system

Abstract

We provide the system. [Solution] A means for extracting important words and numerical information from information resources acquired by a generation processing device, A means for generating additional information related to extracted important words and numerical information using a generation processing device, Means for adjusting additional information based on the user's history and interests, Means for displaying adjusted additional information and information resources on a terminal device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In news information providing services, there is a problem that it is difficult to diversify and differentiate information due to copyright-based restrictions. As a result, information that is uniform and not individualized is often provided to users, hindering the deepening of users' individual interests and understanding. The present invention aims to solve such problems and provide a more individualized news experience to users.

Means for Solving the Problems

[0005] The present invention provides means for extracting important words and numerical information from information resources acquired using a generation processing device. It also provides means for generating related additional information based on the extracted important words and numerical information. Furthermore, it provides means for adjusting the additional information based on the user's history information and interest information, and by displaying the adjusted additional information and information resources on a terminal device, it makes it possible to provide a personalized news experience for each user.

[0006] A "generation processing device" is a device that has the function of extracting important elements from information resources and generating additional information.

[0007] "Information resources" refer to the raw materials of information provided to users, such as news articles and documents.

[0008] "Key words" refer to keywords or phrases that deserve particular attention within an information resource and are targeted for extraction during the information generation process.

[0009] "Numerical information" refers to statistical or quantitative data within information resources, intended to be visualized as graphs or charts.

[0010] "Additional information" refers to supplementary data and explanations generated based on the extracted key words and numerical information.

[0011] "History information" refers to records of a user's past browsing and usage, and is used to form a user profile.

[0012] "Interest information" refers to data related to a user's areas of interest and preferences, and is used to personalize their news experience.

[0013] A "terminal device" is a device used to display information resources and additional information to the user, and includes PCs, smartphones, and other devices. [Brief explanation of the drawing]

[0014] [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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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

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

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

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

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

[0019] In the following embodiments, the 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.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system that, when providing news information to a user, uses a generation and processing device to extract important words and numerical information from information resources, and generates and displays related additional information. This system consists of three main components: a server, a terminal, and a user.

[0036] The server first collects the latest articles from multiple news sources. This collected information resource is then processed by a generation and processing unit, from which key words and numerical information are extracted. The generated key words indicate characteristic elements and topics of the article, while the numerical information represents related data.

[0037] Next, the server generates supplementary information based on the extracted key words and numerical data. This additional information includes definitions of terms, background explanations, and statistical graphs and charts based on the data. This allows users to obtain supplementary information to gain a deeper understanding of the news article.

[0038] Furthermore, the server manages user history and interest information, personalizing additional information. Based on a user's past browsing history and areas of interest, content is personalized, optimizing the news experience. This personalized information provides each user with more relevant information and helps them understand news articles.

[0039] The terminal displays information resources and additional information received from the server on a user-friendly interface that is easily accessible to the user. This display is neatly arranged and in a user-friendly format. Users can select items of interest from the presented information and access detailed information to gain a deeper understanding of the news.

[0040] As a concrete example, suppose a user is reading a news article about "changes to the health insurance system." In this case, the server extracts key words such as "insurance premiums," "general practice," and "preventive medicine," and based on these, generates new policies and graphs based on past statistical data. Furthermore, for users with a high interest in health, additional information such as further research findings and opinions related to this field is provided. As a result, users can gain a deeper understanding than that of a mere news article.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves articles from news sources. This includes using web-based RSS feeds and APIs to automatically collect the latest information. The collected articles are temporarily stored in a database for subsequent processing.

[0044] Step 2:

[0045] The server uses a generation and processing unit to extract important words and numerical information from the retrieved articles. Natural language processing technology is used to identify keywords within the articles and identify related numerical data. This clarifies the article's theme and key points.

[0046] Step 3:

[0047] The server generates additional relevant information based on extracted key words and numerical data. A generative AI model is used to present definitions of terms, background information, and relevant statistical data. Graphs and charts are also created to facilitate visual understanding.

[0048] Step 4:

[0049] The server personalizes additional information based on the user's history and interests. This involves analyzing the user's past browsing history and preferences to provide the most relevant information to each individual user. Personalization is crucial for improving the relevance of the information.

[0050] Step 5:

[0051] The server sends packets containing the final news article and personalized additional information to the terminal. The transmitted data is compressed and encrypted to ensure the security of the communication.

[0052] Step 6:

[0053] The terminal decompresses the data received from the server and displays it on the user interface. The visual layout and navigation are optimized to allow users to easily access information of interest.

[0054] Step 7:

[0055] Users view the provided news articles and additional information, and search for more detailed information as needed. User selections and actions are logged and used to personalize future experiences.

[0056] (Example 1)

[0057] 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."

[0058] Conventional news distribution systems struggle to provide information tailored to individual user interests, resulting in problems such as insufficient or excessive information simply by distributing news. Furthermore, they lack background information and relationship diagrams necessary for a deeper understanding of the news, hindering user comprehension.

[0059] 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.

[0060] In this invention, the server includes means for acquiring data resources from news sources and extracting important words and numerical data using natural language processing techniques; means for generating relevant information based on the extracted words and numerical data; and means for personalizing the relevant information according to the user's history and interest data. This enables users to receive news that includes personalized background information and to understand it more deeply.

[0061] A "news source" is an external data source that provides articles and reports from multiple news providers and media organizations.

[0062] "Data resources" refer to the collection of information that makes up news articles and reports that have been gathered.

[0063] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand and process human language.

[0064] "Key terms" are the main words or phrases that characterize the content of a news article.

[0065] "Numerical data" refers to statistical information and numerical data included within an article.

[0066] "Related information" refers to background explanations and supplementary materials generated based on extracted key keywords and numerical data.

[0067] "User history and interest data" refers to database information related to past browsing history and user interests.

[0068] "Personalization methods" refer to processes or technologies that customize information based on each user's interests and history.

[0069] A "data display device" is a device that provides an interface for visually presenting generated news information and related information to the user.

[0070] The news information provision system in this invention consists of three entities: a server, a terminal, and a user. In order to effectively implement the invention, the following specific devices and technologies are used.

[0071] First, the server collects data resources from multiple news sources. This collection process is carried out via Web APIs and RSS feeds, efficiently retrieving the latest news articles. The data resources are then stored in a database system, ready for further processing.

[0072] Next, the server uses generative AI models and natural language processing techniques to extract important words and numerical data. For this purpose, it implements algorithms such as tokenization, part-of-speech tagging, and named entity recognition. Specifically, it uses spaCy, an open-source natural language processing library, and AI model libraries.

[0073] Subsequently, the server generates related information based on the extracted data. This process accesses external knowledge bases and databases to supplement the information. Matplotlib and D3.js are used as data visualization tools with generative AI models to generate visual graphs and charts.

[0074] Furthermore, the server analyzes user history and interest data to personalize relevant information. Machine learning algorithms process the data and deliver a personalized news feed for each user.

[0075] The terminal displays relevant information obtained from the server on the user interface, making it easily accessible to the user. The display is arranged in an intuitive and easy-to-understand manner, and interactive elements are provided to allow users to explore the details of the information. Frontend frameworks such as Bootstrap and React are used in the terminal.

[0076] Users can browse news through the provided interface and obtain detailed information based on their interests.

[0077] As a concrete example, if a user views news about "changes to the health insurance system," the server extracts key terms such as "insurance premiums," "general practice," and "preventive medicine," and displays relevant information, including policy changes and statistical data, in graph form. An example of a prompt that might be used at this time is "Describe the key changes to the health insurance system and their impact in detail." Through this process, users can obtain information beyond mere news and gain a deeper understanding.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects data resources from news sources. Input is requests via news APIs and RSS feeds, and output is the latest news article data. Specifically, the server periodically accesses news sources, checks for new information, and stores any uncollected data in the database.

[0081] Step 2:

[0082] The server analyzes data resources using natural language processing techniques to extract important words and numerical data. The input is news article data stored in a database, and the output is a list of important words and numbers that characterize the articles. The server organizes the data by tokenizing the articles through a generative AI model and performing part-of-speech tagging and named entity recognition.

[0083] Step 3:

[0084] The server generates relevant information based on extracted keywords and numerical data. The input consists of key keywords and numerical data, while the output includes background explanations and statistical graphs as relevant information. Specifically, the server retrieves supplementary information from an external knowledge base and uses Tableau or D3.js to perform visualizations based on the data.

[0085] Step 4:

[0086] The server personalizes relevant information based on the user's history and interest data. Input is the user's past browsing history and current interests, and output is personalized news content. The server uses machine learning algorithms to analyze the user profile and select the most relevant information.

[0087] Step 5:

[0088] The terminal displays relevant information received from the server on the user interface. Input is personalized news content, and output is a visual representation on the user interface. Specifically, the terminal uses a front-end development framework to organize information and provide interactive elements.

[0089] (Application Example 1)

[0090] 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."

[0091] In today's information society, where a vast amount of news information circulates, users need related information and background knowledge to deeply understand each news article. However, if this information is not provided appropriately, users face information overload, making it difficult to gain accurate understanding. Therefore, there is a need for visually and personalized information presentation that allows users to quickly grasp the importance and relevance of an article.

[0092] 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.

[0093] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, and means for presenting the content to the user in a visual form on a visual display device. This enables the user to visually understand important information related to news articles and to quickly and deeply grasp the context of the articles.

[0094] A "generation processing device" is a computing device that extracts important elements from information resources and generates related additional information.

[0095] "Key words" are words that indicate the main topic or characteristics of information resources such as news articles.

[0096] "Numerical information" refers to quantitative data and numerical values ​​contained in information resources.

[0097] "Additional information" refers to related information and explanations generated based on important words and numerical data, which help users understand the information more deeply.

[0098] A "visual display device" is a device that allows users to visually confirm information, such as a smartphone or a head-mounted display.

[0099] "User history information" refers to data about what kind of information a user has viewed or interacted with in the past.

[0100] "Interest information" refers to data that shows what topics or themes a particular user is interested in.

[0101] Personalization refers to the process of adapting information and services provided based on each user's history and interests.

[0102] This invention realizes a system that provides users with relevant and important information in an easy-to-understand visual format when they view news articles. The system mainly operates with three components: a server, a terminal, and a user.

[0103] The server first acquires news data from multiple sources. This data is analyzed using a generative processing unit and natural language processing models (such as BERT or GPT-3®) to extract important words and numerical information. The server then generates additional explanatory information and data for visual display based on this extracted information. This process is performed by a generative AI model. Furthermore, the generated information is personalized based on each user's history and interests.

