214] Specific example

[1215] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). In this case, the server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where an artificial intelligence model analyzes it and generates a summary like the following:

[1216] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[1217] Once this summary is generated, the user's emotional state is then reflected. For example, if the user is perceived as confused, the emotion engine regenerates the summary in a more understandable form and resends it to the user's device. This additional step allows the user to receive information in a more easily comprehensible format.

[1218] The above describes specific embodiments for carrying out the present invention.

[1219] The following describes the processing flow.

[1220] Step 1:

[1221] The server receives a specified RFC document ID (e.g., "RFC2616") as input. The server sends an HTTP request to a data repository on the internet and retrieves the data for the corresponding RFC document.

[1222] Step 2:

[1223] The server saves the retrieved RFC documents in text format. The retrieved data needs to be formatted for analysis while maintaining the structure of the original document.

[1224] Step 3:

[1225] The server performs the formatting process. Specifically, it extracts the necessary text from the XML or HTML format of the RFC document and converts it into a format suitable for summary generation. During this process, unnecessary metadata and annotations are removed to clean up the text content.

[1226] Step 4:

[1227] The server sends the formatted text data to the summary generation system's API and requests summary generation. This includes an HTTP POST request to the AI ​​model's endpoint.

[1228] Step 5:

[1229] The summary generation system starts analyzing the received text data using an artificial intelligence model. It extracts key points and generates a summary based on them.

[1230] Step 6:

[1231] The summary generation system sends the generated summary text to the server. The summary text concisely summarizes the main information and key points.

[1232] Step 7:

[1233] The server temporarily stores the received summary text. Next, it prepares to send this summary text to the user's terminal in order to interact with the sentiment engine.

[1234] Step 8:

[1235] The user's device collects user emotion data. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice, and generates emotion data from that analysis.

[1236] Step 9:

[1237] The user's device sends the collected emotional data to the emotion engine.

[1238] Step 10:

[1239] The emotion engine determines the user's current emotional state based on the emotional data it receives. For example, it analyzes whether the user is confused or not.

[1240] Step 11:

[1241] The emotion engine adjusts the summary to reflect the user's emotional state. If the user is confused, it regenerates the summary to be even more concise and provide additional information.

[1242] Step 12:

[1243] The emotion engine sends a summarized text, adjusted for emotional processing, to the server.

[1244] Step 13:

[1245] The server sends the revised summary to the user's terminal.

[1246] Step 14:

[1247] Users can view the regenerated summary on their own devices (PC, smartphone, etc.). This allows users to understand the summary in a way that is appropriate to their emotional state.

[1248] The above is a description of the specific processing steps of the program.

[1249] (Example 2)

[1250] 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".

[1251] RFC documents, aimed at standardizing internet technologies, are often difficult to understand due to their detailed nature, posing a burden to users. Furthermore, traditional summarization systems do not consider the user's level of understanding or emotional state, resulting in summaries that are not always easy for users to grasp. There is a need to address these challenges and enable users to understand RFC documents more efficiently.

[1252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1253] In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for the user terminal to collect user sentiment data, means for analyzing the sentiment data and adjusting the summary, and means for transmitting the regenerated summary back to the user terminal. This makes it possible for the user to view a summary optimized based on their sentiment state, thereby facilitating their understanding of the RFC document.

[1254] An "RFC document" is a technical document issued for the purpose of standardizing internet technologies.

[1255] A "summary generation system" is a system that analyzes RFC documents, extracts the main points, and generates a concise summary.

[1256] A "user terminal" refers to a device used by a user, including PCs and smartphones.

[1257] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions, voice, and other factors.

[1258] An "emotion engine" is a system that analyzes emotional data to determine the user's current emotional state.

[1259] An "artificial intelligence model" is an algorithm that learns from large amounts of data and performs tasks such as text analysis and generation.

[1260] A "server" is a computer system that retrieves, processes, and transmits data.

[1261] This invention relates to a system for efficiently summarizing RFC documents aimed at standardizing internet technologies and adjusting the summary text based on the user's emotional state. This system operates in cooperation with a server, user terminal, emotion engine, and summary generation system. Specific embodiments are described below.

