system

Abstract

We provide the system. [Solution] A means for a user to input an article and send that article to the server, A means for summarizing articles received by the server using a natural language processing algorithm, A method for extracting technical terms from summarized articles, A means for generating explanations for extracted technical terms, A means for displaying summary articles and explanations of technical terms on the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information society, the number of specialized and difficult-to-understand articles is increasing, and it is difficult for ordinary users to quickly understand them and acquire related knowledge. In particular, in the case of articles that use a lot of technical terms, users have to spend time conducting investigations, and the labor is great. In addition, there is a large amount of information on the Internet, and it is important to obtain information from reliable information sources, but it is difficult to select and understand the information by oneself.

Means for Solving the Problems

[0005] This invention provides a system in which a user inputs a specialized and complex article, and a server summarizes the article using a natural language processing algorithm, providing a concise summary of its main points. The server also extracts technical terms from the summarized article and generates explanations for them. These explanations are created by obtaining reliable information by referencing external databases or APIs. This allows users to simultaneously obtain an article summary and explanations of related technical terms, enabling them to acquire specialized knowledge quickly and efficiently.

[0006] A "user" is the entity that accesses the system, enters an article, and receives the results.

[0007] A "server" is a device or platform that receives and processes articles submitted by users.

[0008] A "natural language processing algorithm" is a computer program used to analyze and transform input text data.

[0009] A "summary" is a text that expresses the main points of an article in a shortened form.

[0010] "Technical terms" are terms that have a specialized meaning within a particular field.

[0011] An "explanation" is a text that explains the meaning and background of technical terms.

[0012] An "external database" is a system that stores information accessible via a network.

[0013] An "API" is an interface for exchanging functions and data between different software programs.

[0014] A "user terminal" is a device that a user uses to access the system and view the results. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0018] In the following embodiments, 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.

[0019] 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.

[0020] 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, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for summarizing specialized and complex articles and providing explanations of technical terms, and its specific embodiments will be described in detail.

[0037] The user enters an article using a terminal. The article entered by the user is sent to the server. The server analyzes the received article using a natural language processing algorithm (e.g., an NLP library) and generates a summary. The generated summary is temporarily stored on the server.

[0038] Next, the server extracts technical terms from the summarized article. This extraction process uses specific technical term dictionaries and machine learning models. The extracted technical terms are listed, and their definitions are then generated. The server retrieves reliable definition information by referencing external databases and APIs. The retrieved definition information is organized, and definitions for multiple technical terms are generated at once.

[0039] Finally, the server compiles the summary article and explanations of technical terms and sends them to the user's terminal. The user's terminal displays the received information on the screen, allowing the user to simultaneously view a concise summary and detailed explanations of technical terms.

[0040] A concrete example is provided below.

[0041] A user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. The server receives the article, analyzes it using a natural language processing algorithm, and generates a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, it might generate the explanation, "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1."

[0042] Finally, the server sends the generated summary article and glossary of technical terms to the user's terminal. The user's terminal simultaneously displays the summary article, which states, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1."

[0043] In this way, the present invention enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses a terminal to input a specialized and complex article. The entered article is then sent from the terminal to the server.

[0047] Step 2:

[0048] The server receives the submitted article data and stores it temporarily. The stored data is then passed to a natural language processing algorithm.

[0049] Step 3:

[0050] The server uses natural language processing algorithms to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0051] Step 4:

[0052] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article.

[0053] Step 5:

[0054] The server generates explanations for extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously.

[0055] Step 6:

[0056] The server compiles a summary article and explanations of technical terms, and sends them to the user's terminal as a single response.

[0057] Step 7:

[0058] The response received by the device is displayed on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[0059] (Example 1)

[0060] 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."

[0061] In today's information society, quickly understanding specialized and complex articles and deepening related knowledge is a crucial challenge for many users. However, previous systems have lacked sufficient article summaries and explanations of technical terms, making it difficult for users to efficiently acquire information.

[0062] 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.

[0063] In this invention, the server includes means for a user to input an article and send the article to the server, means for the server to summarize the received article using a natural language processing algorithm, means for extracting keywords from the summarized article, means for generating explanations related to the extracted keywords, and means for displaying the summarized article and keyword explanations on the user's terminal. This makes it possible for users to quickly and efficiently understand specialized and complex articles and easily acquire related knowledge.

[0064] A "user" is a person or entity that inputs articles and uses the system.

[0065] An "article" is information or text that is entered by a user and processed by a system.

[0066] A "server" is a device or system that receives articles submitted by users, analyzes them, summarizes them, extracts keywords, generates keyword explanations, and transmits the information to the user's terminal.

[0067] A "natural language processing algorithm" is an algorithm that analyzes human language, understands its meaning, and performs processing based on that understanding.

[0068] "Keywords" are words or terms that are considered particularly important within a summarized article.

[0069] "Explanation" refers to descriptions and detailed information about the extracted keywords.

[0070] A "user terminal" is a device used by users to input articles and ultimately display summary articles and keyword explanations.

[0071] "External information sources" refer to external databases or internet resources that the server consults to obtain explanations about keywords.

[0072] An "interface" is the intermediary part that a server uses to communicate with external information sources.

[0073] This invention is a system for summarizing specialized and complex articles and providing explanations of related technical terms. The embodiments for carrying out this invention are described in detail below.

[0074] The user first enters an article using a terminal. This article is in text format and is usually entered manually into an input field such as a text field. An example of a prompt is "Please summarize an article on the fundamentals of quantum computing and provide explanations of technical terms." This allows the user to provide the system with a specific request.

[0075] The entered article is sent from the terminal to the server. This communication is generally done using an HTTP POST request. The server temporarily stores the received article in memory or a database.

[0076] The server applies natural language processing algorithms to the received articles. Examples of NLP libraries used include "spaCy" and "Transformers." Using these libraries, the server performs grammatical analysis of the articles and extracts key points. Based on this analysis, it summarizes the important content of the articles.

[0077] Next, the server extracts keywords from the summarized article. This keyword extraction uses specific vocabulary dictionaries or machine learning models (e.g., the BERT model). Then, it retrieves explanatory information for the extracted keywords by referencing external sources and interfaces (e.g., the Wikipedia API or the PubMed API). This allows for the automatic generation of reliable explanations.

[0078] The generated summary and keyword explanations are combined into a single packet and sent back to the terminal as an HTTP response.

[0079] Finally, the device displays the received information on its screen. Through the user interface, the summary article and keyword explanations are presented to the user in an easy-to-read format. For example, it might look like this:

[0080] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible." (Summary)

[0081] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1." (Explanation)

[0082] In this way, the present invention provides a system that enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters an article. The entered article is placed in the terminal's text field. For example, the user might enter "An article about the fundamentals of quantum computing." The input here is in string format, and when the submit button is pressed, the process proceeds to the next step.

[0086] Step 2:

[0087] The terminal sends the entered article to the server. Specifically, it sends the article data to the server using an HTTP POST request. The input data is in string format and is POSTed to the destination URL. The output is the article data received by the server.

[0088] Step 3:

[0089] The server applies natural language processing algorithms to analyze the articles it receives. First, the server temporarily stores the received data in memory or a database. Next, it uses an NLP library (e.g., spaCy or Transformers) to perform grammatical analysis and topic extraction of the articles. The input for this analysis is the stored article data, and the output is data showing the structure of the text and its main points.

[0090] Step 4:

[0091] The server generates a summary. Based on the key points extracted in step 3, a summary sentence is created using a natural language processing algorithm. This process uses specific rules and deep learning models. The input is the data of key points, and the output is a summary sentence (for example, "Quantum computing is a computational technique that uses qubits...").

[0092] Step 5:

[0093] The server extracts keywords from the summary. It analyzes the summary and extracts specific technical terms and important keywords using dictionaries or machine learning models (e.g., the BERT model). The input is the summary, and the output is a list of extracted keywords.

[0094] Step 6:

[0095] The server generates definitions for keywords. For the extracted keywords, it references external sources and interfaces (e.g., Wikipedia API or PubMed API) to obtain detailed definitions. The input is a list of keywords, and the output is a definition corresponding to each keyword (e.g., "qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1.").

[0096] Step 7:

[0097] The server sends a summary and keyword explanations to the terminal. The generated summary and explanation are combined into a single packet and sent to the terminal as an HTTP response. The input is the summary and explanation, and the output is the packet sent to the terminal.

[0098] Step 8:

[0099] The terminal displays the information it receives to the user. The terminal analyzes the received packets and displays a summary and explanatory text in an appropriate format on the user interface. The input is the packets sent from the server, and the output is the information displayed on the user's screen. Specifically, it is displayed using HTML and CSS in the following format:

[0100] Summary: "Quantum computing is a computational technique that uses qubits..."

[0101] Explanation: "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0102] (Application Example 1)

[0103] 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."

[0104] Traditional technical documents and manuals for factory workers are often highly specialized and difficult to understand. Especially when introducing new technologies or systems, workers are expected to learn them quickly, but understanding technical terminology can be time-consuming. Furthermore, there is a need for efficient information delivery methods utilizing devices such as smart glasses and head-mounted displays. Therefore, a system is needed that efficiently summarizes specialized technical documents and provides explanations for complex technical terms.

[0105] 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.

[0106] In this invention, the server includes means for a user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for displaying the summarized article and explanations of technical terms on a user terminal; means for using a head-mounted display or smart glasses for factory workers to view technical documents on equipment; means for automatically generating a summary of key points when a user views a technical document; and means for providing explanations of difficult technical terms. This enables workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0107] A "user" refers to a person who uses the system to input articles and receive information.

[0108] "Article" refers to text information entered or submitted by a user.

[0109] A "server" refers to a central processing unit that processes articles received from users and generates summaries and explanations of technical terms.

[0110] "Natural language processing algorithms" refer to technical methods used to analyze articles and generate summaries.

[0111] A "summary" refers to a concise summary extracted from the original article.

[0112] "Technical terms" refer to terms specific to a particular field.

[0113] "Explanation" refers to additional explanatory information regarding technical terms.

[0114] "User terminal" refers to a device used to display summary articles and explanations of technical terms.

[0115] "Factory worker" refers to a person who uses technical documents and manuals within a factory.

[0116] "Equipment" refers to information display devices such as head-mounted displays and smart glasses.

[0117] A "head-mounted display" refers to a device worn on the head that displays information in the field of vision.

[0118] "Smart glasses" refers to glasses-type information display devices.

[0119] "Technical documents" refer to specialized documents related to operations and systems within a factory.

[0120] "Automatically generating a summary of key points" means that the system automatically creates a summary without requiring any specific user action.

[0121] Modes for carrying out the invention

[0122] This invention is a system for factory workers to efficiently understand technical documents and manuals while working and to quickly acquire new technologies. In this system, the user inputs technical documents, the server processes the information to generate summaries and explanations of technical terms, and displays them on the user's terminal.

[0123] System program

[0124] The system program includes the following steps:

[0125] 1. Data entry

[0126] The user enters technical documentation into a terminal and sends the article to the server.

[0127] 2. Data Analysis and Summary Generation

[0128] The server analyzes the received articles using natural language processing algorithms (NLP libraries) and generates summaries. For this purpose, libraries such as Hugging Face Transformers are used.

[0129] 3. Extraction of technical terms and generation of explanations

[0130] This process extracts technical terms from summarized articles and generates explanations for those terms. External databases and APIs are referenced for generating these explanations. Specific technical term dictionaries and machine learning models are also utilized.

[0131] 4. Display of Information

[0132] The generated summary article and glossary of technical terms are sent to the user's device for display. Display is done using a head-mounted display or smart glasses (e.g., Google® Glass®, Vuzix Blade, Microsoft® HoloLens®, Magic Leap One, etc.).

[0133] Hardware and software usage

[0134] 1. Hardware

[0135] Head-mounted displays or smart glasses: These are used by workers to display technical documentation and obtain explanations on the device.

[0136] 2. Software

[0137] Natural language processing algorithms such as Hugging Face Transformers are used to analyze articles and generate summaries.

[0138] Specialized terminology dictionaries and machine learning models: Used to extract specialized terms and generate explanations for them.

[0139] External database or API: Used to retrieve explanations of technical terms.

[0140] Specific example

[0141] For example, if a factory worker wants to read the "Introductory Manual for a New Robot Control System," the content will be displayed on smart glasses (e.g., Vuzix Blade). In this case, the application will automatically generate a summary of the manual and provide explanations of technical terms such as "robot control system," "feedback loop," and "PID control."

[0142] Example of a prompt

[0143] Technical document: "Introduction Manual for the New Robot Control System"

[0144] Objective: To provide summaries of key points and explanations of technical terms, enabling workers to quickly and easily understand and learn the content.

[0145] In this way, the system of the present invention enables factory workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0146] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0147] Step 1:

[0148] Data entry

[0149] Input: The user enters technical documents into the terminal.

[0150] Data processing: The terminal converts technical documents entered by the user into a digital format.

[0151] Output: Send the technical document data, converted to digital format, to the server.

[0152] Step 2:

[0153] Data analysis and summary generation

[0154] Input: The server receives technical document data from the terminal.

[0155] Data processing: The server analyzes the text using natural language processing algorithms (e.g., Hugging Face Transformers) and extracts the main content.

[0156] Output: Generates a summarized technical document.

[0157] Step 3:

[0158] Technical term extraction

[0159] Input: The server receives a summarized technical document.

[0160] Data processing: Extract specialized terminology from summary articles using specific vocabulary dictionaries and machine learning models.

[0161] Output: Generates a list of extracted technical terms.

[0162] Step 4:

[0163] Generation of technical terminology explanations

[0164] Input: The server receives a list of extracted technical terms.

[0165] Data processing: The server retrieves explanatory information about each technical term by referring to external databases and APIs.

[0166] Output: Generates a list of explanations for technical terms.

[0167] Step 5:

[0168] Displaying information

[0169] Input: The server integrates summary articles and glossaries of technical terms.

[0170] Data processing: Converting data into a format that is easy for user devices (head-mounted displays and smart glasses) to display.

[0171] Output: Sends a summary article and a glossary of technical terms to the user's terminal.

[0172] Step 6:

[0173] User interaction

[0174] Input: Users view summary articles and glossaries of technical terms displayed via their device.

[0175] Data processing: The device records the user's actions and browsing history.

[0176] Output: Sends user feedback and operation logs to the server.

[0177] This completes a series of processing steps, allowing factory workers to efficiently understand summaries of technical documents and explanations of technical terms.

[0178] 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.

[0179] This invention combines a system that summarizes specialized and complex articles and provides explanations of technical terms with an emotion engine that recognizes the user's emotions, and a specific embodiment thereof will be described in detail.

[0180] The user enters an article using their device. The article entered by the user is sent from the device to the server. This transmitted data also includes the user's emotional data. Emotional data is often obtained from the user's facial expressions and voice.

[0181] The server receives the submitted article data and sentiment data and stores it temporarily. The stored data is then passed to a natural language processing algorithm. The server uses the natural language processing algorithm to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0182] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list technical terms within the article.

[0183] Next, the server generates explanations for the extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously. Furthermore, the sentiment engine analyzes the user's emotional data and adjusts the display method and content based on the results. For example, if the user is feeling surprised or anxious, it provides a simplified explanation or additional background information accordingly.

[0184] The server compiles a summary article and a glossary of technical terms adjusted based on sentiment data, and sends it to the user's device as a single response. The device displays the received response on the user's screen. The user can simultaneously view the summary article and the sentiment-adjusted glossary of technical terms.

[0185] A concrete example is provided below.

[0186] The user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. In addition, the user's emotions are captured via the camera and microphone, and this data is analyzed by an emotion engine. The server receives the article and analyzes it using a natural language processing algorithm to generate a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, an explanation such as "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1" is generated. The emotion engine further recognizes if the user is feeling anxious and adds additional explanations and diagrams to the explanation to aid understanding.