[0104] The device displays information received from the server through a user interface. The device can be a smartphone or a head-mounted display (HMD), through which the user views the information. The information presented on the visual display device includes in-depth explanations related to important words and data that the user finds interesting. This allows the user to quickly understand the information in the article and obtain relevant context.

[0105] As a concrete example, when a user reads an article about new environmental policies, the server extracts key terms such as "greenhouse gases" and "renewable energy," and generates graphs and explanatory content based on these terms. This visual information is provided to the user via an HMD (Head-Mounted Display) to facilitate their understanding.

[0106] A concrete example of a prompt for a generative AI model is: "Please analyze the news article on recent environmental policies and list key concepts and relevant data for visualization." Using this prompt, the AI ​​identifies important elements of the article and generates visual information.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server collects data from news sources. In this step, the server uses web scraping techniques to collect the latest articles from multiple news sites. Inputs include web URLs and API keys, and output is raw news article data.

[0110] Step 2:

[0111] The server uses a generation and processing unit to begin analyzing raw news article data. During this process, a natural language processing model (e.g., GPT-3) is used to extract important words and numerical information. The input is the collected news article data, and the output is the analyzed important words and numerical information. The model understands the content of the text and selects the most relevant words.

[0112] Step 3:

[0113] The server uses a generative AI model based on the extracted information to generate highly relevant additional information. In this step, it generates relevant explanatory text and visual data (graphs and statistics) based on the extracted words and numbers. The input requires the extracted words and numerical information, and the output is the generated additional information. This generation process is instructed to the AI ​​via prompts. The prompt used is "Please analyze the key concepts and produce descriptive content and visualizations for better understanding."

[0114] Step 4:

[0115] The server references the user's history and interests to personalize additional information. Database access is performed to retrieve past browsing history and topics of interest, and the information is then tailored based on this data. The input is the user's profile data, and the output is personalized additional information.

[0116] Step 5:

[0117] The device receives personalized information sent from the server and displays it to the user. At this stage, a visual display device is used to display the generated content in an easily understandable format. The input is display data from the server, and the output is visual information that the user accesses visually. The user then looks at this and takes action to gain a deeper understanding of the article.

[0118] 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.

[0119] This invention relates to a news information system that incorporates an emotion engine to improve the quality of information delivery. This system is centered around a server, terminals, and users, and has the function of recognizing the user's emotions and optimizing the content of the news displayed.

[0120] The server first collects news articles via the network. These collected information resources are then processed through a generation and processing unit. Key words and numerical information are extracted from the articles, and related additional information is created by a generation AI model.

[0121] Next, an emotion engine is incorporated to analyze the user's emotions. It analyzes interaction data such as text input and clicks performed by the user through the device, recognizing a variety of emotions such as joy, surprise, and sadness in real time. The recognized emotion data is managed on a server.

[0122] Based on the analysis results, the server customizes additional information in news articles to match the user's current emotional state. For example, it provides more hopeful news and positive information to depressed users, and adjusts the display to show more detailed data analysis and visual graphs to encourage calmness in excited users.

[0123] The device displays news articles customized to the user based on their emotions. The user interface is personalized, and different layouts and color schemes may be selected depending on the user's emotional state. Users can use the provided information in conjunction with their own emotions to gain a deeper understanding of the news.

[0124] For example, when a user is browsing news about an economic crisis, if the emotion engine detects the user's anxiety, the server will prioritize displaying information about economic measures and signs of recovery. Furthermore, in such situations, suggestions for listening music or distracting entertainment content may also be presented to help maintain composure. This system allows users not only to obtain news but also to enjoy information while gaining emotional reassurance.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects the latest articles from multiple news sources. This is done using APIs and RSS feeds to automatically retrieve the latest articles. The collected articles are temporarily stored in a database.

[0128] Step 2:

[0129] The server uses a generation and processing unit to extract important words and numerical information from stored articles. Natural language processing techniques are used here to identify keywords and themes in the articles.

[0130] Step 3:

[0131] Based on the key words and numerical information extracted by the server, an AI model is used to create additional information. This additional information includes definitions of terms, background information, and statistical graphs.

[0132] Step 4:

[0133] The server uses an emotion engine to analyze interaction data received from the user's device. It analyzes the text and behavioral data entered by the user to identify their current emotional state.

[0134] Step 5:

[0135] The server personalizes additional information in news articles based on the emotions it recognizes. For example, it provides reassuring content to anxious users and detailed data to help agitated users calm down.

[0136] Step 6:

[0137] The server packets the final news article and any customized additional information and sends it to the terminal. At this point, the data is encrypted to ensure the security of the communication.

[0138] Step 7:

[0139] The device decompresses data received from the server and displays it on the user interface. The layout and color scheme are designed to be emotionally resonant, allowing users to intuitively understand the information.

[0140] Step 8:

[0141] Users view the presented news articles and related information, and research further details as needed. By providing information that responds to the user's emotions, it becomes possible to process news with a deeper understanding and while maintaining emotional stability.

[0142] (Example 2)

[0143] 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".

[0144] In modern society, information overload makes it difficult for users to efficiently acquire only the information they need. Furthermore, users often experience psychological burden when receiving information, and there is a demand for information tailored to their emotional state. However, conventional systems are insufficient in providing information that considers user emotions, resulting in limitations in improving user satisfaction. Therefore, this invention aims to improve the quality of information provision by enabling the optimal provision of necessary and positive information while considering user emotions.

[0145] 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.

[0146] In this invention, the server includes means for extracting important terms and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important terms and numerical information, and means for generating emotional data using an emotional analysis engine that analyzes the user's emotional state. As a result, information is tailored to the user's emotional state, information provision is more personalized, and information reception becomes beneficial and stress-free for the user.

[0147] A "generation and processing device" is a device that acquires information resources, extracts important terms and numerical information from them, and generates additional information as needed.

[0148] "Information resources" refer to news articles and other related data obtained from the internet, and are basic data used in information provision systems.

[0149] "Key terms" are words extracted from information resources that are considered most important for understanding the subject matter of an article or data.

[0150] "Numerical information" refers to statistical or quantitative numerical data contained within information resources, and is an essential element for understanding and analyzing information.

[0151] An "emotion analysis engine" is software or a system that analyzes a user's text input and behavioral data to determine the user's emotional state in real time.

[0152] "Emotional data" refers to data representing the user's emotional state, generated by an emotion analysis engine, and is used for selecting and displaying information.

[0153] A "user interface" refers to the visual elements that allow a user to interact with an information system, including display elements such as screen layout and color scheme displayed on a terminal.

[0154] A "data storage device" is a hardware or software component used to store a user's history information, interest information, and emotional data.

[0155] A "glossary" is information that provides explanations of specialized or important terms used within an information resource.

[0156] "Visual display format" refers to a means of displaying information resources and additional information in a visual format such as graphs and charts, and is a means of providing information that appeals to users visually.

[0157] The embodiments for carrying out the present invention are described below. This system is operated around three parties: a server, a terminal, and a user. The server efficiently collects news articles from the internet. The collected information resources are processed by a generation processing device, and important terms and numerical information are extracted using natural language processing technology. This process may use NLTK or spaCy as programming libraries. The generation AI model uses algorithms developed by artificial intelligence providers such as OpenAI (registered trademark) to generate relevant information based on prompt text.

[0158] Next, the device collects user interaction data (text input, click data, etc.). This data is sent to a server, where a sentiment analysis engine analyzes the user's emotions. This analysis often utilizes machine learning frameworks such as TENSORFLOW® or PyTorch. Based on the sentiment data, the server adjusts and customizes the content of news articles and additional information to provide the user with the most relevant information.

[0159] Customized information is displayed to the user through their device. The user interface visually changes according to the user's emotional state and may include visualized data, additional listening music, and entertainment content. This allows the user to have a pleasant browsing experience with relevant information in real time.

[0160] For example, when a user is viewing news about an economic crisis, if the emotion engine identifies anxiety, the server will highlight information indicating signs of economic recovery, providing a more stable perspective. Another example of a practical prompt is, "Please generate the most relevant news articles based on my current emotional state." In this way, the system provides information optimized for each individual user.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server retrieves news articles from the internet. The input is a pre-configured URL of a news source or feed URL. Specifically, it sends an HTTP request using a programming library (e.g., Python's requests or Scrapy) and retrieves the resulting HTML data. The output is raw HTML data.

[0164] Step 2:

[0165] The server analyzes the HTML data of the acquired news articles and extracts important terms and numerical information. The input is the HTML data obtained in step 1. Specifically, it uses natural language processing libraries (e.g., NLTK or spaCy) to split and tokenize the text data and extract important words and numbers. As a result, a list of important terms and numerical information are output.

[0166] Step 3:

[0167] The server uses a generative AI model to generate additional information based on the extracted key terms and numerical data. The input consists of the key term list and numerical data obtained in step 2. Specifically, a prompt (e.g., "Please provide background information for this article.") is sent to the generative AI model to generate relevant information. This output is the generated additional information.

[0168] Step 4:

[0169] The device collects user interaction data (e.g., keyboard input and click information) and sends it to the server. Input is the user's actions. Specifically, this data is obtained in real time via a JavaScript® tracking script. The output of this process is the user interaction data.

[0170] Step 5:

[0171] The server analyzes the user's emotions using interaction data received from the terminal. The input is the user interaction data obtained in step 4. Specifically, it uses an emotion analysis engine and a machine learning framework (e.g., TensorFlow or PyTorch) to identify the user's emotional state. The output is emotion data.

[0172] Step 6:

[0173] The server customizes news articles by adjusting the content and additional information based on the generated sentiment data. The input consists of the additional information generated in step 3 and the sentiment data identified in step 5. Specifically, it selects information based on user sentiment and adjusts the display order and content via database queries and a content management system. The output is the customized news content.

[0174] Step 7:

[0175] The device displays customized news content to the user. The input is the customized content obtained in step 6. Specifically, dynamic page rendering techniques using CSS and JavaScript are used to display the information on the screen. As a result, the user can view news and visual content that suits their mood.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern society, the overwhelming amount of information is a major source of stress for people. Furthermore, providing appropriate information based on individual user emotions is currently difficult. While there is a demand for personalized information that resonates with users' emotions when they access news and other information, systems that effectively achieve this are not yet widely available.

[0179] 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.

[0180] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, means for analyzing the user's emotional input to acquire emotional information, means for customizing the additional information based on the emotional information, means for displaying the customized additional information and information resources on a terminal device, and means for changing the display layout of the user interface according to the user's emotional state. This makes it possible to provide optimal news information based on the individual emotions of each user.

[0181] A "generation processing device" is a device that extracts important words and numerical information from information resources and generates related additional information.