[1262] First, the server receives a request from the user for a specified RFC document. Next, the server retrieves the RFC document from an internet data repository using an HTTP request and saves it in text format. Specifically, it uses an HTTP client library such as "curl". The retrieved document is then converted to a specific format for text processing.

[1263] Next, the server formats the retrieved RFC document. Formatting includes removing unnecessary metadata and extracting important information (e.g., HTTP request method, status code, header information). Regular expressions are used, such as the Python "re" library, to remove unnecessary parts and extract the important parts.

[1264] The formatted document is sent by the server to the summary generation system. The summary generation system analyzes the document using a generative AI model (e.g., BERT or GPT), extracts the main points, and generates a summary. This summary generation can be performed using an external API request.

[1265] The summary generated by the summary generation system is sent back to the server, which then transmits it to the user's terminal. User terminals include PCs and smartphones, and these devices display the summary to the user.

[1266] Next, the user's device uses its camera and microphone to collect emotional data such as the user's facial expressions and voice. For example, it uses technologies such as OpenCV (camera) or Google Cloud Speech-to-Text API (voice) to collect emotional data. The collected emotional data is then sent to the emotion engine.

[1267] The emotion engine analyzes received emotion data to determine the user's current emotional state. For example, it uses deep learning models such as TensorFlow to perform image recognition and speech analysis. Based on the emotional state, the emotion engine regenerates the summary text in a more easily understandable format.

[1268] The regenerated summary is sent back to the user's terminal via the server and displayed to the user. This process allows the user to view a summary optimized based on their emotional state, thereby facilitating their understanding of the RFC document.

[1269] Specific example

[1270] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document and extracts and formats the important parts of the document (e.g., HTTP request methods, status codes, header information, etc.). The formatted document is sent to the summary generation system, which generates a summary sentence such as, "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST), and entity headers."

[1271] This summary is sent to the user's device and displayed. Furthermore, if the sentiment engine determines that the user is confused, it regenerates the summary to be more concise or to provide additional information, and sends it to the user's device again to facilitate user understanding.

[1272] Example of a prompt

[1273] "Please summarize the RFC 2616 (HTTP / 1.1) document. Include key points such as the HTTP request method, status codes, and header information."

[1274] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1275] Step 1: The server receives the RFC document request.

[1276] Specific operation: The server receives a request from the user. It retrieves the identification information (e.g., RFC number) of the RFC document requested by the user.

[1277] Input: User's RFC document request, RFC number

[1278] Output: Request information including RFC number

[1279] Step 2: The server retrieves the RFC document from an internet data repository.

[1280] Specific operation: The server generates an HTTP request and retrieves the RFC document corresponding to the specified RFC number from the data repository. The retrieved document is saved in text format.

[1281] Input: RFC number

[1282] Output: RFC document text

[1283] Step 3: The server formats the RFC document.

[1284] Specific operation: The server opens the retrieved RFC document and removes unnecessary metadata. Next, it extracts important parts such as the HTTP request method, status code, and header information. This is processed using regular expressions with Python's "re" library, for example.

[1285] Input: RFC document text

[1286] Output: Formatted text information

[1287] Step 4: The server sends the formatted document to the summary generation system.

[1288] Specific operation: The server sends the formatted document to the summarization generation system using an API request (e.g., an HTTP POST request).

[1289] Input: Formatted text information

[1290] Output: Summary generation request

[1291] Step 5: The summary generation system generates a summary.

[1292] Specific operation: The summarization generation system uses a generative AI model (e.g., BERT, GPT) to analyze documents, extract key points, and generate a summary.

[1293] Input: Formatted text information

[1294] Output: Generated summary

[1295] Step 6: The server receives the summary from the summary generation system.

[1296] Specific operation: The server receives the summary text generated from the summary generation system.

[1297] Input: Generated summary

[1298] Output: Summary

[1299] Step 7: The server sends the summary to the user's terminal.

[1300] Specific operation: The server uses WebSocket or HTTP requests to send the generated summary text to the user's terminal.

[1301] Input: Summary

[1302] Output: Summary text sent to the user's terminal

[1303] Step 8: The user terminal receives and displays the summary.

[1304] Specific action: The user terminal displays the received summary text on the screen.