[0187] Finally, the server sends the generated summary article and a refined glossary of technical terms to the user's terminal. The user's terminal displays the summary article, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1." Additional illustrations and explanations may also be included depending on the user's preferences.

[0188] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by appropriately providing information according to the user's emotional state.

[0189] The following describes the processing flow.

[0190] Step 1:

[0191] The user uses their device to input specialized and complex articles. The user can either paste the article into the input field or type it directly. Along with the entered article, permission is granted to collect emotional data from the user's facial expressions and voice using the camera and microphone.

[0192] Step 2:

[0193] The device sends articles and sentiment data received from the user to the server. Sentiment data includes facial expressions, tone of voice, and speed.

[0194] Step 3:

[0195] The server temporarily stores the received article data and passes it to a natural language processing algorithm. It also passes sentiment data to an emotion engine to begin analysis.

[0196] Step 4:

[0197] The server uses a natural language processing algorithm to analyze the article, extract the main points, and generate a summary. The generated summary is then stored on the server.

[0198] Step 5:

[0199] The server extracts technical terms from the summary article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article. The listed technical terms are then passed on to the next step.

[0200] Step 6:

[0201] The server generates explanations for extracted technical terms. It retrieves reliable information by referencing external databases and APIs, and creates explanations for each technical term. The explanations are then compiled and stored on the server.

[0202] Step 7:

[0203] The server receives user sentiment data analyzed by the sentiment engine. The sentiment engine analyzes the user's emotional state while reading the article and provides the results to the server.

[0204] Step 8:

[0205] The server adjusts the display and content of summary articles and glossaries based on user sentiment data. For example, if a user is feeling anxious or surprised, the explanation may be rephrased in simpler language or additional illustrations may be provided.

[0206] Step 9:

[0207] The server compiles a summary article and explanations of technical terms, adjusted based on sentiment data, and sends them to the user's terminal as a single response.

[0208] Step 10:

[0209] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of specialized terminology that are adjusted according to the user's sentiment. For example, if additional explanations or illustrations based on sentiment are included, these are also displayed to aid the user's understanding.

[0210] This process allows users to quickly and efficiently understand specialized and complex articles, and receive support tailored to their emotional needs.

[0211] (Example 2)

[0212] 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".

[0213] Conventional technologies were unable to provide summaries or explanations of technical terms to efficiently understand specialized and complex articles. Furthermore, they failed to deliver information in a way that resonated with users' emotions, making it difficult to deepen their understanding. Therefore, there was a need for an effective system that would allow users to quickly understand specialized articles and efficiently acquire related knowledge.

[0214] 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.

[0215] In this invention, the server includes means for the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for an emotion engine to analyze the user's emotional data and adjust the content of the explanations; and means for displaying the summarized article and the adjusted technical term explanations on the user's terminal. This enables users to quickly and effectively understand specialized and complex articles, and provides appropriate information tailored to the user's emotions, resulting in a more effective learning experience.

[0216] A "user" refers to a person who uses a computer system to input articles and receive services.

[0217] A "terminal" is a device used by a user to input information and display it, such as a personal computer, tablet, or smartphone.

[0218] A "server" refers to a computer system that receives and processes data sent by users.

[0219] "Article data" refers to text data containing specialized information entered by users using their devices.

[0220] "Emotional data" refers to data that indicates the user's emotional state, and includes facial expressions and voice information acquired through cameras and microphones.

[0221] "Natural language processing algorithms" refer to programming techniques that analyze input text data and extract summaries and key information.

[0222] A "summary" refers to a shortened version of an article containing the main points, generated by the server using a natural language processing algorithm.

[0223] "Technical jargon" refers to terms specific to a particular field or area of ​​expertise, and is a specialized term that may be difficult for users to understand.

[0224] "Explanation" refers to text information generated by the server to provide a detailed explanation of technical terms.

[0225] An "emotion engine" refers to software technology that analyzes user emotion data and adjusts the displayed content based on the results.

[0226] An "external database" refers to an external information storage location that a server references to provide explanatory information on technical terms.

[0227] An "API" refers to an interface that allows different software systems to exchange information.

[0228] "Display" refers to showing the response from the server on the terminal screen.

[0229] This invention provides a system that enables users to efficiently understand specialized and complex articles. This system not only provides article summaries and explanations of technical terms, but also has the function of recognizing the user's emotions and adjusting the information accordingly. The specific configuration and operation are described in detail below.

[0230] First, the user enters the article using their device and sends that data to the server. The device can be a PC, tablet, or smartphone. As the user enters the article, emotional data (facial expressions and voice) is captured through the device's camera and microphone and sent to the server along with the article data.

[0231] The server temporarily stores the received article data and sentiment data. High-performance databases such as MySQL® or PostgreSQL are used for storage. Next, the server analyzes the articles and extracts key points using natural language processing (NLP) algorithms. NLP algorithms used here include GPT-3® and BERT.

[0232] From the summaries generated through analysis, the server extracts technical terms. Machine learning models and technical term dictionaries are used in this process. After the technical terms are extracted, the server references external databases and APIs (such as Wikidata and the Oxford Dictionaries API) to generate explanations for each term.

[0233] Furthermore, the emotion engine analyzes the user's emotional data. The emotion engine analyzes the emotions the user is feeling, such as anxiety and interest, and adjusts the explanatory content based on this. For example, if the user is feeling anxious, it provides a simplified explanation, additional background information, and illustrations.

[0234] Ultimately, the server integrates the summarized article and the glossary, adjusted with sentiment data, into a single response and sends it to the user's device. The device displays the received response on the user's screen, allowing the user to view the summarized article and the adjusted glossary simultaneously.

[0235] The following are some specific examples.

[0236] For example, suppose a user enters an article about the fundamentals of quantum computing and sends it to the server. Simultaneously, the user's emotional data, captured through the camera and microphone, is also sent. The server receives this article, analyzes it using a natural language processing algorithm, and generates a summary like the following:

[0237] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0238] Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and retrieves explanations by referring to external databases and APIs. The following is an example.

[0239] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0240] The emotion engine analyzes the user's anxieties and adds further explanations and diagrams to aid understanding. Finally, the server sends the generated summary article and refined glossary of technical terms to the user's device. The device screen displays information such as the following:

[0241] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0242] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0243] This allows users to efficiently understand specialized content by reviewing summarized articles and commentary tailored to their emotions.

[0244] As an example of a prompt statement, the following sentence is used as input to the generative AI model.

[0245] "Please summarize the article I will now enter and provide explanations for technical terms. Furthermore, please adjust the explanations according to the user's feelings. Users are feeling anxious, so they need simple and easy-to-understand explanations."

[0246] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0247] Step 1:

[0248] The user inputs an article using their device. Simultaneously, user sentiment data is acquired via the camera and microphone. The devices used include PCs, tablets, and smartphones. The input article data is acquired in text format, while sentiment data is acquired in image and audio format. For example, suppose a user inputs an article about the fundamentals of quantum computing.

[0249] Input: Text article, image (facial expression), audio (emotion) data

[0250] Output: Article data and sentiment data sent from the terminal.

[0251] Step 2:

[0252] The terminal sends the entered article data and sentiment data to the server. HTTPS is used as the communication protocol, and the data is encrypted. Transmission occurs in real time, ensuring security.

[0253] Input: Encrypted article data and sentiment data

[0254] Output: Data sent to the server

[0255] Step 3:

[0256] The server temporarily stores the received article data and sentiment data in a database. The databases used are MySQL and PostgreSQL. Data storage is performed for efficiency and reliability.

[0257] Input: Received article data and sentiment data

[0258] Output: Data stored in the database

[0259] Step 4:

[0260] The server analyzes article data using natural language processing algorithms. GPT-3 and BERT are used as NLP algorithms. First, the entire article is analyzed to extract key points and generate a summary.

[0261] Input: Saved article data

[0262] Output: Summarized article text

[0263] Step 5:

[0264] The server extracts technical terms from the generated summary text. In this process, machine learning models and technical term dictionaries are used to create a list of these terms.

[0265] Input: Summarized article text

[0266] Output: List of extracted technical terms

[0267] Step 6:

[0268] The server generates explanations for technical terms. It retrieves reliable information by referencing external databases and APIs (e.g., Wikidata, Oxford Dictionaries API).

[0269] Input: List of technical terms

[0270] Output: Explanation text for each technical term

[0271] Step 7:

[0272] The emotion engine analyzes the user's emotion data. Based on the analysis results, the explanation content is adjusted. For example, if the user is feeling anxious, additional explanations or diagrams are added.

[0273] Input: Saved emotion data

[0274] Output: Adjusted explanation text

[0275] <X Step 8:

[0276] The server integrates the summary article and the adjusted technical term explanation. This is generated as one response and sent to the user's terminal.

[0277] Input: Summary article, adjusted explanation text[[ID=X0000879]]

[0278] Output: Integrated response data

[0279] Step 9:

[0280] The terminal displays the received response data on the user's screen. The user can view the summarized article and the explanation of technical terms simultaneously.

[0281] <000089><0>Input: Integrated response data

[0282] Output: Summarized article and explanation text displayed on the terminal screen <000><00895> (Application Example 2)

[0284] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0285] In conventional systems, although it was possible to summarize specialized articles and explain technical terms, it was difficult to provide information according to the user's emotions. For this reason, users sometimes felt anxiety or confusion about parts they could not understand, which hindered efficient learning and understanding. In particular, in the explanation of technical terms, there was a problem that the learning effect decreased because the explanation according to the user's emotional state was insufficient.

[0286] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input an article and transmit the article to the server, means for the server to summarize the received article using a natural language processing algorithm, means for extracting technical terms from the summarized article, means for generating an explanation regarding the extracted technical terms, means for using an emotion engine that recognizes the user's emotion to obtain emotion data and adjust the summary and explanation generated based on the emotion data, and means for displaying the summarized article and the technical term explanation on the user terminal. Thereby, it becomes possible to provide optimal information according to the user's emotion, assist in understanding specialized articles, and improve the learning effect.

[0287] A "user" is a person who inputs an article and obtains information using this system.

[0288] An "article" is text data that a user inputs, transmits to the server, and is analyzed.

[0289] A "server" is a computer system that receives an article transmitted from a user, performs analysis, summarization, extraction, and explanation of technical terms, and returns the results to the user.

[0290] A "natural language processing algorithm" is an algorithm or program for analyzing a sentence and generating a summary.

[0291] A "summary" is the content that concisely summarizes the main points of the original article generated by a natural language processing algorithm.

[0292] "Technical jargon" refers to terms or words specific to a particular field or area that are generally difficult for the average person to understand.

[0293] "Explanation" refers to texts or diagrams that clearly explain the meaning and usage of technical terms.

[0294] An "emotion engine" is a system or software that analyzes a user's facial expressions and voice to acquire emotional data.

[0295] "Emotional data" refers to data obtained by the emotion engine that indicates the user's emotional state.

[0296] A "user terminal" is a device used by users to input articles, view summarized articles, or view explanations of technical terms.

[0297] This invention relates to a system that uses user sentiment data to aid user understanding in summarizing specialized articles and explaining technical terms. Specific embodiments of this system are described in detail below.

[0298] System Overview

[0299] The system mainly consists of the following components:

[0300] User terminal

[0301] server

[0302] Natural Language Processing Algorithms

[0303] Emotional Engine

[0304] External database or API

[0305] User terminal

[0306] The user terminal is a device for the user to input an article, receive, and display the result. Specifically, smart glasses, head-mounted displays, etc. can be used.

[0307] Server

[0308] The server is the core part that receives the article sent from the user, summarizes the article, extracts technical terms, and generates necessary explanations. Also, the server uses an emotion engine to process the user's emotion data and adjusts the information based on it.

[0309] Natural language processing algorithm

[0310] The server uses natural language processing algorithms for automatic article summarization and extraction of technical terms. Specifically, NLP libraries such as spaCy and Transformers are used.

[0311] Emotion engine

[0312] The emotion engine is a system that analyzes the user's facial expressions and voice to obtain emotion data. For example, emotion recognition is performed by using Affectiva API or OpenFace.

[0313] External database and API

[0314] To generate explanations for technical terms, the server accesses an external database or API to obtain reliable information. For example, information is obtained from a dictionary API or a specialized knowledge base.

[0315] Processing flow

[0316] The processing executed by the server is as follows:

[0317] 1. The user inputs an article and sends the article from the terminal to the server.

[0318] 2. The server summarizes the received articles using a natural language processing algorithm.

[0319] 3. Extract technical terms from the summarized article.

[0320] 4. Refer to an external database or API to generate explanations for the extracted technical terms.

[0321] 5. The emotion engine analyzes the user's emotional data and adjusts the generated summary and explanation based on the results.

[0322] 6. The server sends the edited summary article and glossary of technical terms to the user's terminal.

[0323] 7. The user's terminal displays the results.

[0324] Specific example

[0325] Suppose a user uses smart glasses in a virtual store to search for an article about the fundamentals of quantum computing. The smart glasses' camera captures the user's facial expressions, and an emotion engine retrieves the user's emotional data, recognizing surprise, anxiety, and other emotions. As a result, the glasses' display shows a summary stating, "Quantum computers use computational techniques with qubits to enable parallel processing and high-speed computation," along with additional explanations of "qubits" and "superconductivity" accompanied by simple diagrams.

[0326] Example of a prompt

[0327] text

[0328] Prompt message:

[0329] Please summarize the product description entered by the user in the following format.

[0330] Product description:

[0331] "Quantum computers are computing technologies that use qubits, and their characteristics include parallel processing and high-speed computation."

[0332] Expected Output:

[0333] "Quantum computers use computational techniques based on qubits to enable parallel processing and high-speed computation."

[0334] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by providing information that responds to the user's emotions.

[0335] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0336] Step 1:

[0337] The user enters an article and sends it from their device to the server. Input includes the user typing the article into a text field and pressing the submit button. Output is the article data received by the server.

[0338] Step 2:

[0339] The server summarizes the received articles using a natural language processing algorithm. The input is the article data received in step 1, and for data processing, NLP libraries such as spaCy or Transformers are used to extract the main points of the articles. The output is the summarized text.

[0340] Step 3:

[0341] The server extracts technical terms from the summarized article. The input is the summarized text generated in step 2, and the data processing uses algorithms to identify specific keywords or phrases (e.g., POS tagging or keyword extraction). The output is a list of the extracted technical terms.

[0342] Step 4:

[0343] The server references an external database or API to generate explanations for the extracted technical terms. The input is the list of technical terms obtained in step 3, and the API is called as a data operation to obtain a reliable explanation for each technical term. The output is the explanation information for each technical term.

[0344] Step 5:

[0345] The server uses an emotion engine to acquire and analyze user emotion data. The input is emotion data obtained from the user's facial expressions and voice, and the data analysis uses tools such as the Affectiva API and OpenFace to identify emotions. The output is an identification of the user's emotional state (surprise, anxiety, interest, etc.).

[0346] Step 6:

[0347] The server adjusts the generated summary and explanation based on sentiment data. The input is the summary and explanation information obtained in steps 2 and 4, and the sentiment data obtained in step 5. The data processing involves changing the content and expression of the summary and explanation according to the user's emotions. For example, if the user is feeling anxious, more detailed explanations or illustrations are added. The output is the adjusted summary and explanation information.

[0348] Step 7:

[0349] The server sends the compiled summary article and glossary of technical terms to the user terminal. The input is the compiled summary and glossary information from step 6, and the output is the data sent to the user terminal. The terminal displays the received data on the user's screen.

[0350] 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.

[0351] 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.