[0182] "Information resources" refers to all data and content that are analyzed and processed by generation and processing equipment.

[0183] "Key words" are words extracted from information resources that have particular significance in analysis and information provision.

[0184] "Numerical information" refers to data extracted from information resources and expressed as numerical values.

[0185] "Emotional input" refers to input data that indicates the user's emotions and is used for emotion analysis.

[0186] "Emotional information" refers to data that indicates a specific emotional state, which is analyzed and obtained from emotional input.

[0187] "Additional information" refers to supplementary data and information related to important words and numerical information, generated by the generation and processing unit.

[0188] "Customization" refers to the act of adjusting additional information and the user interface based on the emotional information of individual users.

[0189] A "terminal device" is an electronic device used to display customized information to users.

[0190] "User interface" is a general term for screens and operating methods that enable the exchange of information between a user and a terminal device.

[0191] "Layout" refers to the structure, color scheme, and arrangement of design elements within a user interface.

[0192] The server first collects news and information resources via a network connected to a generation processing unit. It extracts important words and numerical information from these resources and generates related additional information using a generation AI model. Natural language processing and sentiment analysis are performed using Python and TensorFlow. In particular, sentiment information is analyzed based on sentiment input obtained through the user's sentiment-based interface. This allows the server to obtain the user's emotional state in real time.

[0193] The device dynamically customizes the user interface based on emotional information provided by the server. This ensures that customized news and information are displayed in a way that is best suited to the user. For example, when a user is feeling down, the device might suggest relaxing music and prioritize displaying positive news. Smart glasses and other mobile devices are often used in this information delivery process. These devices include cameras and sensors and are equipped with the ability to collect user interaction data as emotional input.

[0194] On the device the user normally uses, a generative AI model is used to suggest recommendations and entertainment content that are tailored to the user's emotional state. An example of a prompt used here is, "If the user's emotional state is determined to be stressed, display articles and related content that can help them relax."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server collects news and information resources via the network. It takes text information from various websites and news feeds as input, and outputs it as formatted data for analysis by a data generation and processing unit.

[0198] Step 2:

[0199] The server uses a generation and processing unit to extract important words and numerical information from the acquired information resources. This process utilizes natural language processing techniques to tokenize and tag parts of speech, and analyzes each part of the information resource. The extracted important words and numerical information are then output.

[0200] Step 3:

[0201] The server uses a generative AI model to generate additional relevant information based on the extracted key words and numerical data. Here, a pre-trained model determines the context and relevance of the information and generates textual and visual data. The input to this process is the data obtained in step 2, and the output is the generated additional information.

[0202] Step 4:

[0203] Users input emotions through their devices. Here, an emotion analysis engine acquires emotional information based on data obtained from user interactions such as text input and clicks. The input is user interaction data, and the output is emotional information.

[0204] Step 5:

[0205] The server customizes additional relevant information based on the acquired sentiment information. The sentiment information is used as a prompt to instruct the generative AI model on the optimal direction for providing information. In this step, the sentiment information and the additional information generated in step 3 are used as input, and the customized information set is output.

[0206] Step 6:

[0207] The terminal displays customized information received from the server on the user interface. The terminal presents additional information with a layout adapted to the user's emotions, delivering it to the user in a visually easy-to-understand format. The input for this step is a customized set of information, and the output is the final display to the user.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention relates to a system that, when providing news information to a user, uses a generation and processing device to extract important words and numerical information from information resources, and generates and displays related additional information. This system consists of three main components: a server, a terminal, and a user.

[0225] The server first collects the latest articles from multiple news sources. This collected information resource is then processed by a generation and processing unit, from which key words and numerical information are extracted. The generated key words indicate characteristic elements and topics of the article, while the numerical information represents related data.

[0226] Next, the server generates supplementary information based on the extracted key words and numerical data. This additional information includes definitions of terms, background explanations, and statistical graphs and charts based on the data. This allows users to obtain supplementary information to gain a deeper understanding of the news article.

[0227] Furthermore, the server manages user history and interest information, personalizing additional information. Based on a user's past browsing history and areas of interest, content is personalized, optimizing the news experience. This personalized information provides each user with more relevant information and helps them understand news articles.

[0228] The terminal displays information resources and additional information received from the server on a user-friendly interface that is easily accessible to the user. This display is neatly arranged and in a user-friendly format. Users can select items of interest from the presented information and access detailed information to gain a deeper understanding of the news.

[0229] As a concrete example, suppose a user is reading a news article about "changes to the health insurance system." In this case, the server extracts key words such as "insurance premiums," "general practice," and "preventive medicine," and based on these, generates new policies and graphs based on past statistical data. Furthermore, for users with a high interest in health, additional information such as further research findings and opinions related to this field is provided. As a result, users can gain a deeper understanding than that of a mere news article.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server retrieves articles from news sources. This includes using web-based RSS feeds and APIs to automatically collect the latest information. The collected articles are temporarily stored in a database for subsequent processing.

[0233] Step 2:

[0234] The server uses a generation and processing unit to extract important words and numerical information from the retrieved articles. Natural language processing technology is used to identify keywords within the articles and identify related numerical data. This clarifies the article's theme and key points.

[0235] Step 3:

[0236] The server generates additional relevant information based on extracted key words and numerical data. A generative AI model is used to present definitions of terms, background information, and relevant statistical data. Graphs and charts are also created to facilitate visual understanding.

[0237] Step 4:

[0238] The server personalizes additional information based on the user's history and interests. This involves analyzing the user's past browsing history and preferences to provide the most relevant information to each individual user. Personalization is crucial for improving the relevance of the information.

[0239] Step 5:

[0240] The server sends packets containing the final news article and personalized additional information to the terminal. The transmitted data is compressed and encrypted to ensure the security of the communication.

[0241] Step 6:

[0242] The terminal decompresses the data received from the server and displays it on the user interface. The visual layout and navigation are optimized to allow users to easily access information of interest.

[0243] Step 7:

[0244] Users view the provided news articles and additional information, and search for more detailed information as needed. User selections and actions are logged and used to personalize future experiences.

[0245] (Example 1)

[0246] 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."

[0247] Conventional news distribution systems struggle to provide information tailored to individual user interests, resulting in problems such as insufficient or excessive information simply by distributing news. Furthermore, they lack background information and relationship diagrams necessary for a deeper understanding of the news, hindering user comprehension.

[0248] 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.

[0249] In this invention, the server includes means for acquiring data resources from news sources and extracting important words and numerical data using natural language processing techniques; means for generating relevant information based on the extracted words and numerical data; and means for personalizing the relevant information according to the user's history and interest data. This enables users to receive news that includes personalized background information and to understand it more deeply.

[0250] A "news source" is an external data source that provides articles and reports from multiple news providers and media organizations.

[0251] "Data resources" refer to the collection of information that makes up news articles and reports that have been gathered.

[0252] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand and process human language.

[0253] "Key terms" are the main words or phrases that characterize the content of a news article.

[0254] "Numerical data" refers to statistical information and numerical data included within an article.

[0255] "Related information" refers to background explanations and supplementary materials generated based on extracted key keywords and numerical data.

[0256] "User history and interest data" refers to database information related to past browsing history and user interests.

[0257] "Personalization methods" refer to processes or technologies that customize information based on each user's interests and history.

[0258] A "data display device" is a device that provides an interface for visually presenting generated news information and related information to the user.

[0259] The news information provision system in this invention consists of three entities: a server, a terminal, and a user. In order to effectively implement the invention, the following specific devices and technologies are used.

[0260] First, the server collects data resources from multiple news sources. This collection process is carried out via Web APIs and RSS feeds, efficiently retrieving the latest news articles. The data resources are then stored in a database system, ready for further processing.

[0261] Next, the server uses generative AI models and natural language processing techniques to extract important words and numerical data. For this purpose, it implements algorithms such as tokenization, part-of-speech tagging, and named entity recognition. Specifically, it uses spaCy, an open-source natural language processing library, and AI model libraries.

[0262] Subsequently, the server generates related information based on the extracted data. This process accesses external knowledge bases and databases to supplement the information. Matplotlib and D3.js are used as data visualization tools with generative AI models to generate visual graphs and charts.

[0263] Furthermore, the server analyzes user history and interest data to personalize relevant information. Machine learning algorithms process the data and deliver a personalized news feed for each user.

[0264] The terminal displays relevant information obtained from the server on the user interface, making it easily accessible to the user. The display is arranged in an intuitive and easy-to-understand manner, and interactive elements are provided to allow users to explore the details of the information. Frontend frameworks such as Bootstrap and React are used in the terminal.

[0265] Users can browse news through the provided interface and obtain detailed information based on their interests.

[0266] As a concrete example, if a user views news about "changes to the health insurance system," the server extracts key terms such as "insurance premiums," "general practice," and "preventive medicine," and displays relevant information, including policy changes and statistical data, in graph form. An example of a prompt that might be used at this time is "Describe the key changes to the health insurance system and their impact in detail." Through this process, users can obtain information beyond mere news and gain a deeper understanding.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects data resources from news sources. Input is requests via news APIs and RSS feeds, and output is the latest news article data. Specifically, the server periodically accesses news sources, checks for new information, and stores any uncollected data in the database.

[0270] Step 2:

[0271] The server analyzes data resources using natural language processing techniques to extract important words and numerical data. The input is news article data stored in a database, and the output is a list of important words and numbers that characterize the articles. The server organizes the data by tokenizing the articles through a generative AI model and performing part-of-speech tagging and named entity recognition.

[0272] Step 3:

[0273] The server generates relevant information based on extracted keywords and numerical data. The input consists of key keywords and numerical data, while the output includes background explanations and statistical graphs as relevant information. Specifically, the server retrieves supplementary information from an external knowledge base and uses Tableau or D3.js to perform visualizations based on the data.

[0274] Step 4:

[0275] The server personalizes relevant information based on the user's history and interest data. Input is the user's past browsing history and current interests, and output is personalized news content. The server uses machine learning algorithms to analyze the user profile and select the most relevant information.

[0276] Step 5:

[0277] The terminal displays relevant information received from the server on the user interface. Input is personalized news content, and output is a visual representation on the user interface. Specifically, the terminal uses a front-end development framework to organize information and provide interactive elements.

[0278] (Application Example 1)

[0279] 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."

[0280] In today's information society, where a vast amount of news information circulates, users need related information and background knowledge to deeply understand each news article. However, if this information is not provided appropriately, users face information overload, making it difficult to gain accurate understanding. Therefore, there is a need for visually and personalized information presentation that allows users to quickly grasp the importance and relevance of an article.

[0281] 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.