[1305] Input: Summary text sent from the server

[1306] Output: Displayed summary

[1307] Step 9: The user device collects user sentiment data.

[1308] Specific operation: The user's device uses its camera and microphone to collect the user's facial expressions and voice. Specifically, it uses tools such as OpenCV and the Google Cloud Speech-to-Text API.

[1309] Input: User's facial expressions and voice data

[1310] Output: Sentiment data

[1311] Step 10: The user device sends emotion data to the emotion engine.

[1312] Specific operation: The user terminal transmits the collected emotional data to the emotion engine via the network.

[1313] Input: Sentiment data

[1314] Output: Emotional data sent to the emotion engine

[1315] Step 11: The emotion engine analyzes emotional data to determine the emotional state.

[1316] Specific operation: The emotion engine analyzes the received emotion data and uses a deep learning model (e.g., TensorFlow) to determine the user's emotional state.

[1317] Input: Sentiment data

[1318] Output: User's emotional state

[1319] Step 12: The emotion engine adjusts the summary.

[1320] Specific operation: The emotion engine regenerates or adjusts the summary text based on the user's emotional state.

[1321] Input: Generated summary, user's emotional state

[1322] Output: Regenerated or adjusted summary

[1323] Step 13: The server resends the regenerated summary to the user terminal.

[1324] Specific operation: The server resends the regenerated summary text sent from the emotion engine to the user's terminal.

[1325] Input: Regenerated summary

[1326] Output: Regenerated summary sent to the user terminal

[1327] Step 14: The user terminal receives and displays the regenerated summary.

[1328] Specific operation: The user terminal receives the regenerated summary and displays it on the screen.

[1329] Input: Regenerated summary

[1330] Output: Displayed regenerated summary

[1331] (Application Example 2)

[1332] 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".

[1333] Currently, there is a demand for quick and efficient understanding of technical documents and standards documents (e.g., RFC documents), but this is often difficult, especially for users unfamiliar with technology. In addition, there are very few systems that provide interactive feedback that responds to the user's emotional state. As a result, when users become confused, appropriate responses are not taken, making it even more difficult to understand the information.

[1334] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring an RFC document, means for formatting the RFC document for summary generation, means for transmitting the formatted RFC document to a summary generation system, means for receiving a summary from the summary generation system, means for transmitting the summary to a user terminal, means for collecting user emotion data from the user terminal, means for analyzing the emotion data to determine the user's emotional state, and means for adjusting the summary based on the user's emotional state. This makes it possible not only to make technical documents easier for the user to understand, but also to provide appropriate feedback that corresponds to the user's emotions.

[1335] An "RFC document" is a technical document aimed at standardizing internet technologies, describing specifications related to communication protocols and internet-related technologies.

[1336] "Summary generation" is the process of extracting the key points from a document and concisely describing the original information.

[1337] A "summary generation system" is a system that analyzes an original document, extracts the main points, and generates a summary.

[1338] A "server" is a computer system used for acquiring, formatting, transmitting, and receiving data.

[1339] A "user terminal" refers to a computer or mobile device operated by the user that displays the summary text.

[1340] "Emotional data" refers to data about a user's emotional state obtained by analyzing their facial expressions and voice.

[1341] An "emotion engine" is a software component that analyzes collected emotional data to determine the user's emotional state.

[1342] "Formatting" refers to the process of converting a document into a format suitable for summary generation, such as removing unnecessary metadata and extracting necessary text information.

[1343] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and has the ability to understand and generate documents like a human.

[1344] A "prompt" is an instruction or question given as input to a generative AI model.

[1345] This invention relates to a system for users to quickly and easily obtain RFC documents in an understandable format. For this purpose, a server, a user terminal, and a generative AI model work in conjunction.

[1346] First, the server retrieves the specified RFC document from a data repository on the internet. This is done using an HTTP request, and the document is stored in text format. Next, the server formats the retrieved document. This formatting includes removing unnecessary metadata and extracting the necessary text information.

[1347] The formatted RFC document is sent from the server to the summary generation system. The summary generation system uses a generative AI model (e.g., a natural language processing algorithm) to analyze the document, extract the main points, and generate a summary. The generated summary is then sent back to the server.