[0352] 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.

[0353] [Second Embodiment]

[0354] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0355] 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.

[0356] 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).

[0357] 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.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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".

[0366] This invention relates to a system for summarizing specialized and complex articles and providing explanations of technical terms, and its specific embodiments will be described in detail.

[0367] The user enters an article using a terminal. The article entered by the user is sent to the server. The server analyzes the received article using a natural language processing algorithm (e.g., an NLP library) and generates a summary. The generated summary is temporarily stored on the server.

[0368] Next, the server extracts technical terms from the summarized article. This extraction process uses specific technical term dictionaries and machine learning models. The extracted technical terms are listed, and their definitions are then generated. The server retrieves reliable definition information by referencing external databases and APIs. The retrieved definition information is organized, and definitions for multiple technical terms are generated at once.

[0369] Finally, the server compiles the summary article and explanations of technical terms and sends them to the user's terminal. The user's terminal displays the received information on the screen, allowing the user to simultaneously view a concise summary and detailed explanations of technical terms.

[0370] An explanation with specific examples is provided below.

[0371] A user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. The server receives the article, analyzes it using a natural language processing algorithm, and generates a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, it might generate the explanation, "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1."

[0372] Finally, the server sends the generated summary article and glossary of technical terms to the user's terminal. The user's terminal simultaneously displays the summary article, which states, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1."

[0373] In this way, the present invention enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The user uses a terminal to input a specialized and complex article. The entered article is then sent from the terminal to the server.

[0377] Step 2:

[0378] The server receives the submitted article data and stores it temporarily. The stored data is then passed to a natural language processing algorithm.

[0379] Step 3:

[0380] The server uses natural language processing algorithms to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0381] Step 4:

[0382] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article.

[0383] Step 5:

[0384] The server generates explanations for extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously.

[0385] Step 6:

[0386] The server compiles a summary article and explanations of technical terms, and sends them to the user's terminal as a single response.

[0387] Step 7:

[0388] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[0389] (Example 1)

[0390] 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."

[0391] In today's information society, quickly understanding specialized and complex articles and deepening related knowledge is a crucial challenge for many users. However, previous systems have lacked sufficient article summaries and explanations of technical terms, making it difficult for users to efficiently acquire information.

[0392] 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.

[0393] In this invention, the server includes means for a user to input an article and send the article to the server, means for the server to summarize the received article using a natural language processing algorithm, means for extracting keywords from the summarized article, means for generating explanations related to the extracted keywords, and means for displaying the summarized article and keyword explanations on the user's terminal. This makes it possible for users to quickly and efficiently understand specialized and complex articles and easily acquire related knowledge.

[0394] A "user" is a person or entity that inputs articles and uses the system.

[0395] An "article" is information or text that is entered by a user and processed by a system.

[0396] A "server" is a device or system that receives articles submitted by users, analyzes them, summarizes them, extracts keywords, generates keyword explanations, and transmits the information to the user's terminal.

[0397] A "natural language processing algorithm" is an algorithm that analyzes human language, understands its meaning, and performs processing based on that understanding.

[0398] "Keywords" are words or terms that are considered particularly important within a summarized article.

[0399] "Explanation" refers to descriptions and detailed information about the extracted keywords.

[0400] A "user terminal" is a device used by users to input articles and ultimately display summary articles and keyword explanations.

[0401] "External information sources" refer to external databases or internet resources that the server consults to obtain explanations about keywords.

[0402] An "interface" is the intermediary part that a server uses to communicate with external information sources.

[0403] This invention is a system for summarizing specialized and complex articles and providing explanations of related technical terms. The embodiments for carrying out this invention are described in detail below.

[0404] The user first enters an article using a terminal. This article is in text format and is usually entered manually into an input field such as a text field. An example of a prompt is "Please summarize an article on the fundamentals of quantum computing and provide explanations of technical terms." This allows the user to provide the system with a specific request.

[0405] The entered article is sent from the terminal to the server. This communication is generally done using an HTTP POST request. The server temporarily stores the received article in memory or a database.

[0406] The server applies natural language processing algorithms to the received articles. Examples of NLP libraries used include "spaCy" and "Transformers." Using these libraries, the server performs grammatical analysis of the articles and extracts key points. Based on this analysis, it summarizes the important content of the articles.

[0407] Next, the server extracts keywords from the summarized article. This keyword extraction uses specific vocabulary dictionaries or machine learning models (e.g., the BERT model). Then, it retrieves explanatory information for the extracted keywords by referencing external sources and interfaces (e.g., the Wikipedia API or the PubMed API). This allows for the automatic generation of reliable explanations.

[0408] The generated summary and keyword explanations are combined into a single packet and sent back to the terminal as an HTTP response.

[0409] Finally, the device displays the received information on its screen. Through the user interface, the summary article and keyword explanations are presented to the user in an easy-to-read format. For example, it might look like this:

[0410] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible." (Summary)

[0411] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1." (Explanation)

[0412] In this way, the present invention provides a system that enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0413] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0414] Step 1:

[0415] The user enters an article. The entered article is placed in the terminal's text field. For example, the user might enter "An article about the fundamentals of quantum computing." The input here is in string format, and when the submit button is pressed, the process proceeds to the next step.

[0416] Step 2:

[0417] The terminal sends the entered article to the server. Specifically, it sends the article data to the server using an HTTP POST request. The input data is in string format and is POSTed to the destination URL. The output is the article data received by the server.

[0418] Step 3:

[0419] The server applies natural language processing algorithms to analyze the articles it receives. First, the server temporarily stores the received data in memory or a database. Next, it uses an NLP library (e.g., spaCy or Transformers) to perform grammatical analysis and topic extraction of the articles. The input for this analysis is the stored article data, and the output is data showing the structure of the text and its main points.

[0420] Step 4:

[0421] The server generates a summary. Based on the key points extracted in step 3, a summary sentence is created using a natural language processing algorithm. This process uses specific rules and deep learning models. The input is the data of key points, and the output is a summary sentence (for example, "Quantum computing is a computational technique that uses qubits...").

[0422] Step 5:

[0423] The server extracts keywords from the summary. It analyzes the summary and extracts specific technical terms and important keywords using dictionaries or machine learning models (e.g., the BERT model). The input is the summary, and the output is a list of extracted keywords.

[0424] Step 6:

[0425] The server generates definitions for keywords. For the extracted keywords, it references external sources and interfaces (e.g., Wikipedia API or PubMed API) to obtain detailed definitions. The input is a list of keywords, and the output is a definition corresponding to each keyword (e.g., "qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1.").

[0426] Step 7:

[0427] The server sends a summary and keyword explanations to the terminal. The generated summary and explanation are combined into a single packet and sent to the terminal as an HTTP response. The input is the summary and explanation, and the output is the packet sent to the terminal.

[0428] Step 8:

[0429] The terminal displays the information it receives to the user. The terminal analyzes the received packets and displays a summary and explanatory text in an appropriate format on the user interface. The input is the packets sent from the server, and the output is the information displayed on the user's screen. Specifically, it is displayed using HTML and CSS in the following format:

[0430] Summary: "Quantum computing is a computational technique that uses qubits..."

[0431] Explanation: "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0432] (Application Example 1)

[0433] 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."

[0434] Traditional technical documents and manuals for factory workers are often highly specialized and difficult to understand. Especially when introducing new technologies or systems, workers are expected to learn them quickly, but understanding technical terminology can be time-consuming. Furthermore, there is a need for efficient information delivery methods utilizing devices such as smart glasses and head-mounted displays. Therefore, a system is needed that efficiently summarizes specialized technical documents and provides explanations for complex technical terms.

[0435] 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.

[0436] In this invention, the server includes means for a user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for displaying the summarized article and explanations of technical terms on a user terminal; means for using a head-mounted display or smart glasses for factory workers to view technical documents on equipment; means for automatically generating a summary of key points when a user views a technical document; and means for providing explanations of difficult technical terms. This enables workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0437] A "user" refers to a person who uses the system to input articles and receive information.

[0438] "Article" refers to text information entered or submitted by a user.

[0439] A "server" refers to a central processing unit that processes articles received from users and generates summaries and explanations of technical terms.

[0440] "Natural language processing algorithms" refer to technical methods used to analyze articles and generate summaries.

[0441] A "summary" refers to a concise summary extracted from the original article.

[0442] "Technical terms" refer to terms specific to a particular field.

[0443] "Explanation" refers to additional explanatory information regarding technical terms.

[0444] "User terminal" refers to a device used to display summary articles and explanations of technical terms.

[0445] "Factory worker" refers to a person who uses technical documents and manuals within a factory.

[0446] "Equipment" refers to information display devices such as head-mounted displays and smart glasses.

[0447] A "head-mounted display" refers to a device worn on the head that displays information in the field of vision.

[0448] "Smart glasses" refers to glasses-type information display devices.

[0449] "Technical documents" refer to specialized documents related to operations and systems within a factory.

[0450] "Automatically generating a summary of key points" means that the system automatically creates a summary without requiring any specific user action.

[0451] Modes for carrying out the invention

[0452] This invention is a system for factory workers to efficiently understand technical documents and manuals while working and to quickly acquire new technologies. In this system, the user inputs technical documents, the server processes the information to generate summaries and explanations of technical terms, and displays them on the user's terminal.

[0453] System program

[0454] The system program includes the following steps:

[0455] 1. Data entry

[0456] The user enters technical documentation into a terminal and sends the article to the server.

[0457] 2. Data Analysis and Summary Generation

[0458] The server analyzes the received articles using natural language processing algorithms (NLP libraries) and generates summaries. For this purpose, libraries such as Hugging Face Transformers are used.

[0459] 3. Extraction of technical terms and generation of explanations

[0460] This process extracts technical terms from summarized articles and generates explanations for those terms. External databases and APIs are referenced for generating these explanations. Specific technical term dictionaries and machine learning models are also utilized.

[0461] 4. Display of Information

[0462] The generated summary article and glossary of technical terms are sent to the user's device for display. Display can be done using a head-mounted display or smart glasses (e.g., Google Glass, Vuzix Blade, Microsoft HoloLens, Magic Leap One, etc.).

[0463] Hardware and software usage

[0464] 1. Hardware

[0465] Head-mounted displays or smart glasses: These are used by workers to display technical documentation and obtain explanations on the device.

[0466] 2. Software

[0467] Natural language processing algorithms such as Hugging Face Transformers are used to analyze articles and generate summaries.

[0468] Specialized terminology dictionaries and machine learning models: Used to extract specialized terms and generate explanations for them.

[0469] External database or API: Used to retrieve explanations of technical terms.

[0470] Specific example

[0471] For example, if a factory worker wants to read the "Introductory Manual for a New Robot Control System," the content will be displayed on smart glasses (e.g., Vuzix Blade). In this case, the application will automatically generate a summary of the manual and provide explanations of technical terms such as "robot control system," "feedback loop," and "PID control."

[0472] Example of a prompt

[0473] Technical document: "Implementation Manual for the New Robot Control System"

[0474] Objective: To provide summaries of key points and explanations of technical terms, enabling workers to quickly and easily understand and learn the content.

[0475] In this way, the system of the present invention enables factory workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] Data entry

[0479] Input: The user enters technical documents into the terminal.

[0480] Data processing: The terminal converts technical documents entered by the user into a digital format.

[0481] Output: Send the technical document data, converted to digital format, to the server.

[0482] Step 2:

[0483] Data analysis and summary generation

[0484] Input: The server receives technical document data from the terminal.

[0485] Data processing: The server analyzes the text using natural language processing algorithms (e.g., Hugging Face Transformers) and extracts the main content.

[0486] Output: Generates a summarized technical document.

[0487] Step 3:

[0488] Technical term extraction

[0489] Input: The server receives a summarized technical document.

[0490] Data processing: Extract specialized terminology from summary articles using specific vocabulary dictionaries and machine learning models.

[0491] Output: Generates a list of extracted technical terms.

[0492] Step 4:

[0493] Generation of technical terminology explanations

[0494] Input: The server receives a list of extracted technical terms.

[0495] Data processing: The server retrieves explanatory information about each technical term by referring to external databases and APIs.

[0496] Output: Generates a list of explanations for technical terms.

[0497] Step 5:

[0498] Displaying information

[0499] Input: The server integrates summary articles and glossaries of technical terms.

[0500] Data processing: Converting data into a format that is easy for user devices (head-mounted displays and smart glasses) to display.

[0501] Output: Sends a summary article and a glossary of technical terms to the user's terminal.

[0502] Step 6:

[0503] User interaction

[0504] Input: Users view summary articles and glossaries of technical terms displayed via their device.

[0505] Data processing: The device records the user's actions and browsing history.

[0506] Output: Sends user feedback and operation logs to the server.

[0507] This completes a series of processing steps, allowing factory workers to efficiently understand summaries of technical documents and explanations of technical terms.

[0508] 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.

[0509] This invention combines a system that summarizes specialized and complex articles and provides explanations of technical terms with an emotion engine that recognizes the user's emotions, and a specific embodiment thereof will be described in detail.

[0510] The user enters an article using their device. The article entered by the user is sent from the device to the server. This transmitted data also includes the user's emotional data. Emotional data is often obtained from the user's facial expressions and voice.

[0511] The server receives the submitted article data and sentiment data and stores it temporarily. The stored data is then passed to a natural language processing algorithm. The server uses the natural language processing algorithm to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0512] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list technical terms within the article.

[0513] Next, the server generates explanations for the extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously. Furthermore, the sentiment engine analyzes the user's emotional data and adjusts the display method and content based on the results. For example, if the user is feeling surprised or anxious, it provides a simplified explanation or additional background information accordingly.

[0514] The server compiles a summary article and a glossary of technical terms adjusted based on sentiment data, and sends it to the user's device as a single response. The device displays the received response on the user's screen. The user can simultaneously view the summary article and the sentiment-adjusted glossary of technical terms.

[0515] An explanation with specific examples is provided below.

[0516] The user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. In addition, the user's emotions are captured via the camera and microphone, and this data is analyzed by an emotion engine. The server receives the article and analyzes it using a natural language processing algorithm to generate a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, an explanation such as "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1" is generated. The emotion engine further recognizes if the user is feeling anxious and adds additional explanations and diagrams to the explanation to aid understanding.

[0517] Finally, the server sends the generated summary article and a refined glossary of technical terms to the user's terminal. The user's terminal displays the summary article, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1." Additional illustrations and explanations may also be included depending on the user's preferences.

[0518] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by appropriately providing information according to the user's emotional state.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user uses their device to input specialized and complex articles. The user can either paste the article into the input field or type it directly. Along with the entered article, permission is granted to collect emotional data from the user's facial expressions and voice using the camera and microphone.

[0522] Step 2:

[0523] The device sends articles and sentiment data received from the user to the server. Sentiment data includes facial expressions, tone of voice, and speed.

[0524] Step 3:

[0525] The server temporarily stores the received article data and passes it to a natural language processing algorithm. It also passes sentiment data to an emotion engine to begin analysis.

[0526] Step 4:

[0527] The server uses a natural language processing algorithm to analyze the article, extract the main points, and generate a summary. The generated summary is then stored on the server.

[0528] Step 5:

[0529] The server extracts technical terms from the summary article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article. The listed technical terms are then passed on to the next step.

[0530] Step 6:

[0531] The server generates explanations for extracted technical terms. It retrieves reliable information by referencing external databases and APIs, and creates explanations for each technical term. The explanations are then compiled and stored on the server.

[0532] Step 7:

[0533] The server receives user sentiment data analyzed by the sentiment engine. The sentiment engine analyzes the user's emotional state while reading the article and provides the results to the server.