[0282] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, and means for presenting the content to the user in a visual form on a visual display device. This enables the user to visually understand important information related to news articles and to quickly and deeply grasp the context of the articles.

[0283] A "generation processing device" is a computing device that extracts important elements from information resources and generates related additional information.

[0284] "Important words" refer to words that indicate the theme or characteristics of the content in information resources such as news articles.

[0285] "Numerical information" refers to quantitative data and numerical values contained in information resources.

[0286] "Additional information" refers to relevant information and explanations generated based on important words and numerical information, which are useful for users to understand the information more deeply.

[0287] "Visual display device" refers to a device through which users can visually confirm information, such as a smartphone or a head-mounted display.

[0288] "User history information" refers to data on what information the user has browsed or operated in the past.

[0289] "Interest information" refers to data indicating what topics or themes a specific user is interested in.

[0290] "Personalization" refers to the process of adapting information and services provided based on the history information and interests of each user.

[0291] This invention realizes a system that visually and clearly provides relevant important information when a user browses a news article. The system mainly operates with three components: a server, a terminal, and a user.

[0292] The server first obtains news data from multiple information sources. This data is analyzed by a natural language processing model (such as BERT or GPT-3) using a generation processing device, and important words and numerical information are extracted. Then, based on this extracted information, the server generates additional explanatory information and data for visual display. This process is executed by a generation AI model. Furthermore, the generated information is personalized based on the history information and interest information of each user.

[0293] The device displays information received from the server through a user interface. The device can be a smartphone or a head-mounted display (HMD), through which the user views the information. The information presented on the visual display device includes in-depth explanations related to important words and data that the user finds interesting. This allows the user to quickly understand the information in the article and obtain relevant context.

[0294] As a concrete example, when a user reads an article about new environmental policies, the server extracts key terms such as "greenhouse gases" and "renewable energy," and generates graphs and explanatory content based on these terms. This visual information is provided to the user via an HMD (Head-Mounted Display) to facilitate their understanding.

[0295] A concrete example of a prompt for a generative AI model is: "Please analyze the news article on recent environmental policies and list key concepts and relevant data for visualization." Using this prompt, the AI ​​identifies important elements of the article and generates visual information.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects data from news sources. In this step, the server uses web scraping techniques to collect the latest articles from multiple news sites. Inputs include web URLs and API keys, and output is raw news article data.

[0299] Step 2:

[0300] The server uses a generation and processing unit to begin analyzing raw news article data. During this process, a natural language processing model (e.g., GPT-3) is used to extract important words and numerical information. The input is the collected news article data, and the output is the analyzed important words and numerical information. The model understands the content of the text and selects the most relevant words.

[0301] Step 3:

[0302] The server uses a generative AI model based on the extracted information to generate highly relevant additional information. In this step, it generates relevant explanatory text and visual data (graphs and statistics) based on the extracted words and numbers. The input requires the extracted words and numerical information, and the output is the generated additional information. This generation process is instructed to the AI ​​via prompts. The prompt used is "Please analyze the key concepts and produce descriptive content and visualizations for better understanding."

[0303] Step 4:

[0304] The server references the user's history and interests to personalize additional information. Database access is performed to retrieve past browsing history and topics of interest, and the information is then tailored based on this data. The input is the user's profile data, and the output is personalized additional information.

[0305] Step 5:

[0306] The device receives personalized information sent from the server and displays it to the user. At this stage, a visual display device is used to display the generated content in an easily understandable format. The input is display data from the server, and the output is visual information that the user accesses visually. The user then looks at this and takes action to gain a deeper understanding of the article.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0308] The present invention is a news information system that combines an emotion engine to improve the quality of information provision. This system is mainly composed of a server, a terminal, and a user, and has a function of recognizing the user's emotion and optimizing the display content of news.

[0309] First, the server collects news articles via the network. The collected information resources are processed through a generation processing device. Important words and numerical information are extracted from the articles, and related additional information is created by the generation AI model.

[0310] Next, an emotion engine is incorporated to analyze the user's emotion. Interaction data such as text input and clicks performed by the user through the terminal is analyzed to recognize various emotions such as joy, surprise, and sadness in real time. The recognized emotion data is managed by the server.

[0311] Based on the analysis results, the server customizes the additional information of the news article according to the user's current emotional state. For example, more news and positive information that give hope are provided to users who are feeling down, and detailed data analysis and visual graphs for promoting calmness are displayed to users who are excited.

[0312] The terminal displays news articles customized based on the emotion to the user. The user interface is individualized, and different layouts and color schemes may be selected according to the emotional state. The user can use the provided information in line with their own emotions and gain a deeper understanding of the news.

[0313] For example, when a user is browsing news about an economic crisis, if the emotion engine detects the user's anxiety, the server will prioritize displaying information about economic measures and signs of recovery. Furthermore, in such situations, suggestions for listening music or distracting entertainment content may also be presented to help maintain composure. This system allows users not only to obtain news but also to enjoy information while gaining emotional reassurance.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The server collects the latest articles from multiple news sources. This is done using APIs and RSS feeds to automatically retrieve the latest articles. The collected articles are temporarily stored in a database.

[0317] Step 2:

[0318] The server uses a generation and processing unit to extract important words and numerical information from stored articles. Natural language processing techniques are used here to identify keywords and themes in the articles.

[0319] Step 3:

[0320] Based on the key words and numerical information extracted by the server, an AI model is used to create additional information. This additional information includes definitions of terms, background information, and statistical graphs.

[0321] Step 4:

[0322] The server uses an emotion engine to analyze interaction data received from the user's device. It analyzes the text and behavioral data entered by the user to identify their current emotional state.

[0323] Step 5:

[0324] The server personalizes additional information in news articles based on the emotions it recognizes. For example, it provides reassuring content to anxious users and detailed data to help agitated users calm down.

[0325] Step 6:

[0326] The server packets the final news article and any customized additional information and sends it to the terminal. At this point, the data is encrypted to ensure the security of the communication.

[0327] Step 7:

[0328] The device decompresses data received from the server and displays it on the user interface. The layout and color scheme are designed to be emotionally resonant, allowing users to intuitively understand the information.

[0329] Step 8:

[0330] Users view the presented news articles and related information, and research further details as needed. By providing information that responds to the user's emotions, it becomes possible to process news with a deeper understanding and while maintaining emotional stability.

[0331] (Example 2)

[0332] 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".

[0333] In modern society, information overload makes it difficult for users to efficiently acquire only the information they need. Furthermore, users often experience psychological burden when receiving information, and there is a demand for information tailored to their emotional state. However, conventional systems are insufficient in providing information that considers user emotions, resulting in limitations in improving user satisfaction. Therefore, this invention aims to improve the quality of information provision by enabling the optimal provision of necessary and positive information while considering user emotions.

[0334] 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.

[0335] In this invention, the server includes means for extracting important terms and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important terms and numerical information, and means for generating emotional data using an emotional analysis engine that analyzes the user's emotional state. As a result, information is tailored to the user's emotional state, information provision is more personalized, and information reception becomes beneficial and stress-free for the user.

[0336] A "generation and processing device" is a device that acquires information resources, extracts important terms and numerical information from them, and generates additional information as needed.

[0337] "Information resources" refer to news articles and other related data obtained from the internet, and are basic data used in information provision systems.

[0338] "Key terms" are words extracted from information resources that are considered most important for understanding the subject matter of an article or data.

[0339] "Numerical information" refers to statistical or quantitative numerical data contained within information resources, and is an essential element for understanding and analyzing information.

[0340] An "emotion analysis engine" is software or a system that analyzes a user's text input and behavioral data to determine the user's emotional state in real time.

[0341] "Emotional data" refers to data representing the user's emotional state, generated by an emotion analysis engine, and is used for selecting and displaying information.

[0342] A "user interface" refers to the visual elements that allow a user to interact with an information system, including display elements such as screen layout and color scheme displayed on a terminal.

[0343] A "data storage device" is a hardware or software component used to store a user's history information, interest information, and emotional data.

[0344] A "glossary" is information that provides explanations of specialized or important terms used within an information resource.

[0345] "Visual display format" refers to a means of displaying information resources and additional information in a visual format such as graphs and charts, and is a means of providing information that appeals to users visually.

[0346] The embodiments for carrying out the present invention are described below. This system is operated around three parties: a server, a terminal, and a user. The server efficiently collects news articles from the internet. The collected information resources are processed by a generation processing device, and important terms and numerical information are extracted using natural language processing technology. This process may use NLTK or spaCy as programming libraries. The generation AI model uses algorithms developed by artificial intelligence providers such as OpenAI to generate relevant information based on prompt text.

[0347] Next, the device collects user interaction data (text input, click data, etc.). This data is sent to a server, where a sentiment analysis engine analyzes the user's emotions. This analysis often utilizes machine learning frameworks such as TensorFlow or PyTorch. Based on the sentiment data, the server adjusts and customizes the content of news articles and additional information to provide the user with the most relevant information.

[0348] Customized information is displayed to the user through their device. The user interface visually changes according to the user's emotional state and may include visualized data, additional listening music, and entertainment content. This allows the user to have a pleasant browsing experience with relevant information in real time.

[0349] For example, when a user is viewing news about an economic crisis, if the emotion engine identifies anxiety, the server will highlight information indicating signs of economic recovery, providing a more stable perspective. Another example of a practical prompt is, "Please generate the most relevant news articles based on my current emotional state." In this way, the system provides information optimized for each individual user.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The server retrieves news articles from the internet. The input is a pre-configured URL of a news source or feed URL. Specifically, it sends an HTTP request using a programming library (e.g., Python's requests or Scrapy) and retrieves the resulting HTML data. The output is raw HTML data.

[0353] Step 2:

[0354] The server analyzes the HTML data of the acquired news articles and extracts important terms and numerical information. The input is the HTML data obtained in step 1. Specifically, it uses natural language processing libraries (e.g., NLTK or spaCy) to split and tokenize the text data and extract important words and numbers. As a result, a list of important terms and numerical information are output.

[0355] Step 3:

[0356] The server uses a generative AI model to generate additional information based on the extracted key terms and numerical data. The input consists of the key term list and numerical data obtained in step 2. Specifically, a prompt (e.g., "Please provide background information for this article.") is sent to the generative AI model to generate relevant information. This output is the generated additional information.

[0357] Step 4:

[0358] The device collects user interaction data (e.g., keyboard input and click information) and sends it to the server. Input is the user's actions. Specifically, this data is obtained in real time via a JavaScript tracking script. The output of this process is the user interaction data.