[1348] Next, the server sends the generated summary to the user's terminal. The user terminal is equipped with devices such as a camera and microphone to collect user emotion data. For example, it analyzes the user's facial expressions and voice to determine their emotional state.

[1349] Emotional data is sent from the user's terminal to the emotion engine. The emotion engine analyzes this data to determine the user's current emotional state (e.g., confused, satisfied). Based on the determined emotional state, the server adjusts the summary text. For example, if the server determines that the user is confused, it will either make the summary text clearer or regenerate it by providing additional information.

[1350] The refined summary is then sent back from the server to the user's terminal and displayed to the user. This process allows users to easily understand the technical document and obtain additional information as needed.

[1351] Specific example

[1352] For example, consider the case of generating a summary of RFC 2616 (HTTP / 1.1). The server first retrieves the RFC 2616 document from its repository. Next, it extracts and formats the important parts of this document, such as the HTTP request method, status code, and header information. The formatted document is sent to the summary generation system, where a generation AI model analyzes it to generate a summary like the following:

[1353] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers."

[1354] If the user's emotional state is detected as confused while reading this summary, the sentiment engine will regenerate the summary in a more understandable form and adjust it as follows:

[1355] "HTTP / 1.1 is an internet communication protocol that describes the format of requests and responses and the role of headers. Key points include the use of status codes, methods (GET, POST, etc.), and entity headers. Additional information: A detailed explanation can be found at https: / / ."

[1356] Example of a prompt

[1357] "How can I generate a summary of RFC 2616 and provide additional information if the user is confused?"

[1358] In this way, the system can help users understand the situation and provide real-time, emotion-responsive feedback.

[1359] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1360] Step 1:

[1361] The server retrieves the specified RFC document from an internet data repository. It sends an HTTP request based on the RFC document number entered by the user and retrieves the corresponding document in text format. The input is the RFC document number, and the output is the text data of the retrieved RFC document.

[1362] Step 2:

[1363] The server formats the retrieved RFC document for summary generation. Unnecessary metadata is removed from the document, and important text information such as request method, status code, and header information is extracted. This process ensures efficient summary generation. The input is the text data of the retrieved RFC document, and the output is the formatted text data.

[1364] Step 3:

[1365] The server sends the formatted RFC document to the summarization generation system. The summarization generation system uses a generative AI model to analyze the document, extract key points, and generate a summary. In this process, the generative AI model is given prompt sentences as input and the summarization result is received as output. The input is the formatted text data and prompt sentences, and the output is the generated summary.

[1366] Step 4:

[1367] The server sends the generated summary to the user terminal. At this time, the user terminal prepares to display the received summary. The input is the summary, and the output is the completion of sending the summary to the user terminal.

[1368] Step 5:

[1369] The user's device uses its camera and microphone to collect user emotion data. While the user reads the summary, the device analyzes the user's facial expressions and voice to collect data for determining their emotional state. The input is the user's facial expressions and voice, and the output is the collected emotion data.

[1370] Step 6:

[1371] The user terminal sends collected emotional data to the emotion engine, which then analyzes it. Based on the emotional data, the emotion engine determines the user's emotional state and identifies whether it is confused, satisfied, focused, etc. The input is the collected emotional data, and the output is the determined emotional state.

[1372] Step 7:

[1373] The server adjusts the summary based on the user's emotional state, as determined by the emotion engine. For example, if the server determines that the user is confused, it regenerates the summary to make it clearer or provides additional information. This process makes the information easier for the user to understand. The input is the determined emotional state and the original summary, and the output is the adjusted summary.

[1374] Step 8:

[1375] The server sends the adjusted summary back to the user terminal, which then displays the summary. This allows the user to view a summary tailored to their situation in real time. The input is the adjusted summary, and the output is the transmission and display of the summary to the user terminal.

[1376] 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.

[1377] 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.

[1378] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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."

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1397] The following is further disclosed regarding the embodiments described above.

[1398] (Claim 1)

[1399] Means of obtaining RFC documents,

[1400] A means for formatting the aforementioned RFC document for summary generation,

[1401] Means for transmitting the formatted RFC document to a summary generation system,

[1402] A means of receiving a summary from a summary generation system,

[1403] Means for transmitting the aforementioned summary to the user terminal,

[1404] A system that includes this.