[0534] Step 8:

[0535] The server adjusts the display and content of summary articles and glossaries based on user sentiment data. For example, if a user is feeling anxious or surprised, the explanation may be rephrased in simpler language or additional illustrations may be provided.

[0536] Step 9:

[0537] The server compiles a summary article and explanations of technical terms, adjusted based on sentiment data, and sends them to the user's terminal as a single response.

[0538] Step 10:

[0539] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of specialized terminology that are adjusted according to the user's sentiment. For example, if additional explanations or illustrations based on sentiment are included, these are also displayed to aid the user's understanding.

[0540] This process allows users to quickly and efficiently understand specialized and complex articles, and receive support tailored to their emotional needs.

[0541] (Example 2)

[0542] 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".

[0543] Conventional technologies were unable to provide summaries or explanations of technical terms to efficiently understand specialized and complex articles. Furthermore, they failed to deliver information in a way that resonated with users' emotions, making it difficult to deepen their understanding. Therefore, there was a need for an effective system that would allow users to quickly understand specialized articles and efficiently acquire related knowledge.

[0544] 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.

[0545] In this invention, the server includes means for the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for an emotion engine to analyze the user's emotional data and adjust the content of the explanations; and means for displaying the summarized article and the adjusted technical term explanations on the user's terminal. This enables users to quickly and effectively understand specialized and complex articles, and provides appropriate information tailored to the user's emotions, resulting in a more effective learning experience.

[0546] A "user" refers to a person who uses a computer system to input articles and receive services.

[0547] A "terminal" is a device used by a user to input information and display it, such as a personal computer, tablet, or smartphone.

[0548] A "server" refers to a computer system that receives and processes data sent by users.

[0549] "Article data" refers to text data containing specialized information entered by users using their devices.

[0550] "Emotional data" refers to data that indicates the user's emotional state, and includes facial expressions and voice information acquired through cameras and microphones.

[0551] "Natural language processing algorithms" refer to programming techniques that analyze input text data and extract summaries and key information.

[0552] A "summary" refers to a shortened version of an article containing the main points, generated by the server using a natural language processing algorithm.

[0553] "Technical jargon" refers to terms specific to a particular field or area of ​​expertise, and is a specialized term that may be difficult for users to understand.

[0554] "Explanation" refers to text information generated by the server to provide a detailed explanation of technical terms.

[0555] An "emotion engine" refers to software technology that analyzes user emotion data and adjusts the displayed content based on the results.

[0556] An "external database" refers to an external information storage location that a server references to provide explanatory information on technical terms.

[0557] An "API" refers to an interface that allows different software systems to exchange information.

[0558] "Display" refers to showing the response from the server on the terminal screen.

[0559] This invention provides a system that enables users to efficiently understand specialized and complex articles. This system not only provides article summaries and explanations of technical terms, but also has the function of recognizing the user's emotions and adjusting the information accordingly. The specific configuration and operation are described in detail below.

[0560] First, the user enters the article using their device and sends that data to the server. The device can be a PC, tablet, or smartphone. As the user enters the article, emotional data (facial expressions and voice) is captured through the device's camera and microphone and sent to the server along with the article data.

[0561] The server temporarily stores the received article data and sentiment data. High-performance databases such as MySQL or PostgreSQL are used for storage. Next, the server analyzes the articles and extracts key points using natural language processing (NLP) algorithms. NLP algorithms used here include GPT-3 and BERT.

[0562] From the summaries generated through analysis, the server extracts technical terms. Machine learning models and technical term dictionaries are used in this process. After the technical terms are extracted, the server references external databases and APIs (such as Wikidata and the Oxford Dictionaries API) to generate explanations for each term.

[0563] Furthermore, the emotion engine analyzes the user's emotional data. The emotion engine analyzes the emotions the user is feeling, such as anxiety and interest, and adjusts the explanatory content based on this. For example, if the user is feeling anxious, it provides a simplified explanation, additional background information, and illustrations.

[0564] Ultimately, the server integrates the summarized article and the glossary, adjusted with sentiment data, into a single response and sends it to the user's device. The device displays the received response on the user's screen, allowing the user to view the summarized article and the adjusted glossary simultaneously.

[0565] The following are some specific examples.

[0566] For example, suppose a user enters an article about the fundamentals of quantum computing and sends it to the server. Simultaneously, the user's emotional data, captured through the camera and microphone, is also sent. The server receives this article, analyzes it using a natural language processing algorithm, and generates a summary like the following:

[0567] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0568] Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and retrieves explanations by referring to external databases and APIs. The following is an example.

[0569] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0570] The emotion engine analyzes the user's anxieties and adds further explanations and diagrams to aid understanding. Finally, the server sends the generated summary article and refined glossary of technical terms to the user's device. The device screen displays information such as the following:

[0571] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0572] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0573] This allows users to efficiently understand specialized content by reviewing summarized articles and commentary tailored to their emotions.

[0574] As an example of a prompt statement, the following sentence is used as input to the generative AI model.

[0575] "Please summarize the article I will now enter and provide explanations for technical terms. Furthermore, please adjust the explanations according to the user's feelings. Users are feeling anxious, so they need simple and easy-to-understand explanations."

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The user inputs an article using their device. Simultaneously, user sentiment data is acquired via the camera and microphone. The devices used include PCs, tablets, and smartphones. The input article data is acquired in text format, while sentiment data is acquired in image and audio format. For example, suppose a user inputs an article about the fundamentals of quantum computing.

[0579] Input: Text article, image (facial expression), audio (emotion) data

[0580] Output: Article data and sentiment data sent from the terminal.

[0581] Step 2:

[0582] The terminal sends the entered article data and sentiment data to the server. HTTPS is used as the communication protocol, and the data is encrypted. Transmission occurs in real time, ensuring security.

[0583] Input: Encrypted article data and sentiment data

[0584] Output: Data sent to the server

[0585] Step 3:

[0586] The server temporarily stores the received article data and sentiment data in a database. The databases used are MySQL and PostgreSQL. Data storage is performed for efficiency and reliability.

[0587] Input: Received article data and sentiment data

[0588] Output: Data stored in the database

[0589] Step 4:

[0590] The server analyzes article data using natural language processing algorithms. GPT-3 and BERT are used as NLP algorithms. First, the entire article is analyzed to extract key points and generate a summary.

[0591] Input: Saved article data

[0592] Output: Summarized article text

[0593] Step 5:

[0594] The server extracts technical terms from the generated summary text. In this process, machine learning models and technical term dictionaries are used to create a list of these terms.

[0595] Input: Summarized article text

[0596] Output: List of extracted technical terms

[0597] Step 6:

[0598] The server generates explanations for technical terms. It retrieves reliable information by referencing external databases and APIs (e.g., Wikidata, Oxford Dictionaries API).

[0599] Input: List of technical terms

[0600] Output: Explanatory text for each technical term

[0601] Step 7:

[0602] The emotion engine analyzes the user's emotional data. Based on the analysis results, it adjusts the explanations. For example, if the user is feeling anxious, it adds additional explanations or diagrams.

[0603] Input: Saved emotion data

[0604] Output: Adjusted explanatory text

[0605] Step 8:

[0606] The server integrates the summary article and the refined glossary of technical terms. It then generates this as a single response and sends it to the user's terminal.

[0607] Input: Summary article, edited explanatory text

[0608] Output: Integrated response data

[0609] Step 9:

[0610] The response data received by the device is displayed on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[0611] Input: Integrated response data

[0612] Output: Summary article and explanatory text displayed on the terminal screen.

[0613] (Application Example 2)

[0614] 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."

[0615] Traditional systems could summarize specialized articles and explain technical terms, but they struggled to provide information that resonated with the user's emotions. This often led to anxiety and confusion regarding parts the user didn't understand, hindering efficient learning and comprehension. In particular, the lack of explanations that considered the user's emotional state in the explanations of technical terms reduced learning effectiveness.

[0616] 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 the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations related to the extracted technical terms; means for acquiring emotion data using an emotion engine that recognizes the user's emotions and adjusting the summary and explanation generated based on that data; and means for displaying the summarized article and the explanation of technical terms on the user terminal. This makes it possible to provide optimal information in accordance with the user's emotions, which helps in understanding technical articles and improves learning effectiveness.

[0617] A "user" is someone who uses this system to input articles and obtain information.

[0618] An "article" is text data that a user inputs, sends to the server, and is then analyzed.

[0619] A "server" is a computer system that receives articles submitted by users, analyzes and summarizes them, extracts and explains technical terms, and then returns the results to the user.

[0620] A "natural language processing algorithm" refers to an algorithm or program used to analyze text and generate a summary.

[0621] A "summary" is a concise summary of the main points of an original article, generated by a natural language processing algorithm.

[0622] "Technical jargon" refers to terms or words specific to a particular field or area that are generally difficult for the average person to understand.

[0623] "Explanation" refers to texts or diagrams that clearly explain the meaning and usage of technical terms.

[0624] An "emotion engine" is a system or software that analyzes a user's facial expressions and voice to acquire emotional data.

[0625] "Emotional data" refers to data obtained by the emotion engine that indicates the user's emotional state.

[0626] A "user terminal" is a device used by users to input articles, view summarized articles, or view explanations of technical terms.

[0627] This invention relates to a system that uses user sentiment data to aid user understanding in summarizing specialized articles and explaining technical terms. Specific embodiments of this system are described in detail below.

[0628] System Overview

[0629] The system mainly consists of the following components:

[0630] User terminal

[0631] server

[0632] Natural Language Processing Algorithms

[0633] Emotional Engine

[0634] External database or API

[0635] User terminal

[0636] The user terminal is a device used by the user to input articles, receive the results, and display them. Specifically, smart glasses or head-mounted displays can be used.

[0637] server

[0638] The server is the core component that receives articles submitted by users, summarizes them, extracts technical terms, and generates necessary explanations. The server also uses a sentiment engine to process user sentiment data and adjust the information accordingly.

[0639] Natural Language Processing Algorithms

[0640] The server uses natural language processing algorithms for automatic text summarization and extraction of technical terms. Specifically, NLP libraries such as spaCy and Transformers are used.

[0641] Emotional Engine

[0642] An emotion engine is a system that analyzes a user's facial expressions and voice to acquire emotional data. For example, it performs emotion recognition using the Affectiva API or OpenFace.

[0643] External databases and APIs

[0644] To generate definitions of technical terms, the server accesses external databases or APIs to obtain reliable information. For example, it might retrieve information from a dictionary API or a knowledge base.

[0645] Processing flow

[0646] The server will perform the following actions:

[0647] 1. The user enters an article and sends it from their device to the server.

[0648] 2. The server summarizes the received articles using a natural language processing algorithm.

[0649] 3. Extract technical terms from the summarized article.

[0650] 4. Refer to an external database or API to generate explanations for the extracted technical terms.

[0651] 5. The emotion engine analyzes the user's emotional data and adjusts the generated summary and explanation based on the results.

[0652] 6. The server sends the edited summary article and glossary of technical terms to the user's terminal.

[0653] 7. The user terminal displays the results.

[0654] Specific example

[0655] Suppose a user uses smart glasses in a virtual store to search for an article about the fundamentals of quantum computing. The smart glasses' camera captures the user's facial expressions, and an emotion engine retrieves the user's emotional data, recognizing surprise, anxiety, and other emotions. As a result, the glasses' display shows a summary stating, "Quantum computers use computational techniques with qubits to enable parallel processing and high-speed computation," along with additional explanations of "qubits" and "superconductivity" accompanied by simple diagrams.

[0656] Example of a prompt

[0657] text

[0658] Prompt message:

[0659] Please summarize the product description entered by the user in the following format.

[0660] Product description:

[0661] "Quantum computers are computing technologies that use qubits, and their characteristics include parallel processing and high-speed computation."

[0662] Expected Output:

[0663] "Quantum computers use computational techniques based on qubits to enable parallel processing and high-speed computation."

[0664] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by providing information that responds to the user's emotions.

[0665] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0666] Step 1:

[0667] The user enters an article and sends it from their device to the server. Input includes the user typing the article into a text field and pressing the submit button. Output is the article data received by the server.

[0668] Step 2:

[0669] The server summarizes the received articles using a natural language processing algorithm. The input is the article data received in step 1, and for data processing, NLP libraries such as spaCy or Transformers are used to extract the main points of the articles. The output is the summarized text.

[0670] Step 3:

[0671] The server extracts technical terms from the summarized article. The input is the summarized text generated in step 2, and the data processing uses algorithms to identify specific keywords or phrases (e.g., POS tagging or keyword extraction). The output is a list of the extracted technical terms.

[0672] Step 4:

[0673] The server references an external database or API to generate explanations for the extracted technical terms. The input is the list of technical terms obtained in step 3, and the API is called as a data operation to obtain a reliable explanation for each technical term. The output is the explanation information for each technical term.

[0674] Step 5:

[0675] The server uses an emotion engine to acquire and analyze user emotion data. The input is emotion data obtained from the user's facial expressions and voice, and the data analysis uses tools such as the Affectiva API and OpenFace to identify emotions. The output is an identification of the user's emotional state (surprise, anxiety, interest, etc.).

[0676] Step 6:

[0677] The server adjusts the generated summary and explanation based on sentiment data. The input is the summary and explanation information obtained in steps 2 and 4, and the sentiment data obtained in step 5. The data processing involves changing the content and expression of the summary and explanation according to the user's emotions. For example, if the user is feeling anxious, more detailed explanations or illustrations are added. The output is the adjusted summary and explanation information.

[0678] Step 7:

[0679] The server sends the compiled summary article and glossary of technical terms to the user terminal. The input is the compiled summary and glossary information from step 6, and the output is the data sent to the user terminal. The terminal displays the received data on the user's screen.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] [Third Embodiment]

[0684] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0685] 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.

[0686] 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).

[0687] 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.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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".

[0696] This invention relates to a system for summarizing specialized and complex articles and providing explanations of technical terms, and its specific embodiments will be described in detail.

[0697] The user enters an article using a terminal. The article entered by the user is sent to the server. The server analyzes the received article using a natural language processing algorithm (e.g., an NLP library) and generates a summary. The generated summary is temporarily stored on the server.

[0698] Next, the server extracts technical terms from the summarized article. This extraction process uses specific technical term dictionaries and machine learning models. The extracted technical terms are listed, and their definitions are then generated. The server retrieves reliable definition information by referencing external databases and APIs. The retrieved definition information is organized, and definitions for multiple technical terms are generated at once.

[0699] Finally, the server compiles the summary article and explanations of technical terms and sends them to the user's terminal. The user's terminal displays the received information on the screen, allowing the user to simultaneously view a concise summary and detailed explanations of technical terms.

[0700] An explanation with specific examples is provided below.

[0701] A user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. The server receives the article, analyzes it using a natural language processing algorithm, and generates a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, it might generate the explanation, "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1."

[0702] Finally, the server sends the generated summary article and glossary of technical terms to the user's terminal. The user's terminal simultaneously displays the summary article, which states, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1."

[0703] In this way, the present invention enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] The user uses a terminal to input a specialized and complex article. The entered article is then sent from the terminal to the server.

[0707] Step 2:

[0708] The server receives the submitted article data and stores it temporarily. The stored data is then passed to a natural language processing algorithm.

[0709] Step 3:

[0710] The server uses natural language processing algorithms to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0711] Step 4:

[0712] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article.

[0713] Step 5:

[0714] The server generates explanations for extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously.

[0715] Step 6:

[0716] The server compiles a summary article and explanations of technical terms, and sends them to the user's terminal as a single response.

[0717] Step 7:

[0718] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[0719] (Example 1)

[0720] 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."

[0721] In today's information society, quickly understanding specialized and complex articles and deepening related knowledge is a crucial challenge for many users. However, previous systems have lacked sufficient article summaries and explanations of technical terms, making it difficult for users to efficiently acquire information.