[0359] Step 5:

[0360] The server analyzes the user's emotions using interaction data received from the terminal. The input is the user interaction data obtained in step 4. Specifically, it uses an emotion analysis engine and a machine learning framework (e.g., TensorFlow or PyTorch) to identify the user's emotional state. The output is emotion data.

[0361] Step 6:

[0362] The server customizes news articles by adjusting the content and additional information based on the generated sentiment data. The input consists of the additional information generated in step 3 and the sentiment data identified in step 5. Specifically, it selects information based on user sentiment and adjusts the display order and content via database queries and a content management system. The output is the customized news content.

[0363] Step 7:

[0364] The device displays customized news content to the user. The input is the customized content obtained in step 6. Specifically, dynamic page rendering techniques using CSS and JavaScript are used to display the information on the screen. As a result, the user can view news and visual content that suits their mood.

[0365] (Application Example 2)

[0366] 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."

[0367] In modern society, the overwhelming amount of information is a major source of stress for people. Furthermore, providing appropriate information based on individual user emotions is currently difficult. While there is a demand for personalized information that resonates with users' emotions when they access news and other information, systems that effectively achieve this are not yet widely available.

[0368] 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.

[0369] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, means for analyzing the user's emotional input to acquire emotional information, means for customizing the additional information based on the emotional information, means for displaying the customized additional information and information resources on a terminal device, and means for changing the display layout of the user interface according to the user's emotional state. This makes it possible to provide optimal news information based on the individual emotions of each user.

[0370] A "generation processing device" is a device that extracts important words and numerical information from information resources and generates related additional information.

[0371] "Information resources" refers to all data and content that are analyzed and processed by generation and processing equipment.

[0372] "Key words" are words extracted from information resources that have particular significance in analysis and information provision.

[0373] "Numerical information" refers to data extracted from information resources and expressed as numerical values.

[0374] "Emotional input" refers to input data that indicates the user's emotions and is used for emotion analysis.

[0375] "Emotional information" refers to data that indicates a specific emotional state, which is analyzed and obtained from emotional input.

[0376] "Additional information" refers to supplementary data and information related to important words and numerical information, generated by the generation and processing unit.

[0377] "Customization" refers to the act of adjusting additional information and the user interface based on the emotional information of individual users.

[0378] A "terminal device" is an electronic device used to display customized information to users.

[0379] "User interface" is a general term for screens and operating methods that enable the exchange of information between a user and a terminal device.

[0380] "Layout" refers to the structure, color scheme, and arrangement of design elements within a user interface.

[0381] The server first collects news and information resources via a network connected to a generation processing unit. It extracts important words and numerical information from these resources and generates related additional information using a generation AI model. Natural language processing and sentiment analysis are performed using Python and TensorFlow. In particular, sentiment information is analyzed based on sentiment input obtained through the user's sentiment-based interface. This allows the server to obtain the user's emotional state in real time.

[0382] The device dynamically customizes the user interface based on emotional information provided by the server. This ensures that customized news and information are displayed in a way that is best suited to the user. For example, when a user is feeling down, the device might suggest relaxing music and prioritize displaying positive news. Smart glasses and other mobile devices are often used in this information delivery process. These devices include cameras and sensors and are equipped with the ability to collect user interaction data as emotional input.

[0383] On the device the user normally uses, a generative AI model is used to suggest recommendations and entertainment content that are tailored to the user's emotional state. An example of a prompt used here is, "If the user's emotional state is determined to be stressed, display articles and related content that can help them relax."

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The server collects news and information resources via the network. It takes text information from various websites and news feeds as input, and outputs it as formatted data for analysis by a data generation and processing unit.

[0387] Step 2:

[0388] The server uses a generation and processing unit to extract important words and numerical information from the acquired information resources. This process utilizes natural language processing techniques to tokenize and tag parts of speech, and analyzes each part of the information resource. The extracted important words and numerical information are then output.

[0389] Step 3:

[0390] The server uses a generative AI model to generate additional relevant information based on the extracted key words and numerical data. Here, a pre-trained model determines the context and relevance of the information and generates textual and visual data. The input to this process is the data obtained in step 2, and the output is the generated additional information.

[0391] Step 4:

[0392] Users input emotions through their devices. Here, an emotion analysis engine acquires emotional information based on data obtained from user interactions such as text input and clicks. The input is user interaction data, and the output is emotional information.

[0393] Step 5:

[0394] The server customizes additional relevant information based on the acquired sentiment information. The sentiment information is used as a prompt to instruct the generative AI model on the optimal direction for providing information. In this step, the sentiment information and the additional information generated in step 3 are used as input, and the customized information set is output.

[0395] Step 6:

[0396] The terminal displays customized information received from the server on the user interface. The terminal presents additional information with a layout adapted to the user's emotions, delivering it to the user in a visually easy-to-understand format. The input for this step is a customized set of information, and the output is the final display to the user.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention relates to a system that, when providing news information to a user, uses a generation and processing device to extract important words and numerical information from information resources, and generates and displays related additional information. This system consists of three main components: a server, a terminal, and a user.

[0414] The server first collects the latest articles from multiple news sources. This collected information resource is then processed by a generation and processing unit, from which key words and numerical information are extracted. The generated key words indicate characteristic elements and topics of the article, while the numerical information represents related data.

[0415] Next, the server generates supplementary information based on the extracted key words and numerical data. This additional information includes definitions of terms, background explanations, and statistical graphs and charts based on the data. This allows users to obtain supplementary information to gain a deeper understanding of the news article.

[0416] Furthermore, the server manages user history and interest information, personalizing additional information. Based on a user's past browsing history and areas of interest, content is personalized, optimizing the news experience. This personalized information provides each user with more relevant information and helps them understand news articles.

[0417] The terminal displays information resources and additional information received from the server on a user-friendly interface that is easily accessible to the user. This display is neatly arranged and in a user-friendly format. Users can select items of interest from the presented information and access detailed information to gain a deeper understanding of the news.

[0418] As a concrete example, suppose a user is reading a news article about "changes to the health insurance system." In this case, the server extracts key words such as "insurance premiums," "general practice," and "preventive medicine," and based on these, generates new policies and graphs based on past statistical data. Furthermore, for users with a high interest in health, additional information such as further research findings and opinions related to this field is provided. As a result, users can gain a deeper understanding than that of a mere news article.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server retrieves articles from news sources. This includes using web-based RSS feeds and APIs to automatically collect the latest information. The collected articles are temporarily stored in a database for subsequent processing.

[0422] Step 2:

[0423] The server uses a generation and processing unit to extract important words and numerical information from the retrieved articles. Natural language processing technology is used to identify keywords within the articles and identify related numerical data. This clarifies the article's theme and key points.

[0424] Step 3:

[0425] The server generates additional relevant information based on extracted key words and numerical data. A generative AI model is used to present definitions of terms, background information, and relevant statistical data. Graphs and charts are also created to facilitate visual understanding.

[0426] Step 4:

[0427] The server personalizes additional information based on the user's history and interests. This involves analyzing the user's past browsing history and preferences to provide the most relevant information to each individual user. Personalization is crucial for improving the relevance of the information.

[0428] Step 5:

[0429] The server sends packets containing the final news article and personalized additional information to the terminal. The transmitted data is compressed and encrypted to ensure the security of the communication.

[0430] Step 6:

[0431] The terminal decompresses the data received from the server and displays it on the user interface. The visual layout and navigation are optimized to allow users to easily access information of interest.

[0432] Step 7:

[0433] Users view the provided news articles and additional information, and search for more detailed information as needed. User selections and actions are logged and used to personalize future experiences.

[0434] (Example 1)

[0435] 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."

[0436] Conventional news distribution systems struggle to provide information tailored to individual user interests, resulting in problems such as insufficient or excessive information simply by distributing news. Furthermore, they lack background information and relationship diagrams necessary for a deeper understanding of the news, hindering user comprehension.

[0437] 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.

[0438] In this invention, the server includes means for acquiring data resources from news sources and extracting important words and numerical data using natural language processing techniques; means for generating relevant information based on the extracted words and numerical data; and means for personalizing the relevant information according to the user's history and interest data. This enables users to receive news that includes personalized background information and to understand it more deeply.

[0439] A "news source" is an external data source that provides articles and reports from multiple news providers and media organizations.

[0440] "Data resources" refer to the collection of information that makes up news articles and reports that have been gathered.

[0441] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand and process human language.

[0442] "Key terms" are the main words or phrases that characterize the content of a news article.

[0443] "Numerical data" refers to statistical information and numerical data included within an article.

[0444] "Related information" refers to background explanations and supplementary materials generated based on extracted key keywords and numerical data.

[0445] "User history and interest data" refers to database information related to past browsing history and user interests.

[0446] "Personalization methods" refer to processes or technologies that customize information based on each user's interests and history.

[0447] A "data display device" is a device that provides an interface for visually presenting generated news information and related information to the user.

[0448] The news information provision system in this invention consists of three entities: a server, a terminal, and a user. In order to effectively implement the invention, the following specific devices and technologies are used.

[0449] First, the server collects data resources from multiple news sources. This collection process is carried out via Web APIs and RSS feeds, efficiently retrieving the latest news articles. The data resources are then stored in a database system, ready for further processing.

[0450] Next, the server uses generative AI models and natural language processing techniques to extract important words and numerical data. For this purpose, it implements algorithms such as tokenization, part-of-speech tagging, and named entity recognition. Specifically, it uses spaCy, an open-source natural language processing library, and AI model libraries.

[0451] Subsequently, the server generates related information based on the extracted data. This process accesses external knowledge bases and databases to supplement the information. Matplotlib and D3.js are used as data visualization tools with generative AI models to generate visual graphs and charts.

[0452] Furthermore, the server analyzes user history and interest data to personalize relevant information. Machine learning algorithms process the data and deliver a personalized news feed for each user.

[0453] The terminal displays relevant information obtained from the server on the user interface, making it easily accessible to the user. The display is arranged in an intuitive and easy-to-understand manner, and interactive elements are provided to allow users to explore the details of the information. Frontend frameworks such as Bootstrap and React are used in the terminal.

[0454] Users can browse news through the provided interface and obtain detailed information based on their interests.

[0455] As a concrete example, if a user views news about "changes to the health insurance system," the server extracts key terms such as "insurance premiums," "general practice," and "preventive medicine," and displays relevant information, including policy changes and statistical data, in graph form. An example of a prompt that might be used at this time is "Describe the key changes to the health insurance system and their impact in detail." Through this process, users can obtain information beyond mere news and gain a deeper understanding.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The server collects data resources from news sources. Input is requests via news APIs and RSS feeds, and output is the latest news article data. Specifically, the server periodically accesses news sources, checks for new information, and stores any uncollected data in the database.