[1405] (Claim 2)

[1406] The system according to claim 1, which retrieves a specified RFC document from an internet data repository.

[1407] (Claim 3)

[1408] The system according to claim 1, wherein the summarization generation system analyzes an RFC document using an artificial intelligence model and generates a summary.

[1409] That's all.

[1410] "Example 1"

[1411] (Claim 1)

[1412] Means of obtaining the specified technical documents,

[1413] Means for formatting the aforementioned technical document for summary generation,

[1414] Means for transmitting the formatted technical document to a natural language processing algorithm,

[1415] A means of receiving a summary text from a natural language processing algorithm,

[1416] Means for transmitting the aforementioned summary to the user terminal,

[1417] A system that includes this.

[1418] (Claim 2)

[1419] The system according to claim 1, which retrieves specified technical documents from an internet data repository.

[1420] (Claim 3)

[1421] The system according to claim 1, wherein the natural language processing algorithm analyzes a technical document using a generated AI model and generates a summary.

[1422] "Application Example 1"

[1423] (Claim 1)

[1424] Means of obtaining RFC documents,

[1425] A means for formatting the aforementioned RFC document for summary generation,

[1426] Means for transmitting the formatted RFC document to a summary generation system,

[1427] A means of receiving a summary from a summary generation system,

[1428] Means for transmitting the aforementioned summary to the user terminal,

[1429] Means for transmitting a summary to the target device,

[1430] A system that includes this.

[1431] (Claim 2)

[1432] The system according to claim 1, which retrieves a specified RFC document from an internet data repository.

[1433] (Claim 3)

[1434] The system according to claim 1, wherein the summary generation system analyzes an RFC document using a generation AI model and generates a summary.

[1435] "Example 2 of combining an emotion engine"

[1436] (Claim 1)

[1437] Means of obtaining RFC documents,

[1438] A means for formatting the aforementioned RFC document for summary generation,

[1439] Means for transmitting the formatted RFC document to a summary generation system,

[1440] A means of receiving a summary from a summary generation system,

[1441] Means for transmitting the aforementioned summary to the user terminal,

[1442] The user terminal includes means for collecting user emotion data,

[1443] A means for analyzing the aforementioned sentiment data and adjusting the summary text,

[1444] A means of sending the regenerated summary text back to the user terminal,

[1445] A system that includes this.

[1446] (Claim 2)

[1447] The system according to claim 1, which retrieves a specified RFC document from an internet data repository.

[1448] (Claim 3)

[1449] The system according to claim 1, wherein the summarization generation system analyzes an RFC document using an artificial intelligence model and generates a summary.

[1450] "Application example 2 when combining with an emotional engine"

[1451] (Claim 1)

[1452] Means of obtaining RFC documents,

[1453] A means for formatting the aforementioned RFC document for summary generation,

[1454] Means for transmitting the formatted RFC document to a summary generation system,

[1455] A means of receiving a summary from a summary generation system,

[1456] Means for transmitting the aforementioned summary to the user terminal,

[1457] A means for collecting user emotion data from the user terminal,

[1458] A means for analyzing the aforementioned emotional data to determine the user's emotional state,

[1459] Means for adjusting the summary text based on the user's emotional state,

[1460] A system that includes this.

[1461] (Claim 2)

[1462] The system according to claim 1, which retrieves a specified RFC document from an internet data repository.

[1463] (Claim 3)

[1464] The system according to claim 1, wherein the summary generation system analyzes an RFC document using a generation AI model and generates a summary. [Explanation of symbols]

[1465] 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. Means of obtaining RFC documents, A means for formatting the aforementioned RFC document for summary generation, Means for transmitting the formatted RFC document to a summary generation system, A means of receiving a summary from a summary generation system, Means for transmitting the aforementioned summary to the user terminal, A system that includes this.

2. The system according to claim 1, which retrieves a specified RFC document from an internet data repository.

3. The system according to claim 1, wherein the summarization generation system uses an artificial intelligence model to analyze an RFC document and generate a summary.

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