[0722] 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.

[0723] In this invention, the server includes means for a user to input an article and send the article to the server, means for the server to summarize the received article using a natural language processing algorithm, means for extracting keywords from the summarized article, means for generating explanations related to the extracted keywords, and means for displaying the summarized article and keyword explanations on the user's terminal. This makes it possible for users to quickly and efficiently understand specialized and complex articles and easily acquire related knowledge.

[0724] A "user" is a person or entity that inputs articles and uses the system.

[0725] An "article" is information or text that is entered by a user and processed by a system.

[0726] A "server" is a device or system that receives articles submitted by users, analyzes them, summarizes them, extracts keywords, generates keyword explanations, and transmits the information to the user's terminal.

[0727] A "natural language processing algorithm" is an algorithm that analyzes human language, understands its meaning, and performs processing based on that understanding.

[0728] "Keywords" are words or terms that are considered particularly important within a summarized article.

[0729] "Explanation" refers to descriptions and detailed information about the extracted keywords.

[0730] A "user terminal" is a device used by users to input articles and ultimately display summary articles and keyword explanations.

[0731] "External information sources" refer to external databases or internet resources that the server consults to obtain explanations about keywords.

[0732] An "interface" is the intermediary part that a server uses to communicate with external information sources.

[0733] This invention is a system for summarizing specialized and complex articles and providing explanations of related technical terms. The embodiments for carrying out this invention are described in detail below.

[0734] The user first enters an article using a terminal. This article is in text format and is usually entered manually into an input field such as a text field. An example of a prompt is "Please summarize an article on the fundamentals of quantum computing and provide explanations of technical terms." This allows the user to provide the system with a specific request.

[0735] The entered article is sent from the terminal to the server. This communication is generally done using an HTTP POST request. The server temporarily stores the received article in memory or a database.

[0736] The server applies natural language processing algorithms to the received articles. Examples of NLP libraries used include "spaCy" and "Transformers." Using these libraries, the server performs grammatical analysis of the articles and extracts key points. Based on this analysis, it summarizes the important content of the articles.

[0737] Next, the server extracts keywords from the summarized article. This keyword extraction uses specific vocabulary dictionaries or machine learning models (e.g., the BERT model). Then, it retrieves explanatory information for the extracted keywords by referencing external sources and interfaces (e.g., the Wikipedia API or the PubMed API). This allows for the automatic generation of reliable explanations.

[0738] The generated summary and keyword explanations are combined into a single packet and sent back to the terminal as an HTTP response.

[0739] Finally, the device displays the received information on its screen. Through the user interface, the summary article and keyword explanations are presented to the user in an easy-to-read format. For example, it might look like this:

[0740] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible." (Summary)

[0741] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1." (Explanation)

[0742] In this way, the present invention provides a system that enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[0743] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0744] Step 1:

[0745] The user enters an article. The entered article is placed in the terminal's text field. For example, the user might enter "An article about the fundamentals of quantum computing." The input here is in string format, and when the submit button is pressed, the process proceeds to the next step.

[0746] Step 2:

[0747] The terminal sends the entered article to the server. Specifically, it sends the article data to the server using an HTTP POST request. The input data is in string format and is POSTed to the destination URL. The output is the article data received by the server.

[0748] Step 3:

[0749] The server applies natural language processing algorithms to analyze the articles it receives. First, the server temporarily stores the received data in memory or a database. Next, it uses an NLP library (e.g., spaCy or Transformers) to perform grammatical analysis and topic extraction of the articles. The input for this analysis is the stored article data, and the output is data showing the structure of the text and its main points.

[0750] Step 4:

[0751] The server generates a summary. Based on the key points extracted in step 3, a summary sentence is created using a natural language processing algorithm. This process uses specific rules and deep learning models. The input is the data of key points, and the output is a summary sentence (for example, "Quantum computing is a computational technique that uses qubits...").

[0752] Step 5:

[0753] The server extracts keywords from the summary. It analyzes the summary and extracts specific technical terms and important keywords using dictionaries or machine learning models (e.g., the BERT model). The input is the summary, and the output is a list of extracted keywords.

[0754] Step 6:

[0755] The server generates definitions for keywords. For the extracted keywords, it references external sources and interfaces (e.g., Wikipedia API or PubMed API) to obtain detailed definitions. The input is a list of keywords, and the output is a definition corresponding to each keyword (e.g., "qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1.").

[0756] Step 7:

[0757] The server sends a summary and keyword explanations to the terminal. The generated summary and explanation are combined into a single packet and sent to the terminal as an HTTP response. The input is the summary and explanation, and the output is the packet sent to the terminal.

[0758] Step 8:

[0759] The terminal displays the information it receives to the user. The terminal analyzes the received packets and displays a summary and explanatory text in an appropriate format on the user interface. The input is the packets sent from the server, and the output is the information displayed on the user's screen. Specifically, it is displayed using HTML and CSS in the following format:

[0760] Summary: "Quantum computing is a computational technique that uses qubits..."

[0761] Explanation: "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0762] (Application Example 1)

[0763] 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."

[0764] Traditional technical documents and manuals for factory workers are often highly specialized and difficult to understand. Especially when introducing new technologies or systems, workers are expected to learn them quickly, but understanding technical terminology can be time-consuming. Furthermore, there is a need for efficient information delivery methods utilizing devices such as smart glasses and head-mounted displays. Therefore, a system is needed that efficiently summarizes specialized technical documents and provides explanations for complex technical terms.

[0765] 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.

[0766] In this invention, the server includes means for a user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for displaying the summarized article and explanations of technical terms on a user terminal; means for using a head-mounted display or smart glasses for factory workers to view technical documents on equipment; means for automatically generating a summary of key points when a user views a technical document; and means for providing explanations of difficult technical terms. This enables workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0767] A "user" refers to a person who uses the system to input articles and receive information.

[0768] "Article" refers to text information entered or submitted by a user.

[0769] A "server" refers to a central processing unit that processes articles received from users and generates summaries and explanations of technical terms.

[0770] "Natural language processing algorithms" refer to technical methods used to analyze articles and generate summaries.

[0771] A "summary" refers to a concise summary extracted from the original article.

[0772] "Technical terms" refer to terms specific to a particular field.

[0773] "Explanation" refers to additional explanatory information regarding technical terms.

[0774] "User terminal" refers to a device used to display summary articles and explanations of technical terms.

[0775] "Factory worker" refers to a person who uses technical documents and manuals within a factory.

[0776] "Equipment" refers to information display devices such as head-mounted displays and smart glasses.

[0777] A "head-mounted display" refers to a device worn on the head that displays information in the field of vision.

[0778] "Smart glasses" refers to glasses-type information display devices.

[0779] "Technical documents" refer to specialized documents related to operations and systems within a factory.

[0780] "Automatically generating a summary of key points" means that the system automatically creates a summary without requiring any specific user action.

[0781] Modes for carrying out the invention

[0782] This invention is a system for factory workers to efficiently understand technical documents and manuals while working and to quickly acquire new technologies. In this system, the user inputs technical documents, the server processes the information to generate summaries and explanations of technical terms, and displays them on the user's terminal.

[0783] System program

[0784] The system program includes the following steps:

[0785] 1. Data entry

[0786] The user enters technical documentation into a terminal and sends the article to the server.

[0787] 2. Data Analysis and Summary Generation

[0788] The server analyzes the received articles using natural language processing algorithms (NLP libraries) and generates summaries. For this purpose, libraries such as Hugging Face Transformers are used.

[0789] 3. Extraction of technical terms and generation of explanations

[0790] This process extracts technical terms from summarized articles and generates explanations for those terms. External databases and APIs are referenced for generating these explanations. Specific technical term dictionaries and machine learning models are also utilized.

[0791] 4. Display of Information

[0792] The generated summary article and glossary of technical terms are sent to the user's device for display. Display can be done using a head-mounted display or smart glasses (e.g., Google Glass, Vuzix Blade, Microsoft HoloLens, Magic Leap One, etc.).

[0793] Hardware and software usage

[0794] 1. Hardware

[0795] Head-mounted displays or smart glasses: These are used by workers to display technical documentation and obtain explanations on the device.

[0796] 2. Software

[0797] Natural language processing algorithms such as Hugging Face Transformers are used to analyze articles and generate summaries.

[0798] Specialized terminology dictionaries and machine learning models: Used to extract specialized terms and generate explanations for them.

[0799] External database or API: Used to retrieve explanations of technical terms.

[0800] Specific example

[0801] For example, if a factory worker wants to read the "Introductory Manual for a New Robot Control System," the content will be displayed on smart glasses (e.g., Vuzix Blade). In this case, the application will automatically generate a summary of the manual and provide explanations of technical terms such as "robot control system," "feedback loop," and "PID control."

[0802] Example of a prompt

[0803] Technical document: "Implementation Manual for the New Robot Control System"

[0804] Objective: To provide summaries of key points and explanations of technical terms, enabling workers to quickly and easily understand and learn the content.

[0805] In this way, the system of the present invention enables factory workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[0806] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0807] Step 1:

[0808] Data entry

[0809] Input: The user enters technical documents into the terminal.

[0810] Data processing: The terminal converts technical documents entered by the user into a digital format.

[0811] Output: Send the technical document data, converted to digital format, to the server.

[0812] Step 2:

[0813] Data analysis and summary generation

[0814] Input: The server receives technical document data from the terminal.

[0815] Data processing: The server analyzes the text using natural language processing algorithms (e.g., Hugging Face Transformers) and extracts the main content.

[0816] Output: Generates a summarized technical document.

[0817] Step 3:

[0818] Technical term extraction

[0819] Input: The server receives a summarized technical document.

[0820] Data processing: Extract specialized terminology from summary articles using specific vocabulary dictionaries and machine learning models.

[0821] Output: Generates a list of extracted technical terms.

[0822] Step 4:

[0823] Generation of technical terminology explanations

[0824] Input: The server receives a list of extracted technical terms.

[0825] Data processing: The server retrieves explanatory information about each technical term by referring to external databases and APIs.

[0826] Output: Generates a list of explanations for technical terms.

[0827] Step 5:

[0828] Displaying information

[0829] Input: The server integrates summary articles and glossaries of technical terms.

[0830] Data processing: Converting data into a format that is easy for user devices (head-mounted displays and smart glasses) to display.

[0831] Output: Sends a summary article and a glossary of technical terms to the user's terminal.

[0832] Step 6:

[0833] User interaction

[0834] Input: Users view summary articles and glossaries of technical terms displayed via their device.

[0835] Data processing: The device records the user's actions and browsing history.

[0836] Output: Sends user feedback and operation logs to the server.

[0837] This completes a series of processing steps, allowing factory workers to efficiently understand summaries of technical documents and explanations of technical terms.

[0838] 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.

[0839] This invention combines a system that summarizes specialized and complex articles and provides explanations of technical terms with an emotion engine that recognizes the user's emotions, and a specific embodiment thereof will be described in detail.

[0840] The user enters an article using their device. The article entered by the user is sent from the device to the server. This transmitted data also includes the user's emotional data. Emotional data is often obtained from the user's facial expressions and voice.

[0841] The server receives the submitted article data and sentiment data and stores it temporarily. The stored data is then passed to a natural language processing algorithm. The server uses the natural language processing algorithm to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[0842] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list technical terms within the article.

[0843] Next, the server generates explanations for the extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously. Furthermore, the sentiment engine analyzes the user's emotional data and adjusts the display method and content based on the results. For example, if the user is feeling surprised or anxious, it provides a simplified explanation or additional background information accordingly.

[0844] The server compiles a summary article and a glossary of technical terms adjusted based on sentiment data, and sends it to the user's device as a single response. The device displays the received response on the user's screen. The user can simultaneously view the summary article and the sentiment-adjusted glossary of technical terms.

[0845] An explanation with specific examples is provided below.

[0846] The user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. In addition, the user's emotions are captured via the camera and microphone, and this data is analyzed by an emotion engine. The server receives the article and analyzes it using a natural language processing algorithm to generate a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, an explanation such as "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1" is generated. The emotion engine further recognizes if the user is feeling anxious and adds additional explanations and diagrams to the explanation to aid understanding.

[0847] Finally, the server sends the generated summary article and a refined glossary of technical terms to the user's terminal. The user's terminal displays the summary article, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1." Additional illustrations and explanations may also be included depending on the user's preferences.

[0848] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by appropriately providing information according to the user's emotional state.

[0849] The following describes the processing flow.

[0850] Step 1:

[0851] The user uses their device to input specialized and complex articles. The user can either paste the article into the input field or type it directly. Along with the entered article, permission is granted to collect emotional data from the user's facial expressions and voice using the camera and microphone.

[0852] Step 2:

[0853] The device sends articles and sentiment data received from the user to the server. Sentiment data includes facial expressions, tone of voice, and speed.

[0854] Step 3:

[0855] The server temporarily stores the received article data and passes it to a natural language processing algorithm. It also passes sentiment data to an emotion engine to begin analysis.

[0856] Step 4:

[0857] The server uses a natural language processing algorithm to analyze the article, extract the main points, and generate a summary. The generated summary is then stored on the server.

[0858] Step 5:

[0859] The server extracts technical terms from the summary article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article. The listed technical terms are then passed on to the next step.

[0860] Step 6:

[0861] The server generates explanations for extracted technical terms. It retrieves reliable information by referencing external databases and APIs, and creates explanations for each technical term. The explanations are then compiled and stored on the server.

[0862] Step 7:

[0863] The server receives user sentiment data analyzed by the sentiment engine. The sentiment engine analyzes the user's emotional state while reading the article and provides the results to the server.

[0864] Step 8:

[0865] The server adjusts the display and content of summary articles and glossaries based on user sentiment data. For example, if a user is feeling anxious or surprised, the explanation may be rephrased in simpler language or additional illustrations may be provided.

[0866] Step 9:

[0867] The server compiles a summary article and explanations of technical terms, adjusted based on sentiment data, and sends them to the user's terminal as a single response.

[0868] Step 10:

[0869] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of specialized terminology that are adjusted according to the user's sentiment. For example, if additional explanations or illustrations based on sentiment are included, these are also displayed to aid the user's understanding.

[0870] This process allows users to quickly and efficiently understand specialized and complex articles, and receive support tailored to their emotional needs.

[0871] (Example 2)

[0872] 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."

[0873] Conventional technologies were unable to provide summaries or explanations of technical terms to efficiently understand specialized and complex articles. Furthermore, they failed to deliver information in a way that resonated with users' emotions, making it difficult to deepen their understanding. Therefore, there was a need for an effective system that would allow users to quickly understand specialized articles and efficiently acquire related knowledge.

[0874] 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.

[0875] In this invention, the server includes means for the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for an emotion engine to analyze the user's emotional data and adjust the content of the explanations; and means for displaying the summarized article and the adjusted technical term explanations on the user's terminal. This enables users to quickly and effectively understand specialized and complex articles, and provides appropriate information tailored to the user's emotions, resulting in a more effective learning experience.

[0876] A "user" refers to a person who uses a computer system to input articles and receive services.

[0877] A "terminal" is a device used by a user to input information and display it, such as a personal computer, tablet, or smartphone.

[0878] A "server" refers to a computer system that receives and processes data sent by users.

[0879] "Article data" refers to text data containing specialized information entered by users using their devices.

[0880] "Emotional data" refers to data that indicates the user's emotional state, and includes facial expressions and voice information acquired through cameras and microphones.

[0881] "Natural language processing algorithms" refer to programming techniques that analyze input text data and extract summaries and key information.

[0882] A "summary" refers to a shortened version of an article containing the main points, generated by the server using a natural language processing algorithm.

[0883] "Technical jargon" refers to terms specific to a particular field or area of ​​expertise, and is a specialized term that may be difficult for users to understand.