[0459] Step 2:

[0460] The server analyzes data resources using natural language processing techniques to extract important words and numerical data. The input is news article data stored in a database, and the output is a list of important words and numbers that characterize the articles. The server organizes the data by tokenizing the articles through a generative AI model and performing part-of-speech tagging and named entity recognition.

[0461] Step 3:

[0462] The server generates relevant information based on extracted keywords and numerical data. The input consists of key keywords and numerical data, while the output includes background explanations and statistical graphs as relevant information. Specifically, the server retrieves supplementary information from an external knowledge base and uses Tableau or D3.js to perform visualizations based on the data.

[0463] Step 4:

[0464] The server personalizes relevant information based on the user's history and interest data. Input is the user's past browsing history and current interests, and output is personalized news content. The server uses machine learning algorithms to analyze the user profile and select the most relevant information.

[0465] Step 5:

[0466] The terminal displays relevant information received from the server on the user interface. Input is personalized news content, and output is a visual representation on the user interface. Specifically, the terminal uses a front-end development framework to organize information and provide interactive elements.

[0467] (Application Example 1)

[0468] 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."

[0469] In today's information society, where a vast amount of news information circulates, users need related information and background knowledge to deeply understand each news article. However, if this information is not provided appropriately, users face information overload, making it difficult to gain accurate understanding. Therefore, there is a need for visually and personalized information presentation that allows users to quickly grasp the importance and relevance of an article.

[0470] 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.

[0471] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, and means for presenting the content to the user in a visual form on a visual display device. This enables the user to visually understand important information related to news articles and to quickly and deeply grasp the context of the articles.

[0472] A "generation processing device" is a computing device that extracts important elements from information resources and generates related additional information.

[0473] "Key words" are words that indicate the main topic or characteristics of information resources such as news articles.

[0474] "Numerical information" refers to quantitative data and numerical values ​​contained in information resources.

[0475] "Additional information" refers to related information and explanations generated based on important words and numerical data, which help users understand the information more deeply.

[0476] A "visual display device" is a device that allows users to visually confirm information, such as a smartphone or a head-mounted display.

[0477] "User history information" refers to data about what kind of information a user has viewed or interacted with in the past.

[0478] "Interest information" refers to data that shows what topics or themes a particular user is interested in.

[0479] Personalization refers to the process of adapting information and services provided based on each user's history and interests.

[0480] This invention realizes a system that provides users with relevant and important information in an easy-to-understand visual format when they view news articles. The system mainly operates with three components: a server, a terminal, and a user.

[0481] The server first acquires news data from multiple sources. This data is analyzed using a generative processing unit and natural language processing models (such as BERT or GPT-3) to extract important words and numerical information. The server then uses this extracted information to generate additional explanatory information and data for visual display. This process is performed by a generative AI model. Furthermore, the generated information is personalized based on each user's history and interests.

[0482] The device displays information received from the server through a user interface. The device can be a smartphone or a head-mounted display (HMD), through which the user views the information. The information presented on the visual display device includes in-depth explanations related to important words and data that the user finds interesting. This allows the user to quickly understand the information in the article and obtain relevant context.

[0483] As a concrete example, when a user reads an article about new environmental policies, the server extracts key terms such as "greenhouse gases" and "renewable energy," and generates graphs and explanatory content based on these terms. This visual information is provided to the user via an HMD (Head-Mounted Display) to facilitate their understanding.

[0484] A concrete example of a prompt for a generative AI model is: "Please analyze the news article on recent environmental policies and list key concepts and relevant data for visualization." Using this prompt, the AI ​​identifies important elements of the article and generates visual information.

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server collects data from news sources. In this step, the server uses web scraping techniques to collect the latest articles from multiple news sites. Inputs include web URLs and API keys, and output is raw news article data.

[0488] Step 2:

[0489] The server uses a generation and processing unit to begin analyzing raw news article data. During this process, a natural language processing model (e.g., GPT-3) is used to extract important words and numerical information. The input is the collected news article data, and the output is the analyzed important words and numerical information. The model understands the content of the text and selects the most relevant words.

[0490] Step 3:

[0491] The server uses a generative AI model based on the extracted information to generate highly relevant additional information. In this step, it generates relevant explanatory text and visual data (graphs and statistics) based on the extracted words and numbers. The input requires the extracted words and numerical information, and the output is the generated additional information. This generation process is instructed to the AI ​​via prompts. The prompt used is "Please analyze the key concepts and produce descriptive content and visualizations for better understanding."

[0492] Step 4:

[0493] The server references the user's history and interests to personalize additional information. Database access is performed to retrieve past browsing history and topics of interest, and the information is then tailored based on this data. The input is the user's profile data, and the output is personalized additional information.

[0494] Step 5:

[0495] The device receives personalized information sent from the server and displays it to the user. At this stage, a visual display device is used to display the generated content in an easily understandable format. The input is display data from the server, and the output is visual information that the user accesses visually. The user then looks at this and takes action to gain a deeper understanding of the article.

[0496] 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.

[0497] This invention relates to a news information system that incorporates an emotion engine to improve the quality of information delivery. This system is centered around a server, terminals, and users, and has the function of recognizing the user's emotions and optimizing the content of the news displayed.

[0498] The server first collects news articles via the network. These collected information resources are then processed through a generation and processing unit. Key words and numerical information are extracted from the articles, and related additional information is created by a generation AI model.

[0499] Next, an emotion engine is incorporated to analyze the user's emotions. It analyzes interaction data such as text input and clicks performed by the user through the device, recognizing a variety of emotions such as joy, surprise, and sadness in real time. The recognized emotion data is managed on a server.

[0500] Based on the analysis results, the server customizes additional information in news articles to match the user's current emotional state. For example, it provides more hopeful news and positive information to depressed users, and adjusts the display to show more detailed data analysis and visual graphs to encourage calmness in excited users.

[0501] The device displays news articles customized to the user based on their emotions. The user interface is personalized, and different layouts and color schemes may be selected depending on the user's emotional state. Users can use the provided information in conjunction with their own emotions to gain a deeper understanding of the news.

[0502] For example, when a user is browsing news about an economic crisis, if the emotion engine detects the user's anxiety, the server will prioritize displaying information about economic measures and signs of recovery. Furthermore, in such situations, suggestions for listening music or distracting entertainment content may also be presented to help maintain composure. This system allows users not only to obtain news but also to enjoy information while gaining emotional reassurance.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The server collects the latest articles from multiple news sources. This is done using APIs and RSS feeds to automatically retrieve the latest articles. The collected articles are temporarily stored in a database.

[0506] Step 2:

[0507] The server uses a generation and processing unit to extract important words and numerical information from stored articles. Natural language processing techniques are used here to identify keywords and themes in the articles.

[0508] Step 3:

[0509] Based on the key words and numerical information extracted by the server, an AI model is used to create additional information. This additional information includes definitions of terms, background information, and statistical graphs.

[0510] Step 4:

[0511] The server uses an emotion engine to analyze interaction data received from the user's device. It analyzes the text and behavioral data entered by the user to identify their current emotional state.

[0512] Step 5:

[0513] The server personalizes additional information in news articles based on the emotions it recognizes. For example, it provides reassuring content to anxious users and detailed data to help agitated users calm down.

[0514] Step 6:

[0515] The server packets the final news article and any customized additional information and sends it to the terminal. At this point, the data is encrypted to ensure the security of the communication.

[0516] Step 7:

[0517] The device decompresses data received from the server and displays it on the user interface. The layout and color scheme are designed to be emotionally resonant, allowing users to intuitively understand the information.

[0518] Step 8:

[0519] Users view the presented news articles and related information, and research further details as needed. By providing information that responds to the user's emotions, it becomes possible to process news with a deeper understanding and while maintaining emotional stability.

[0520] (Example 2)

[0521] 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."

[0522] In modern society, information overload makes it difficult for users to efficiently acquire only the information they need. Furthermore, users often experience psychological burden when receiving information, and there is a demand for information tailored to their emotional state. However, conventional systems are insufficient in providing information that considers user emotions, resulting in limitations in improving user satisfaction. Therefore, this invention aims to improve the quality of information provision by enabling the optimal provision of necessary and positive information while considering user emotions.

[0523] 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.

[0524] In this invention, the server includes means for extracting important terms and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important terms and numerical information, and means for generating emotional data using an emotional analysis engine that analyzes the user's emotional state. As a result, information is tailored to the user's emotional state, information provision is more personalized, and information reception becomes beneficial and stress-free for the user.

[0525] A "generation and processing device" is a device that acquires information resources, extracts important terms and numerical information from them, and generates additional information as needed.

[0526] "Information resources" refer to news articles and other related data obtained from the internet, and are basic data used in information provision systems.

[0527] "Key terms" are words extracted from information resources that are considered most important for understanding the subject matter of an article or data.

[0528] "Numerical information" refers to statistical or quantitative numerical data contained within information resources, and is an essential element for understanding and analyzing information.

[0529] An "emotion analysis engine" is software or a system that analyzes a user's text input and behavioral data to determine the user's emotional state in real time.

[0530] "Emotional data" refers to data representing the user's emotional state, generated by an emotion analysis engine, and is used for selecting and displaying information.

[0531] A "user interface" refers to the visual elements that allow a user to interact with an information system, including display elements such as screen layout and color scheme displayed on a terminal.

[0532] A "data storage device" is a hardware or software component used to store a user's history information, interest information, and emotional data.

[0533] A "glossary" is information that provides explanations of specialized or important terms used within an information resource.

[0534] "Visual display format" refers to a means of displaying information resources and additional information in a visual format such as graphs and charts, and is a means of providing information that appeals to users visually.

[0535] The embodiments for carrying out the present invention are described below. This system is operated around three parties: a server, a terminal, and a user. The server efficiently collects news articles from the internet. The collected information resources are processed by a generation processing device, and important terms and numerical information are extracted using natural language processing technology. This process may use NLTK or spaCy as programming libraries. The generation AI model uses algorithms developed by artificial intelligence providers such as OpenAI to generate relevant information based on prompt text.

[0536] Next, the device collects user interaction data (text input, click data, etc.). This data is sent to a server, where a sentiment analysis engine analyzes the user's emotions. This analysis often utilizes machine learning frameworks such as TensorFlow or PyTorch. Based on the sentiment data, the server adjusts and customizes the content of news articles and additional information to provide the user with the most relevant information.

[0537] Customized information is displayed to the user through their device. The user interface visually changes according to the user's emotional state and may include visualized data, additional listening music, and entertainment content. This allows the user to have a pleasant browsing experience with relevant information in real time.

[0538] For example, when a user is viewing news about an economic crisis, if the emotion engine identifies anxiety, the server will highlight information indicating signs of economic recovery, providing a more stable perspective. Another example of a practical prompt is, "Please generate the most relevant news articles based on my current emotional state." In this way, the system provides information optimized for each individual user.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The server retrieves news articles from the internet. The input is a pre-configured URL of a news source or feed URL. Specifically, it sends an HTTP request using a programming library (e.g., Python's requests or Scrapy) and retrieves the resulting HTML data. The output is raw HTML data.

[0542] Step 2:

[0543] The server analyzes the HTML data of the acquired news articles and extracts important terms and numerical information. The input is the HTML data obtained in step 1. Specifically, it uses natural language processing libraries (e.g., NLTK or spaCy) to split and tokenize the text data and extract important words and numbers. As a result, a list of important terms and numerical information are output.

[0544] Step 3:

[0545] The server uses a generative AI model to generate additional information based on the extracted key terms and numerical data. The input consists of the key term list and numerical data obtained in step 2. Specifically, a prompt (e.g., "Please provide background information for this article.") is sent to the generative AI model to generate relevant information. This output is the generated additional information.

[0546] Step 4:

[0547] The device collects user interaction data (e.g., keyboard input and click information) and sends it to the server. Input is the user's actions. Specifically, this data is obtained in real time via a JavaScript tracking script. The output of this process is the user interaction data.

[0548] Step 5:

[0549] The server analyzes the user's emotions using interaction data received from the terminal. The input is the user interaction data obtained in step 4. Specifically, it uses an emotion analysis engine and a machine learning framework (e.g., TensorFlow or PyTorch) to identify the user's emotional state. The output is emotion data.

[0550] Step 6:

[0551] The server customizes news articles by adjusting the content and additional information based on the generated sentiment data. The input consists of the additional information generated in step 3 and the sentiment data identified in step 5. Specifically, it selects information based on user sentiment and adjusts the display order and content via database queries and a content management system. The output is the customized news content.

[0552] Step 7:

[0553] The device displays customized news content to the user. The input is the customized content obtained in step 6. Specifically, dynamic page rendering techniques using CSS and JavaScript are used to display the information on the screen. As a result, the user can view news and visual content that suits their mood.

[0554] (Application Example 2)

[0555] 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."

[0556] In modern society, the overwhelming amount of information is a major source of stress for people. Furthermore, providing appropriate information based on individual user emotions is currently difficult. While there is a demand for personalized information that resonates with users' emotions when they access news and other information, systems that effectively achieve this are not yet widely available.

[0557] 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.

[0558] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, means for analyzing the user's emotional input to acquire emotional information, means for customizing the additional information based on the emotional information, means for displaying the customized additional information and information resources on a terminal device, and means for changing the display layout of the user interface according to the user's emotional state. This makes it possible to provide optimal news information based on the individual emotions of each user.

[0559] A "generation processing device" is a device that extracts important words and numerical information from information resources and generates related additional information.

[0560] "Information resources" refers to all data and content that are analyzed and processed by generation and processing equipment.

[0561] "Key words" are words extracted from information resources that have particular significance in analysis and information provision.

[0562] "Numerical information" refers to data extracted from information resources and expressed as numerical values.

[0563] "Emotional input" refers to input data that indicates the user's emotions and is used for emotion analysis.

[0564] "Emotional information" refers to data that indicates a specific emotional state, which is analyzed and obtained from emotional input.

[0565] "Additional information" refers to supplementary data and information related to important words and numerical information, generated by the generation and processing unit.

[0566] "Customization" refers to the act of adjusting additional information and the user interface based on the emotional information of individual users.

[0567] A "terminal device" is an electronic device used to display customized information to users.

[0568] "User interface" is a general term for screens and operating methods that enable the exchange of information between a user and a terminal device.

[0569] "Layout" refers to the structure, color scheme, and arrangement of design elements within a user interface.

[0570] The server first collects news and information resources via a network connected to a generation processing unit. It extracts important words and numerical information from these resources and generates related additional information using a generation AI model. Natural language processing and sentiment analysis are performed using Python and TensorFlow. In particular, sentiment information is analyzed based on sentiment input obtained through the user's sentiment-based interface. This allows the server to obtain the user's emotional state in real time.

[0571] The device dynamically customizes the user interface based on emotional information provided by the server. This ensures that customized news and information are displayed in a way that is best suited to the user. For example, when a user is feeling down, the device might suggest relaxing music and prioritize displaying positive news. Smart glasses and other mobile devices are often used in this information delivery process. These devices include cameras and sensors and are equipped with the ability to collect user interaction data as emotional input.

[0572] On the device the user normally uses, a generative AI model is used to suggest recommendations and entertainment content that are tailored to the user's emotional state. An example of a prompt used here is, "If the user's emotional state is determined to be stressed, display articles and related content that can help them relax."

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The server collects news and information resources via the network. It takes text information from various websites and news feeds as input, and outputs it as formatted data for analysis by a data generation and processing unit.

[0576] Step 2:

[0577] The server uses a generation and processing unit to extract important words and numerical information from the acquired information resources. This process utilizes natural language processing techniques to tokenize and tag parts of speech, and analyzes each part of the information resource. The extracted important words and numerical information are then output.

[0578] Step 3:

[0579] The server uses a generative AI model to generate additional relevant information based on the extracted key words and numerical data. Here, a pre-trained model determines the context and relevance of the information and generates textual and visual data. The input to this process is the data obtained in step 2, and the output is the generated additional information.

[0580] Step 4:

[0581] Users input emotions through their devices. Here, an emotion analysis engine acquires emotional information based on data obtained from user interactions such as text input and clicks. The input is user interaction data, and the output is emotional information.

[0582] Step 5:

[0583] The server customizes additional relevant information based on the acquired sentiment information. The sentiment information is used as a prompt to instruct the generative AI model on the optimal direction for providing information. In this step, the sentiment information and the additional information generated in step 3 are used as input, and the customized information set is output.

[0584] Step 6:

[0585] The terminal displays customized information received from the server on the user interface. The terminal presents additional information with a layout adapted to the user's emotions, delivering it to the user in a visually easy-to-understand format. The input for this step is a customized set of information, and the output is the final display to the user.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention relates to a system that, when providing news information to a user, uses a generation and processing device to extract important words and numerical information from information resources, and generates and displays related additional information. This system consists of three main components: a server, a terminal, and a user.

[0604] The server first collects the latest articles from multiple news sources. This collected information resource is then processed by a generation and processing unit, from which key words and numerical information are extracted. The generated key words indicate characteristic elements and topics of the article, while the numerical information represents related data.

[0605] Next, the server generates supplementary information based on the extracted key words and numerical data. This additional information includes definitions of terms, background explanations, and statistical graphs and charts based on the data. This allows users to obtain supplementary information to gain a deeper understanding of the news article.

[0606] Furthermore, the server manages user history and interest information, personalizing additional information. Based on a user's past browsing history and areas of interest, content is personalized, optimizing the news experience. This personalized information provides each user with more relevant information and helps them understand news articles.

[0607] The terminal displays information resources and additional information received from the server on a user-friendly interface that is easily accessible to the user. This display is neatly arranged and in a user-friendly format. Users can select items of interest from the presented information and access detailed information to gain a deeper understanding of the news.

[0608] As a concrete example, suppose a user is reading a news article about "changes to the health insurance system." In this case, the server extracts key words such as "insurance premiums," "general practice," and "preventive medicine," and based on these, generates new policies and graphs based on past statistical data. Furthermore, for users with a high interest in health, additional information such as further research findings and opinions related to this field is provided. As a result, users can gain a deeper understanding than that of a mere news article.

[0609] The following describes the processing flow.

[0610] Step 1:

[0611] The server retrieves articles from news sources. This includes using web-based RSS feeds and APIs to automatically collect the latest information. The collected articles are temporarily stored in a database for subsequent processing.

[0612] Step 2:

[0613] The server uses a generation and processing unit to extract important words and numerical information from the retrieved articles. Natural language processing technology is used to identify keywords within the articles and identify related numerical data. This clarifies the article's theme and key points.

[0614] Step 3:

[0615] The server generates additional relevant information based on extracted key words and numerical data. A generative AI model is used to present definitions of terms, background information, and relevant statistical data. Graphs and charts are also created to facilitate visual understanding.

[0616] Step 4:

[0617] The server personalizes additional information based on the user's history and interests. This involves analyzing the user's past browsing history and preferences to provide the most relevant information to each individual user. Personalization is crucial for improving the relevance of the information.

[0618] Step 5:

[0619] The server sends packets containing the final news article and personalized additional information to the terminal. The transmitted data is compressed and encrypted to ensure the security of the communication.

[0620] Step 6:

[0621] The terminal decompresses the data received from the server and displays it on the user interface. The visual layout and navigation are optimized to allow users to easily access information of interest.

[0622] Step 7:

[0623] Users view the provided news articles and additional information, and search for more detailed information as needed. User selections and actions are logged and used to personalize future experiences.

[0624] (Example 1)

[0625] 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".

[0626] Conventional news distribution systems struggle to provide information tailored to individual user interests, resulting in problems such as insufficient or excessive information simply by distributing news. Furthermore, they lack background information and relationship diagrams necessary for a deeper understanding of the news, hindering user comprehension.

[0627] 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.

[0628] In this invention, the server includes means for acquiring data resources from news sources and extracting important words and numerical data using natural language processing techniques; means for generating relevant information based on the extracted words and numerical data; and means for personalizing the relevant information according to the user's history and interest data. This enables users to receive news that includes personalized background information and to understand it more deeply.

[0629] A "news source" is an external data source that provides articles and reports from multiple news providers and media organizations.

[0630] "Data resources" refer to the collection of information that makes up news articles and reports that have been gathered.

[0631] "Natural language processing technology" is a general term for algorithms and techniques that enable computers to understand and process human language.

[0632] "Key terms" are the main words or phrases that characterize the content of a news article.

[0633] "Numerical data" refers to statistical information and numerical data included within an article.

[0634] "Related information" refers to background explanations and supplementary materials generated based on extracted key keywords and numerical data.

[0635] "User history and interest data" refers to database information related to past browsing history and user interests.

[0636] "Personalization methods" refer to processes or technologies that customize information based on each user's interests and history.

[0637] A "data display device" is a device that provides an interface for visually presenting generated news information and related information to the user.

[0638] The news information provision system in this invention consists of three entities: a server, a terminal, and a user. In order to effectively implement the invention, the following specific devices and technologies are used.