[0884] "Explanation" refers to text information generated by the server to provide a detailed explanation of technical terms.

[0885] An "emotion engine" refers to software technology that analyzes user emotion data and adjusts the displayed content based on the results.

[0886] An "external database" refers to an external information storage location that a server references to provide explanatory information on technical terms.

[0887] An "API" refers to an interface that allows different software systems to exchange information.

[0888] "Display" refers to showing the response from the server on the terminal screen.

[0889] This invention provides a system that enables users to efficiently understand specialized and complex articles. This system not only provides article summaries and explanations of technical terms, but also has the function of recognizing the user's emotions and adjusting the information accordingly. The specific configuration and operation are described in detail below.

[0890] First, the user enters the article using their device and sends that data to the server. The device can be a PC, tablet, or smartphone. As the user enters the article, emotional data (facial expressions and voice) is captured through the device's camera and microphone and sent to the server along with the article data.

[0891] The server temporarily stores the received article data and sentiment data. High-performance databases such as MySQL or PostgreSQL are used for storage. Next, the server analyzes the articles and extracts key points using natural language processing (NLP) algorithms. NLP algorithms used here include GPT-3 and BERT.

[0892] From the summaries generated through analysis, the server extracts technical terms. Machine learning models and technical term dictionaries are used in this process. After the technical terms are extracted, the server references external databases and APIs (such as Wikidata and the Oxford Dictionaries API) to generate explanations for each term.

[0893] Furthermore, the emotion engine analyzes the user's emotional data. The emotion engine analyzes the emotions the user is feeling, such as anxiety and interest, and adjusts the explanatory content based on this. For example, if the user is feeling anxious, it provides a simplified explanation, additional background information, and illustrations.

[0894] Ultimately, the server integrates the summarized article and the glossary, adjusted with sentiment data, into a single response and sends it to the user's device. The device displays the received response on the user's screen, allowing the user to view the summarized article and the adjusted glossary simultaneously.

[0895] The following are some specific examples.

[0896] For example, suppose a user enters an article about the fundamentals of quantum computing and sends it to the server. Simultaneously, the user's emotional data, captured through the camera and microphone, is also sent. The server receives this article, analyzes it using a natural language processing algorithm, and generates a summary like the following:

[0897] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0898] Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and retrieves explanations by referring to external databases and APIs. The following is an example.

[0899] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0900] The emotion engine analyzes the user's anxieties and adds further explanations and diagrams to aid understanding. Finally, the server sends the generated summary article and refined glossary of technical terms to the user's device. The device screen displays information such as the following:

[0901] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[0902] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[0903] This allows users to efficiently understand specialized content by reviewing summarized articles and commentary tailored to their emotions.

[0904] As an example of a prompt statement, the following sentence is used as input to the generative AI model.

[0905] "Please summarize the article I will now enter and provide explanations for technical terms. Furthermore, please adjust the explanations according to the user's feelings. Users are feeling anxious, so they need simple and easy-to-understand explanations."

[0906] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0907] Step 1:

[0908] The user inputs an article using their device. Simultaneously, user sentiment data is acquired via the camera and microphone. The devices used include PCs, tablets, and smartphones. The input article data is acquired in text format, while sentiment data is acquired in image and audio format. For example, suppose a user inputs an article about the fundamentals of quantum computing.

[0909] Input: Text article, image (facial expression), audio (emotion) data

[0910] Output: Article data and sentiment data sent from the terminal.

[0911] Step 2:

[0912] The terminal sends the entered article data and sentiment data to the server. HTTPS is used as the communication protocol, and the data is encrypted. Transmission occurs in real time, ensuring security.

[0913] Input: Encrypted article data and sentiment data

[0914] Output: Data sent to the server

[0915] Step 3:

[0916] The server temporarily stores the received article data and sentiment data in a database. The databases used are MySQL and PostgreSQL. Data storage is performed for efficiency and reliability.

[0917] Input: Received article data and sentiment data

[0918] Output: Data stored in the database

[0919] Step 4:

[0920] The server analyzes article data using natural language processing algorithms. GPT-3 and BERT are used as NLP algorithms. First, the entire article is analyzed to extract key points and generate a summary.

[0921] Input: Saved article data

[0922] Output: Summarized article text

[0923] Step 5:

[0924] The server extracts technical terms from the generated summary text. In this process, machine learning models and technical term dictionaries are used to create a list of these terms.

[0925] Input: Summarized article text

[0926] Output: List of extracted technical terms

[0927] Step 6:

[0928] The server generates explanations for technical terms. It retrieves reliable information by referencing external databases and APIs (e.g., Wikidata, Oxford Dictionaries API).

[0929] Input: List of technical terms

[0930] Output: Explanatory text for each technical term

[0931] Step 7:

[0932] The emotion engine analyzes the user's emotional data. Based on the analysis results, it adjusts the explanations. For example, if the user is feeling anxious, it adds additional explanations or diagrams.

[0933] Input: Saved emotion data

[0934] Output: Adjusted explanatory text

[0935] Step 8:

[0936] The server integrates the summary article and the refined glossary of technical terms. It then generates this as a single response and sends it to the user's terminal.

[0937] Input: Summary article, edited explanatory text

[0938] Output: Integrated response data

[0939] Step 9:

[0940] The response data received by the device is displayed on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[0941] Input: Integrated response data

[0942] Output: Summary article and explanatory text displayed on the terminal screen.

[0943] (Application Example 2)

[0944] 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."

[0945] Traditional systems could summarize specialized articles and explain technical terms, but they struggled to provide information that resonated with the user's emotions. This often led to anxiety and confusion regarding parts the user didn't understand, hindering efficient learning and comprehension. In particular, the lack of explanations that considered the user's emotional state in the explanations of technical terms reduced learning effectiveness.

[0946] 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 the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations related to the extracted technical terms; means for acquiring emotion data using an emotion engine that recognizes the user's emotions and adjusting the summary and explanation generated based on that data; and means for displaying the summarized article and the explanation of technical terms on the user terminal. This makes it possible to provide optimal information in accordance with the user's emotions, which helps in understanding technical articles and improves learning effectiveness.

[0947] A "user" is someone who uses this system to input articles and obtain information.

[0948] An "article" is text data that a user inputs, sends to the server, and is then analyzed.

[0949] A "server" is a computer system that receives articles submitted by users, analyzes and summarizes them, extracts and explains technical terms, and then returns the results to the user.

[0950] A "natural language processing algorithm" refers to an algorithm or program used to analyze text and generate a summary.

[0951] A "summary" is a concise summary of the main points of an original article, generated by a natural language processing algorithm.

[0952] "Technical jargon" refers to terms or words specific to a particular field or area that are generally difficult for the average person to understand.

[0953] "Explanation" refers to texts or diagrams that clearly explain the meaning and usage of technical terms.

[0954] An "emotion engine" is a system or software that analyzes a user's facial expressions and voice to acquire emotional data.

[0955] "Emotional data" refers to data obtained by the emotion engine that indicates the user's emotional state.

[0956] A "user terminal" is a device used by users to input articles, view summarized articles, or view explanations of technical terms.

[0957] This invention relates to a system that uses user sentiment data to aid user understanding in summarizing specialized articles and explaining technical terms. Specific embodiments of this system are described in detail below.

[0958] System Overview

[0959] The system mainly consists of the following components:

[0960] User terminal

[0961] server

[0962] Natural Language Processing Algorithms

[0963] Emotional Engine

[0964] External database or API

[0965] User terminal

[0966] The user terminal is a device used by the user to input articles, receive the results, and display them. Specifically, smart glasses or head-mounted displays can be used.

[0967] server

[0968] The server is the core component that receives articles submitted by users, summarizes them, extracts technical terms, and generates necessary explanations. The server also uses a sentiment engine to process user sentiment data and adjust the information accordingly.

[0969] Natural Language Processing Algorithms

[0970] The server uses natural language processing algorithms for automatic text summarization and extraction of technical terms. Specifically, NLP libraries such as spaCy and Transformers are used.

[0971] Emotional Engine

[0972] An emotion engine is a system that analyzes a user's facial expressions and voice to acquire emotional data. For example, it performs emotion recognition using the Affectiva API or OpenFace.

[0973] External databases and APIs

[0974] To generate definitions of technical terms, the server accesses external databases or APIs to obtain reliable information. For example, it might retrieve information from a dictionary API or a knowledge base.

[0975] Processing flow

[0976] The server will perform the following actions:

[0977] 1. The user enters an article and sends it from their device to the server.

[0978] 2. The server summarizes the received articles using a natural language processing algorithm.

[0979] 3. Extract technical terms from the summarized article.

[0980] 4. Refer to an external database or API to generate explanations for the extracted technical terms.

[0981] 5. The emotion engine analyzes the user's emotional data and adjusts the generated summary and explanation based on the results.

[0982] 6. The server sends the edited summary article and glossary of technical terms to the user's terminal.

[0983] 7. The user terminal displays the results.

[0984] Specific example

[0985] Suppose a user uses smart glasses in a virtual store to search for an article about the fundamentals of quantum computing. The smart glasses' camera captures the user's facial expressions, and an emotion engine retrieves the user's emotional data, recognizing surprise, anxiety, and other emotions. As a result, the glasses' display shows a summary stating, "Quantum computers use computational techniques with qubits to enable parallel processing and high-speed computation," along with additional explanations of "qubits" and "superconductivity" accompanied by simple diagrams.

[0986] Example of a prompt

[0987] text

[0988] Prompt message:

[0989] Please summarize the product description entered by the user in the following format.

[0990] Product description:

[0991] "Quantum computers are computing technologies that use qubits, and their characteristics include parallel processing and high-speed computation."

[0992] Expected Output:

[0993] "Quantum computers use computational techniques based on qubits to enable parallel processing and high-speed computation."

[0994] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by providing information that responds to the user's emotions.

[0995] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0996] Step 1:

[0997] The user enters an article and sends it from their device to the server. Input includes the user typing the article into a text field and pressing the submit button. Output is the article data received by the server.

[0998] Step 2:

[0999] The server summarizes the received articles using a natural language processing algorithm. The input is the article data received in step 1, and for data processing, NLP libraries such as spaCy or Transformers are used to extract the main points of the articles. The output is the summarized text.

[1000] Step 3:

[1001] The server extracts technical terms from the summarized article. The input is the summarized text generated in step 2, and the data processing uses algorithms to identify specific keywords or phrases (e.g., POS tagging or keyword extraction). The output is a list of the extracted technical terms.

[1002] Step 4:

[1003] The server references an external database or API to generate explanations for the extracted technical terms. The input is the list of technical terms obtained in step 3, and the API is called as a data operation to obtain a reliable explanation for each technical term. The output is the explanation information for each technical term.

[1004] Step 5:

[1005] The server uses an emotion engine to acquire and analyze user emotion data. The input is emotion data obtained from the user's facial expressions and voice, and the data analysis uses tools such as the Affectiva API and OpenFace to identify emotions. The output is an identification of the user's emotional state (surprise, anxiety, interest, etc.).

[1006] Step 6:

[1007] The server adjusts the generated summary and explanation based on sentiment data. The input is the summary and explanation information obtained in steps 2 and 4, and the sentiment data obtained in step 5. The data processing involves changing the content and expression of the summary and explanation according to the user's emotions. For example, if the user is feeling anxious, more detailed explanations or illustrations are added. The output is the adjusted summary and explanation information.

[1008] Step 7:

[1009] The server sends the compiled summary article and glossary of technical terms to the user terminal. The input is the compiled summary and glossary information from step 6, and the output is the data sent to the user terminal. The terminal displays the received data on the user's screen.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] [Fourth Embodiment]

[1014] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1015] 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.

[1016] 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).

[1017] 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.

[1018] 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.

[1019] 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).

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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".

[1027] This invention relates to a system for summarizing specialized and complex articles and providing explanations of technical terms, and its specific embodiments will be described in detail.

[1028] The user enters an article using a terminal. The article entered by the user is sent to the server. The server analyzes the received article using a natural language processing algorithm (e.g., an NLP library) and generates a summary. The generated summary is temporarily stored on the server.

[1029] Next, the server extracts technical terms from the summarized article. This extraction process uses specific technical term dictionaries and machine learning models. The extracted technical terms are listed, and their definitions are then generated. The server retrieves reliable definition information by referencing external databases and APIs. The retrieved definition information is organized, and definitions for multiple technical terms are generated at once.

[1030] Finally, the server compiles the summary article and explanations of technical terms and sends them to the user's terminal. The user's terminal displays the received information on the screen, allowing the user to simultaneously view a concise summary and detailed explanations of technical terms.

[1031] An explanation with specific examples is provided below.

[1032] A user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. The server receives the article, analyzes it using a natural language processing algorithm, and generates a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, it might generate the explanation, "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1."

[1033] Finally, the server sends the generated summary article and glossary of technical terms to the user's terminal. The user's terminal simultaneously displays the summary article, which states, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1."

[1034] In this way, the present invention enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[1035] The following describes the processing flow.

[1036] Step 1:

[1037] The user uses a terminal to input a specialized and complex article. The entered article is then sent from the terminal to the server.

[1038] Step 2:

[1039] The server receives the submitted article data and stores it temporarily. The stored data is then passed to a natural language processing algorithm.

[1040] Step 3:

[1041] The server uses natural language processing algorithms to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[1042] Step 4:

[1043] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article.

[1044] Step 5:

[1045] The server generates explanations for extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously.

[1046] Step 6:

[1047] The server compiles a summary article and explanations of technical terms, and sends them to the user's terminal as a single response.

[1048] Step 7:

[1049] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[1050] (Example 1)

[1051] 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".

[1052] In today's information society, quickly understanding specialized and complex articles and deepening related knowledge is a crucial challenge for many users. However, previous systems have lacked sufficient article summaries and explanations of technical terms, making it difficult for users to efficiently acquire information.

[1053] 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.

[1054] In this invention, the server includes means for a user to input an article and send the article to the server, means for the server to summarize the received article using a natural language processing algorithm, means for extracting keywords from the summarized article, means for generating explanations related to the extracted keywords, and means for displaying the summarized article and keyword explanations on the user's terminal. This makes it possible for users to quickly and efficiently understand specialized and complex articles and easily acquire related knowledge.

[1055] A "user" is a person or entity that inputs articles and uses the system.

[1056] An "article" is information or text that is entered by a user and processed by a system.

[1057] A "server" is a device or system that receives articles submitted by users, analyzes them, summarizes them, extracts keywords, generates keyword explanations, and transmits the information to the user's terminal.

[1058] A "natural language processing algorithm" is an algorithm that analyzes human language, understands its meaning, and performs processing based on that understanding.

[1059] "Keywords" are words or terms that are considered particularly important within a summarized article.

[1060] "Explanation" refers to descriptions and detailed information about the extracted keywords.

[1061] A "user terminal" is a device used by users to input articles and ultimately display summary articles and keyword explanations.

[1062] "External information sources" refer to external databases or internet resources that the server consults to obtain explanations about keywords.

[1063] An "interface" is the intermediary part that a server uses to communicate with external information sources.

[1064] This invention is a system for summarizing specialized and complex articles and providing explanations of related technical terms. The embodiments for carrying out this invention are described in detail below.

[1065] The user first enters an article using a terminal. This article is in text format and is usually entered manually into an input field such as a text field. An example of a prompt is "Please summarize an article on the fundamentals of quantum computing and provide explanations of technical terms." This allows the user to provide the system with a specific request.

[1066] The entered article is sent from the terminal to the server. This communication is generally done using an HTTP POST request. The server temporarily stores the received article in memory or a database.

[1067] The server applies natural language processing algorithms to the received articles. Examples of NLP libraries used include "spaCy" and "Transformers." Using these libraries, the server performs grammatical analysis of the articles and extracts key points. Based on this analysis, it summarizes the important content of the articles.

[1068] Next, the server extracts keywords from the summarized article. This keyword extraction uses specific vocabulary dictionaries or machine learning models (e.g., the BERT model). Then, it retrieves explanatory information for the extracted keywords by referencing external sources and interfaces (e.g., the Wikipedia API or the PubMed API). This allows for the automatic generation of reliable explanations.

[1069] The generated summary and keyword explanations are combined into a single packet and sent back to the terminal as an HTTP response.

[1070] Finally, the device displays the received information on its screen. Through the user interface, the summary article and keyword explanations are presented to the user in an easy-to-read format. For example, it might look like this:

[1071] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible." (Summary)

[1072] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1." (Explanation)

[1073] In this way, the present invention provides a system that enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise.

[1074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1075] Step 1:

[1076] The user enters an article. The entered article is placed in the terminal's text field. For example, the user might enter "An article about the fundamentals of quantum computing." The input here is in string format, and when the submit button is pressed, the process proceeds to the next step.

[1077] Step 2:

[1078] The terminal sends the entered article to the server. Specifically, it sends the article data to the server using an HTTP POST request. The input data is in string format and is POSTed to the destination URL. The output is the article data received by the server.

[1079] Step 3:

[1080] The server applies natural language processing algorithms to analyze the articles it receives. First, the server temporarily stores the received data in memory or a database. Next, it uses an NLP library (e.g., spaCy or Transformers) to perform grammatical analysis and topic extraction of the articles. The input for this analysis is the stored article data, and the output is data showing the structure of the text and its main points.

[1081] Step 4:

[1082] The server generates a summary. Based on the key points extracted in step 3, a summary sentence is created using a natural language processing algorithm. This process uses specific rules and deep learning models. The input is the data of key points, and the output is a summary sentence (for example, "Quantum computing is a computational technique that uses qubits...").

[1083] Step 5:

[1084] The server extracts keywords from the summary. It analyzes the summary and extracts specific technical terms and important keywords using dictionaries or machine learning models (e.g., the BERT model). The input is the summary, and the output is a list of extracted keywords.

[1085] Step 6:

[1086] The server generates definitions for keywords. For the extracted keywords, it references external sources and interfaces (e.g., Wikipedia API or PubMed API) to obtain detailed definitions. The input is a list of keywords, and the output is a definition corresponding to each keyword (e.g., "qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1.").

[1087] Step 7:

[1088] The server sends a summary and keyword explanations to the terminal. The generated summary and explanation are combined into a single packet and sent to the terminal as an HTTP response. The input is the summary and explanation, and the output is the packet sent to the terminal.

[1089] Step 8:

[1090] The terminal displays the information it receives to the user. The terminal analyzes the received packets and displays a summary and explanatory text in an appropriate format on the user interface. The input is the packets sent from the server, and the output is the information displayed on the user's screen. Specifically, it is displayed using HTML and CSS in the following format:

[1091] Summary: "Quantum computing is a computational technique that uses qubits..."

[1092] Explanation: "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[1093] (Application Example 1)

[1094] 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".

[1095] Traditional technical documents and manuals for factory workers are often highly specialized and difficult to understand. Especially when introducing new technologies or systems, workers are expected to learn them quickly, but understanding technical terminology can be time-consuming. Furthermore, there is a need for efficient information delivery methods utilizing devices such as smart glasses and head-mounted displays. Therefore, a system is needed that efficiently summarizes specialized technical documents and provides explanations for complex technical terms.

[1096] 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.

[1097] In this invention, the server includes means for a user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for displaying the summarized article and explanations of technical terms on a user terminal; means for using a head-mounted display or smart glasses for factory workers to view technical documents on equipment; means for automatically generating a summary of key points when a user views a technical document; and means for providing explanations of difficult technical terms. This enables workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[1098] A "user" refers to a person who uses the system to input articles and receive information.

[1099] "Article" refers to text information entered or submitted by a user.

[1100] A "server" refers to a central processing unit that processes articles received from users and generates summaries and explanations of technical terms.

[1101] "Natural language processing algorithms" refer to technical methods used to analyze articles and generate summaries.

[1102] A "summary" refers to a concise summary extracted from the original article.

[1103] "Technical terms" refer to terms specific to a particular field.

[1104] "Explanation" refers to additional explanatory information regarding technical terms.

[1105] "User terminal" refers to a device used to display summary articles and explanations of technical terms.

[1106] "Factory worker" refers to a person who uses technical documents and manuals within a factory.

[1107] "Equipment" refers to information display devices such as head-mounted displays and smart glasses.

[1108] A "head-mounted display" refers to a device worn on the head that displays information in the field of vision.

[1109] "Smart glasses" refers to glasses-type information display devices.

[1110] "Technical documents" refer to specialized documents related to operations and systems within a factory.

[1111] "Automatically generating a summary of key points" means that the system automatically creates a summary without requiring any specific user action.

[1112] Modes for carrying out the invention

[1113] This invention is a system for factory workers to efficiently understand technical documents and manuals while working and to quickly acquire new technologies. In this system, the user inputs technical documents, the server processes the information to generate summaries and explanations of technical terms, and displays them on the user's terminal.

[1114] System program

[1115] The system program includes the following steps:

[1116] 1. Data entry

[1117] The user enters technical documentation into a terminal and sends the article to the server.

[1118] 2. Data Analysis and Summary Generation

[1119] The server analyzes the received articles using natural language processing algorithms (NLP libraries) and generates summaries. For this purpose, libraries such as Hugging Face Transformers are used.

[1120] 3. Extraction of technical terms and generation of explanations

[1121] This process extracts technical terms from summarized articles and generates explanations for those terms. External databases and APIs are referenced for generating these explanations. Specific technical term dictionaries and machine learning models are also utilized.

[1122] 4. Display of Information

[1123] The generated summary article and glossary of technical terms are sent to the user's device for display. Display can be done using a head-mounted display or smart glasses (e.g., Google Glass, Vuzix Blade, Microsoft HoloLens, Magic Leap One, etc.).

[1124] Hardware and software usage

[1125] 1. Hardware

[1126] Head-mounted displays or smart glasses: These are used by workers to display technical documentation and obtain explanations on the device.

[1127] 2. Software

[1128] Natural language processing algorithms such as Hugging Face Transformers are used to analyze articles and generate summaries.

[1129] Specialized terminology dictionaries and machine learning models: Used to extract specialized terms and generate explanations for them.

[1130] External database or API: Used to retrieve explanations of technical terms.

[1131] Specific example

[1132] For example, if a factory worker wants to read the "Introductory Manual for a New Robot Control System," the content will be displayed on smart glasses (e.g., Vuzix Blade). In this case, the application will automatically generate a summary of the manual and provide explanations of technical terms such as "robot control system," "feedback loop," and "PID control."

[1133] Example of a prompt

[1134] Technical document: "Implementation Manual for the New Robot Control System"

[1135] Objective: To provide summaries of key points and explanations of technical terms, enabling workers to quickly and easily understand and learn the content.

[1136] In this way, the system of the present invention enables factory workers to efficiently understand technical documents and quickly acquire new technologies and systems.

[1137] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1138] Step 1:

[1139] Data entry

[1140] Input: The user enters technical documents into the terminal.

[1141] Data processing: The terminal converts technical documents entered by the user into a digital format.

[1142] Output: Send the technical document data, converted to digital format, to the server.

[1143] Step 2:

[1144] Data analysis and summary generation

[1145] Input: The server receives technical document data from the terminal.

[1146] Data processing: The server analyzes the text using natural language processing algorithms (e.g., Hugging Face Transformers) and extracts the main content.

[1147] Output: Generates a summarized technical document.

[1148] Step 3:

[1149] Technical term extraction

[1150] Input: The server receives a summarized technical document.

[1151] Data processing: Extract specialized terminology from summary articles using specific vocabulary dictionaries and machine learning models.

[1152] Output: Generates a list of extracted technical terms.

[1153] Step 4:

[1154] Generation of technical terminology explanations

[1155] Input: The server receives a list of extracted technical terms.

[1156] Data processing: The server retrieves explanatory information about each technical term by referring to external databases and APIs.

[1157] Output: Generates a list of explanations for technical terms.

[1158] Step 5:

[1159] Displaying information

[1160] Input: The server integrates summary articles and glossaries of technical terms.

[1161] Data processing: Converting data into a format that is easy for user devices (head-mounted displays and smart glasses) to display.

[1162] Output: Sends a summary article and a glossary of technical terms to the user's terminal.

[1163] Step 6:

[1164] User interaction

[1165] Input: Users view summary articles and glossaries of technical terms displayed via their device.

[1166] Data processing: The device records the user's actions and browsing history.

[1167] Output: Sends user feedback and operation logs to the server.

[1168] This completes a series of processing steps, allowing factory workers to efficiently understand summaries of technical documents and explanations of technical terms.

[1169] 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.

[1170] This invention combines a system that summarizes specialized and complex articles and provides explanations of technical terms with an emotion engine that recognizes the user's emotions, and a specific embodiment thereof will be described in detail.

[1171] The user enters an article using their device. The article entered by the user is sent from the device to the server. This transmitted data also includes the user's emotional data. Emotional data is often obtained from the user's facial expressions and voice.

[1172] The server receives the submitted article data and sentiment data and stores it temporarily. The stored data is then passed to a natural language processing algorithm. The server uses the natural language processing algorithm to analyze the article, extract key points, and generate a summary. The generated summary is stored on the server.

[1173] The server extracts technical terms from the summarized article. It uses a technical term dictionary and machine learning models to identify and list technical terms within the article.

[1174] Next, the server generates explanations for the extracted technical terms. It references external databases and APIs to obtain reliable information and create explanations. Explanations for multiple technical terms are generated simultaneously. Furthermore, the sentiment engine analyzes the user's emotional data and adjusts the display method and content based on the results. For example, if the user is feeling surprised or anxious, it provides a simplified explanation or additional background information accordingly.

[1175] The server compiles a summary article and a glossary of technical terms adjusted based on sentiment data, and sends it to the user's device as a single response. The device displays the received response on the user's screen. The user can simultaneously view the summary article and the sentiment-adjusted glossary of technical terms.

[1176] An explanation with specific examples is provided below.

[1177] The user inputs an article about the fundamentals of quantum computing using their device and sends it to the server. In addition, the user's emotions are captured via the camera and microphone, and this data is analyzed by an emotion engine. The server receives the article and analyzes it using a natural language processing algorithm to generate a summary such as, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. Parallel processing and high-speed computation are possible by utilizing quantum entanglement." Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and obtains explanations for each by referring to external databases and APIs. For example, an explanation such as "qubit: Unlike classical bits, it is a particle that exists in a superposition state of 0 and 1" is generated. The emotion engine further recognizes if the user is feeling anxious and adds additional explanations and diagrams to the explanation to aid understanding.

[1178] Finally, the server sends the generated summary article and a refined glossary of technical terms to the user's terminal. The user's terminal displays the summary article, "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1 and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible," and the explanation, "Qubit: Unlike classical bits, it is a particle that has a superposition state of 0 and 1." Additional illustrations and explanations may also be included depending on the user's preferences.

[1179] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by appropriately providing information according to the user's emotional state.

[1180] The following describes the processing flow.

[1181] Step 1:

[1182] The user uses their device to input specialized and complex articles. The user can either paste the article into the input field or type it directly. Along with the entered article, permission is granted to collect emotional data from the user's facial expressions and voice using the camera and microphone.

[1183] Step 2:

[1184] The device sends articles and sentiment data received from the user to the server. Sentiment data includes facial expressions, tone of voice, and speed.

[1185] Step 3:

[1186] The server temporarily stores the received article data and passes it to a natural language processing algorithm. It also passes sentiment data to an emotion engine to begin analysis.

[1187] Step 4:

[1188] The server uses a natural language processing algorithm to analyze the article, extract the main points, and generate a summary. The generated summary is then stored on the server.

[1189] Step 5:

[1190] The server extracts technical terms from the summary article. It uses a technical term dictionary and machine learning models to identify and list the technical terms within the article. The listed technical terms are then passed on to the next step.

[1191] Step 6:

[1192] The server generates explanations for extracted technical terms. It retrieves reliable information by referencing external databases and APIs, and creates explanations for each technical term. The explanations are then compiled and stored on the server.

[1193] Step 7:

[1194] The server receives user sentiment data analyzed by the sentiment engine. The sentiment engine analyzes the user's emotional state while reading the article and provides the results to the server.

[1195] Step 8:

[1196] The server adjusts the display and content of summary articles and glossaries based on user sentiment data. For example, if a user is feeling anxious or surprised, the explanation may be rephrased in simpler language or additional illustrations may be provided.

[1197] Step 9:

[1198] The server compiles a summary article and explanations of technical terms, adjusted based on sentiment data, and sends them to the user's terminal as a single response.

[1199] Step 10:

[1200] The device displays the response it receives on the user's screen. The user can simultaneously view a summarized article and explanations of specialized terminology that are adjusted according to the user's sentiment. For example, if additional explanations or illustrations based on sentiment are included, these are also displayed to aid the user's understanding.

[1201] This process allows users to quickly and efficiently understand specialized and complex articles, and receive support tailored to their emotional needs.

[1202] (Example 2)

[1203] 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".

[1204] Conventional technologies were unable to provide summaries or explanations of technical terms to efficiently understand specialized and complex articles. Furthermore, they failed to deliver information in a way that resonated with users' emotions, making it difficult to deepen their understanding. Therefore, there was a need for an effective system that would allow users to quickly understand specialized articles and efficiently acquire related knowledge.

[1205] 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.

[1206] In this invention, the server includes means for the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations of the extracted technical terms; means for an emotion engine to analyze the user's emotional data and adjust the content of the explanations; and means for displaying the summarized article and the adjusted technical term explanations on the user's terminal. This enables users to quickly and effectively understand specialized and complex articles, and provides appropriate information tailored to the user's emotions, resulting in a more effective learning experience.

[1207] A "user" refers to a person who uses a computer system to input articles and receive services.

[1208] A "terminal" is a device used by a user to input information and display it, such as a personal computer, tablet, or smartphone.

[1209] A "server" refers to a computer system that receives and processes data sent by users.

[1210] "Article data" refers to text data containing specialized information entered by users using their devices.

[1211] "Emotional data" refers to data that indicates the user's emotional state, and includes facial expressions and voice information acquired through cameras and microphones.

[1212] "Natural language processing algorithms" refer to programming techniques that analyze input text data and extract summaries and key information.

[1213] A "summary" refers to a shortened version of an article containing the main points, generated by the server using a natural language processing algorithm.

[1214] "Technical jargon" refers to terms specific to a particular field or area of ​​expertise, and is a specialized term that may be difficult for users to understand.

[1215] "Explanation" refers to text information generated by the server to provide a detailed explanation of technical terms.

[1216] An "emotion engine" refers to software technology that analyzes user emotion data and adjusts the displayed content based on the results.

[1217] An "external database" refers to an external information storage location that a server references to provide explanatory information on technical terms.

[1218] An "API" refers to an interface that allows different software systems to exchange information.

[1219] "Display" refers to showing the response from the server on the terminal screen.

[1220] This invention provides a system that enables users to efficiently understand specialized and complex articles. This system not only provides article summaries and explanations of technical terms, but also has the function of recognizing the user's emotions and adjusting the information accordingly. The specific configuration and operation are described in detail below.

[1221] First, the user enters the article using their device and sends that data to the server. The device can be a PC, tablet, or smartphone. As the user enters the article, emotional data (facial expressions and voice) is captured through the device's camera and microphone and sent to the server along with the article data.

[1222] The server temporarily stores the received article data and sentiment data. High-performance databases such as MySQL or PostgreSQL are used for storage. Next, the server analyzes the articles and extracts key points using natural language processing (NLP) algorithms. NLP algorithms used here include GPT-3 and BERT.

[1223] From the summaries generated through analysis, the server extracts technical terms. Machine learning models and technical term dictionaries are used in this process. After the technical terms are extracted, the server references external databases and APIs (such as Wikidata and the Oxford Dictionaries API) to generate explanations for each term.

[1224] Furthermore, the emotion engine analyzes the user's emotional data. The emotion engine analyzes the emotions the user is feeling, such as anxiety and interest, and adjusts the explanatory content based on this. For example, if the user is feeling anxious, it provides a simplified explanation, additional background information, and illustrations.

[1225] Ultimately, the server integrates the summarized article and the glossary, adjusted with sentiment data, into a single response and sends it to the user's device. The device displays the received response on the user's screen, allowing the user to view the summarized article and the adjusted glossary simultaneously.

[1226] The following are some specific examples.

[1227] For example, suppose a user enters an article about the fundamentals of quantum computing and sends it to the server. Simultaneously, the user's emotional data, captured through the camera and microphone, is also sent. The server receives this article, analyzes it using a natural language processing algorithm, and generates a summary like the following:

[1228] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[1229] Next, the server extracts technical terms such as "qubit," "superconductivity," and "quantum entanglement" from this summary and retrieves explanations by referring to external databases and APIs. The following is an example.

[1230] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[1231] The emotion engine analyzes the user's anxieties and adds further explanations and diagrams to aid understanding. Finally, the server sends the generated summary article and refined glossary of technical terms to the user's device. The device screen displays information such as the following:

[1232] "Quantum computing is a computational technique that uses qubits. Qubits can exist in a superposition state of 0 and 1, and are often realized using superconducting materials. By utilizing quantum entanglement, parallel processing and high-speed computation are possible."

[1233] "Quantum bit: Unlike classical bits, a particle that has a superposition state of 0 and 1."

[1234] This allows users to efficiently understand specialized content by reviewing summarized articles and commentary tailored to their emotions.

[1235] As an example of a prompt statement, the following sentence is used as input to the generative AI model.

[1236] "Please summarize the article I will now enter and provide explanations for technical terms. Furthermore, please adjust the explanations according to the user's feelings. Users are feeling anxious, so they need simple and easy-to-understand explanations."

[1237] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1238] Step 1:

[1239] The user inputs an article using their device. Simultaneously, user sentiment data is acquired via the camera and microphone. The devices used include PCs, tablets, and smartphones. The input article data is acquired in text format, while sentiment data is acquired in image and audio format. For example, suppose a user inputs an article about the fundamentals of quantum computing.

[1240] Input: Text article, image (facial expression), audio (emotion) data

[1241] Output: Article data and sentiment data sent from the terminal.

[1242] Step 2:

[1243] The terminal sends the entered article data and sentiment data to the server. HTTPS is used as the communication protocol, and the data is encrypted. Transmission occurs in real time, ensuring security.

[1244] Input: Encrypted article data and sentiment data

[1245] Output: Data sent to the server

[1246] Step 3:

[1247] The server temporarily stores the received article data and sentiment data in a database. The databases used are MySQL and PostgreSQL. Data storage is performed for efficiency and reliability.

[1248] Input: Received article data and sentiment data

[1249] Output: Data stored in the database

[1250] Step 4:

[1251] The server analyzes article data using natural language processing algorithms. GPT-3 and BERT are used as NLP algorithms. First, the entire article is analyzed to extract key points and generate a summary.

[1252] Input: Saved article data

[1253] Output: Summarized article text

[1254] Step 5:

[1255] The server extracts technical terms from the generated summary text. In this process, machine learning models and technical term dictionaries are used to create a list of these terms.

[1256] Input: Summarized article text

[1257] Output: List of extracted technical terms

[1258] Step 6:

[1259] The server generates explanations for technical terms. It retrieves reliable information by referencing external databases and APIs (e.g., Wikidata, Oxford Dictionaries API).

[1260] Input: List of technical terms

[1261] Output: Explanatory text for each technical term

[1262] Step 7:

[1263] The emotion engine analyzes the user's emotional data. Based on the analysis results, it adjusts the explanations. For example, if the user is feeling anxious, it adds additional explanations or diagrams.

[1264] Input: Saved emotion data

[1265] Output: Adjusted explanatory text

[1266] Step 8:

[1267] The server integrates the summary article and the refined glossary of technical terms. It then generates this as a single response and sends it to the user's terminal.

[1268] Input: Summary article, edited explanatory text

[1269] Output: Integrated response data

[1270] Step 9:

[1271] The response data received by the device is displayed on the user's screen. The user can simultaneously view a summarized article and explanations of technical terms.

[1272] Input: Integrated response data

[1273] Output: Summary article and explanatory text displayed on the terminal screen.

[1274] (Application Example 2)

[1275] 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".

[1276] Traditional systems could summarize specialized articles and explain technical terms, but they struggled to provide information that resonated with the user's emotions. This often led to anxiety and confusion regarding parts the user didn't understand, hindering efficient learning and comprehension. In particular, the lack of explanations that considered the user's emotional state in the explanations of technical terms reduced learning effectiveness.

[1277] 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 the user to input an article and send the article to the server; means for the server to summarize the received article using a natural language processing algorithm; means for extracting technical terms from the summarized article; means for generating explanations related to the extracted technical terms; means for acquiring emotion data using an emotion engine that recognizes the user's emotions and adjusting the summary and explanation generated based on that data; and means for displaying the summarized article and the explanation of technical terms on the user terminal. This makes it possible to provide optimal information in accordance with the user's emotions, which helps in understanding technical articles and improves learning effectiveness.

[1278] A "user" is someone who uses this system to input articles and obtain information.

[1279] An "article" is text data that a user inputs, sends to the server, and is then analyzed.

[1280] A "server" is a computer system that receives articles submitted by users, analyzes and summarizes them, extracts and explains technical terms, and then returns the results to the user.

[1281] A "natural language processing algorithm" refers to an algorithm or program used to analyze text and generate a summary.

[1282] A "summary" is a concise summary of the main points of an original article, generated by a natural language processing algorithm.

[1283] "Technical jargon" refers to terms or words specific to a particular field or area that are generally difficult for the average person to understand.

[1284] "Explanation" refers to texts or diagrams that clearly explain the meaning and usage of technical terms.

[1285] An "emotion engine" is a system or software that analyzes a user's facial expressions and voice to acquire emotional data.

[1286] "Emotional data" refers to data obtained by the emotion engine that indicates the user's emotional state.

[1287] A "user terminal" is a device used by users to input articles, view summarized articles, or view explanations of technical terms.

[1288] This invention relates to a system that uses user sentiment data to aid user understanding in summarizing specialized articles and explaining technical terms. Specific embodiments of this system are described in detail below.

[1289] System Overview

[1290] The system mainly consists of the following components:

[1291] User terminal

[1292] server

[1293] Natural Language Processing Algorithms

[1294] Emotional Engine

[1295] External database or API

[1296] User terminal

[1297] The user terminal is a device used by the user to input articles, receive the results, and display them. Specifically, smart glasses or head-mounted displays can be used.

[1298] server

[1299] The server is the core component that receives articles submitted by users, summarizes them, extracts technical terms, and generates necessary explanations. The server also uses a sentiment engine to process user sentiment data and adjust the information accordingly.

[1300] Natural Language Processing Algorithms

[1301] The server uses natural language processing algorithms for automatic text summarization and extraction of technical terms. Specifically, NLP libraries such as spaCy and Transformers are used.

[1302] Emotional Engine

[1303] An emotion engine is a system that analyzes a user's facial expressions and voice to acquire emotional data. For example, it performs emotion recognition using the Affectiva API or OpenFace.

[1304] External databases and APIs

[1305] To generate definitions of technical terms, the server accesses external databases or APIs to obtain reliable information. For example, it might retrieve information from a dictionary API or a knowledge base.

[1306] Processing flow

[1307] The server will perform the following actions:

[1308] 1. The user enters an article and sends it from their device to the server.

[1309] 2. The server summarizes the received articles using a natural language processing algorithm.

[1310] 3. Extract technical terms from the summarized article.

[1311] 4. Refer to an external database or API to generate explanations for the extracted technical terms.

[1312] 5. The emotion engine analyzes the user's emotional data and adjusts the generated summary and explanation based on the results.

[1313] 6. The server sends the edited summary article and glossary of technical terms to the user's terminal.

[1314] 7. The user terminal displays the results.

[1315] Specific example

[1316] Suppose a user uses smart glasses in a virtual store to search for an article about the fundamentals of quantum computing. The smart glasses' camera captures the user's facial expressions, and an emotion engine retrieves the user's emotional data, recognizing surprise, anxiety, and other emotions. As a result, the glasses' display shows a summary stating, "Quantum computers use computational techniques with qubits to enable parallel processing and high-speed computation," along with additional explanations of "qubits" and "superconductivity" accompanied by simple diagrams.

[1317] Example of a prompt

[1318] text

[1319] Prompt message:

[1320] Please summarize the product description entered by the user in the following format.

[1321] Product description:

[1322] "Quantum computers are computing technologies that use qubits, and their characteristics include parallel processing and high-speed computation."

[1323] Expected Output:

[1324] "Quantum computers use computational techniques based on qubits to enable parallel processing and high-speed computation."

[1325] In this way, the present invention not only enables users to quickly and efficiently understand specialized and complex articles and acquire relevant expertise, but also realizes a more effective learning experience by providing information that responds to the user's emotions.

[1326] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1327] Step 1:

[1328] The user enters an article and sends it from their device to the server. Input includes the user typing the article into a text field and pressing the submit button. Output is the article data received by the server.

[1329] Step 2:

[1330] The server summarizes the received articles using a natural language processing algorithm. The input is the article data received in step 1, and for data processing, NLP libraries such as spaCy or Transformers are used to extract the main points of the articles. The output is the summarized text.

[1331] Step 3:

[1332] The server extracts technical terms from the summarized article. The input is the summarized text generated in step 2, and the data processing uses algorithms to identify specific keywords or phrases (e.g., POS tagging or keyword extraction). The output is a list of the extracted technical terms.

[1333] Step 4:

[1334] The server references an external database or API to generate explanations for the extracted technical terms. The input is the list of technical terms obtained in step 3, and the API is called as a data operation to obtain a reliable explanation for each technical term. The output is the explanation information for each technical term.

[1335] Step 5:

[1336] The server uses an emotion engine to acquire and analyze user emotion data. The input is emotion data obtained from the user's facial expressions and voice, and the data analysis uses tools such as the Affectiva API and OpenFace to identify emotions. The output is an identification of the user's emotional state (surprise, anxiety, interest, etc.).

[1337] Step 6:

[1338] The server adjusts the generated summary and explanation based on sentiment data. The input is the summary and explanation information obtained in steps 2 and 4, and the sentiment data obtained in step 5. The data processing involves changing the content and expression of the summary and explanation according to the user's emotions. For example, if the user is feeling anxious, more detailed explanations or illustrations are added. The output is the adjusted summary and explanation information.

[1339] Step 7:

[1340] The server sends the compiled summary article and glossary of technical terms to the user terminal. The input is the compiled summary and glossary information from step 6, and the output is the data sent to the user terminal. The terminal displays the received data on the user's screen.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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."

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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.

[1362] The following is further disclosed regarding the embodiments described above.

[1363] (Claim 1)

[1364] A means for a user to input an article and send that article to the server,

[1365] A means for summarizing articles received by the server using a natural language processing algorithm,

[1366] A method for extracting technical terms from summarized articles,

[1367] A means for generating explanations for extracted technical terms,

[1368] A means for displaying summary articles and explanations of technical terms on the user's terminal,

[1369] A system that includes this.

[1370] (Claim 2)

[1371] The system according to claim 1, comprising means of using a specific NLP library as a natural language processing algorithm.

[1372] (Claim 3)

[1373] The system according to claim 1, which includes means for referencing an external database or API in generating a glossary of technical terms.

[1374] "Example 1"

[1375] (Claim 1)

[1376] A means for a user to input an article and send that article to the server,

[1377] A means for summarizing articles received by the server using a natural language processing algorithm,

[1378] Methods for extracting keywords from summarized articles,

[1379] A means for generating explanations related to extracted keywords,

[1380] A means for displaying summary articles and keyword explanations on the user's terminal,

[1381] A system that includes this.

[1382] (Claim 2)

[1383] The system according to claim 1, comprising means of using a specific library as a natural language processing algorithm.

[1384] (Claim 3)

[1385] The system according to claim 1, which includes means for referencing an external source or interface in generating keyword explanations.

[1386] "Application Example 1"

[1387] (Claim 1)

[1388] A means for a user to input an article and send that article to the server,

[1389] A means for summarizing articles received by the server using a natural language processing algorithm,

[1390] A method for extracting technical terms from summarized articles,

[1391] A means for generating explanations for extracted technical terms,

[1392] A means for displaying summary articles and explanations of technical terms on the user's terminal,

[1393] Methods for factory workers to use head-mounted displays or smart glasses to display technical documents on equipment,

[1394] A means of automatically generating a summary of key points when a user views a technical document,

[1395] A means of providing explanations for difficult technical terms,

[1396] A system that includes this.

[1397] (Claim 2)

[1398] The system according to claim 1, comprising means of using a specific NLP library as a natural language processing algorithm.

[1399] (Claim 3)

[1400] The system according to claim 1, which includes means for referencing an external database or API in generating a glossary of technical terms.

[1401] "Example 2 of combining an emotion engine"

[1402] (Claim 1)

[1403] A means for a user to input an article and send that article to the server,

[1404] A means for summarizing articles received by the server using a natural language processing algorithm,

[1405] A method for extracting technical terms from summarized articles,

[1406] A means for generating explanations for extracted technical terms,

[1407] The emotion engine analyzes the user's emotional data and adjusts the explanatory content accordingly.

[1408] A means for displaying a summary article and a revised glossary of technical terms on the user's terminal,

[1409] A system that includes this.

[1410] (Claim 2)

[1411] The system according to claim 1, comprising means of using a specific library as a natural language processing algorithm.

[1412] (Claim 3)

[1413] The system according to claim 1, which includes means for referencing an external database or interface in generating a glossary of technical terms.

[1414] "Application example 2 when combining with an emotional engine"

[1415] (Claim 1)

[1416] A means for a user to input an article and send that article to the server,

[1417] A means for summarizing articles received by the server using a natural language processing algorithm,

[1418] A method for extracting technical terms from summarized articles,

[1419] A means for generating explanations for extracted technical terms,

[1420] A means for acquiring emotional data using an emotion engine that recognizes user emotions, and for adjusting summaries and explanations generated based on that data,

[1421] A means for displaying summary articles and explanations of technical terms on the user's terminal,

[1422] A system that includes this.

[1423] (Claim 2)

[1424] The system according to claim 1, comprising means of using a specific NLP library as a natural language processing algorithm.

[1425] (Claim 3)

[1426] The system according to claim 1, which includes means for referencing an external database or API in generating a glossary of technical terms. [Explanation of Symbols]

[1427] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input an article and send that article to the server, A means for summarizing articles received by the server using a natural language processing algorithm, A method for extracting technical terms from summarized articles, A means for generating explanations for extracted technical terms, A means for displaying summary articles and explanations of technical terms on the user's terminal, A system that includes this.

2. The system according to claim 1, comprising means of using a specific NLP library as a natural language processing algorithm.

3. The system according to claim 1, which includes means for referencing an external database or API in generating a glossary of technical terms.

Images