[0639] First, the server collects data resources from multiple news sources. This collection process is carried out via Web APIs and RSS feeds, efficiently retrieving the latest news articles. The data resources are then stored in a database system, ready for further processing.

[0640] Next, the server uses generative AI models and natural language processing techniques to extract important words and numerical data. For this purpose, it implements algorithms such as tokenization, part-of-speech tagging, and named entity recognition. Specifically, it uses spaCy, an open-source natural language processing library, and AI model libraries.

[0641] Subsequently, the server generates related information based on the extracted data. This process accesses external knowledge bases and databases to supplement the information. Matplotlib and D3.js are used as data visualization tools with generative AI models to generate visual graphs and charts.

[0642] Furthermore, the server analyzes user history and interest data to personalize relevant information. Machine learning algorithms process the data and deliver a personalized news feed for each user.

[0643] The terminal displays relevant information obtained from the server on the user interface, making it easily accessible to the user. The display is arranged in an intuitive and easy-to-understand manner, and interactive elements are provided to allow users to explore the details of the information. Frontend frameworks such as Bootstrap and React are used in the terminal.

[0644] Users can browse news through the provided interface and obtain detailed information based on their interests.

[0645] As a concrete example, if a user views news about "changes to the health insurance system," the server extracts key terms such as "insurance premiums," "general practice," and "preventive medicine," and displays relevant information, including policy changes and statistical data, in graph form. An example of a prompt that might be used at this time is "Describe the key changes to the health insurance system and their impact in detail." Through this process, users can obtain information beyond mere news and gain a deeper understanding.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The server collects data resources from news sources. Input is requests via news APIs and RSS feeds, and output is the latest news article data. Specifically, the server periodically accesses news sources, checks for new information, and stores any uncollected data in the database.

[0649] Step 2:

[0650] The server analyzes data resources using natural language processing techniques to extract important words and numerical data. The input is news article data stored in a database, and the output is a list of important words and numbers that characterize the articles. The server organizes the data by tokenizing the articles through a generative AI model and performing part-of-speech tagging and named entity recognition.

[0651] Step 3:

[0652] The server generates relevant information based on extracted keywords and numerical data. The input consists of key keywords and numerical data, while the output includes background explanations and statistical graphs as relevant information. Specifically, the server retrieves supplementary information from an external knowledge base and uses Tableau or D3.js to perform visualizations based on the data.

[0653] Step 4:

[0654] The server personalizes relevant information based on the user's history and interest data. Input is the user's past browsing history and current interests, and output is personalized news content. The server uses machine learning algorithms to analyze the user profile and select the most relevant information.

[0655] Step 5:

[0656] The terminal displays relevant information received from the server on the user interface. Input is personalized news content, and output is a visual representation on the user interface. Specifically, the terminal uses a front-end development framework to organize information and provide interactive elements.

[0657] (Application Example 1)

[0658] 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".

[0659] In today's information society, where a vast amount of news information circulates, users need related information and background knowledge to deeply understand each news article. However, if this information is not provided appropriately, users face information overload, making it difficult to gain accurate understanding. Therefore, there is a need for visually and personalized information presentation that allows users to quickly grasp the importance and relevance of an article.

[0660] 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.

[0661] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, and means for presenting the content to the user in a visual form on a visual display device. This enables the user to visually understand important information related to news articles and to quickly and deeply grasp the context of the articles.

[0662] A "generation processing device" is a computing device that extracts important elements from information resources and generates related additional information.

[0663] "Key words" are words that indicate the main topic or characteristics of information resources such as news articles.

[0664] "Numerical information" refers to quantitative data and numerical values ​​contained in information resources.

[0665] "Additional information" refers to related information and explanations generated based on important words and numerical data, which help users understand the information more deeply.

[0666] A "visual display device" is a device that allows users to visually confirm information, such as a smartphone or a head-mounted display.

[0667] "User history information" refers to data about what kind of information a user has viewed or interacted with in the past.

[0668] "Interest information" refers to data that shows what topics or themes a particular user is interested in.

[0669] Personalization refers to the process of adapting information and services provided based on each user's history and interests.

[0670] This invention realizes a system that provides users with relevant and important information in an easy-to-understand visual format when they view news articles. The system mainly operates with three components: a server, a terminal, and a user.

[0671] The server first acquires news data from multiple sources. This data is analyzed using a generative processing unit and natural language processing models (such as BERT or GPT-3) to extract important words and numerical information. The server then uses this extracted information to generate additional explanatory information and data for visual display. This process is performed by a generative AI model. Furthermore, the generated information is personalized based on each user's history and interests.

[0672] The device displays information received from the server through a user interface. The device can be a smartphone or a head-mounted display (HMD), through which the user views the information. The information presented on the visual display device includes in-depth explanations related to important words and data that the user finds interesting. This allows the user to quickly understand the information in the article and obtain relevant context.

[0673] As a concrete example, when a user reads an article about new environmental policies, the server extracts key terms such as "greenhouse gases" and "renewable energy," and generates graphs and explanatory content based on these terms. This visual information is provided to the user via an HMD (Head-Mounted Display) to facilitate their understanding.

[0674] A concrete example of a prompt for a generative AI model is: "Please analyze the news article on recent environmental policies and list key concepts and relevant data for visualization." Using this prompt, the AI ​​identifies important elements of the article and generates visual information.

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The server collects data from news sources. In this step, the server uses web scraping techniques to collect the latest articles from multiple news sites. Inputs include web URLs and API keys, and output is raw news article data.

[0678] Step 2:

[0679] The server uses a generation and processing unit to begin analyzing raw news article data. During this process, a natural language processing model (e.g., GPT-3) is used to extract important words and numerical information. The input is the collected news article data, and the output is the analyzed important words and numerical information. The model understands the content of the text and selects the most relevant words.

[0680] Step 3:

[0681] The server uses a generative AI model based on the extracted information to generate highly relevant additional information. In this step, it generates relevant explanatory text and visual data (graphs and statistics) based on the extracted words and numbers. The input requires the extracted words and numerical information, and the output is the generated additional information. This generation process is instructed to the AI ​​via prompts. The prompt used is "Please analyze the key concepts and produce descriptive content and visualizations for better understanding."

[0682] Step 4:

[0683] The server references the user's history and interests to personalize additional information. Database access is performed to retrieve past browsing history and topics of interest, and the information is then tailored based on this data. The input is the user's profile data, and the output is personalized additional information.

[0684] Step 5:

[0685] The device receives personalized information sent from the server and displays it to the user. At this stage, a visual display device is used to display the generated content in an easily understandable format. The input is display data from the server, and the output is visual information that the user accesses visually. The user then looks at this and takes action to gain a deeper understanding of the article.

[0686] 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.

[0687] This invention relates to a news information system that incorporates an emotion engine to improve the quality of information delivery. This system is centered around a server, terminals, and users, and has the function of recognizing the user's emotions and optimizing the content of the news displayed.

[0688] The server first collects news articles via the network. These collected information resources are then processed through a generation and processing unit. Key words and numerical information are extracted from the articles, and related additional information is created by a generation AI model.

[0689] Next, an emotion engine is incorporated to analyze the user's emotions. It analyzes interaction data such as text input and clicks performed by the user through the device, recognizing a variety of emotions such as joy, surprise, and sadness in real time. The recognized emotion data is managed on a server.

[0690] Based on the analysis results, the server customizes additional information in news articles to match the user's current emotional state. For example, it provides more hopeful news and positive information to depressed users, and adjusts the display to show more detailed data analysis and visual graphs to encourage calmness in excited users.

[0691] The device displays news articles customized to the user based on their emotions. The user interface is personalized, and different layouts and color schemes may be selected depending on the user's emotional state. Users can use the provided information in conjunction with their own emotions to gain a deeper understanding of the news.

[0692] For example, when a user is browsing news about an economic crisis, if the emotion engine detects the user's anxiety, the server will prioritize displaying information about economic measures and signs of recovery. Furthermore, in such situations, suggestions for listening music or distracting entertainment content may also be presented to help maintain composure. This system allows users not only to obtain news but also to enjoy information while gaining emotional reassurance.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The server collects the latest articles from multiple news sources. This is done using APIs and RSS feeds to automatically retrieve the latest articles. The collected articles are temporarily stored in a database.

[0696] Step 2:

[0697] The server uses a generation and processing unit to extract important words and numerical information from stored articles. Natural language processing techniques are used here to identify keywords and themes in the articles.

[0698] Step 3:

[0699] Based on the key words and numerical information extracted by the server, an AI model is used to create additional information. This additional information includes definitions of terms, background information, and statistical graphs.

[0700] Step 4:

[0701] The server uses an emotion engine to analyze interaction data received from the user's device. It analyzes the text and behavioral data entered by the user to identify their current emotional state.

[0702] Step 5:

[0703] The server personalizes additional information in news articles based on the emotions it recognizes. For example, it provides reassuring content to anxious users and detailed data to help agitated users calm down.

[0704] Step 6:

[0705] The server packets the final news article and any customized additional information and sends it to the terminal. At this point, the data is encrypted to ensure the security of the communication.

[0706] Step 7:

[0707] The device decompresses data received from the server and displays it on the user interface. The layout and color scheme are designed to be emotionally resonant, allowing users to intuitively understand the information.

[0708] Step 8:

[0709] Users view the presented news articles and related information, and research further details as needed. By providing information that responds to the user's emotions, it becomes possible to process news with a deeper understanding and while maintaining emotional stability.

[0710] (Example 2)

[0711] 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".

[0712] In modern society, information overload makes it difficult for users to efficiently acquire only the information they need. Furthermore, users often experience psychological burden when receiving information, and there is a demand for information tailored to their emotional state. However, conventional systems are insufficient in providing information that considers user emotions, resulting in limitations in improving user satisfaction. Therefore, this invention aims to improve the quality of information provision by enabling the optimal provision of necessary and positive information while considering user emotions.

[0713] 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.

[0714] In this invention, the server includes means for extracting important terms and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important terms and numerical information, and means for generating emotional data using an emotional analysis engine that analyzes the user's emotional state. As a result, information is tailored to the user's emotional state, information provision is more personalized, and information reception becomes beneficial and stress-free for the user.

[0715] A "generation and processing device" is a device that acquires information resources, extracts important terms and numerical information from them, and generates additional information as needed.

[0716] "Information resources" refer to news articles and other related data obtained from the internet, and are basic data used in information provision systems.

[0717] "Key terms" are words extracted from information resources that are considered most important for understanding the subject matter of an article or data.

[0718] "Numerical information" refers to statistical or quantitative numerical data contained within information resources, and is an essential element for understanding and analyzing information.

[0719] An "emotion analysis engine" is software or a system that analyzes a user's text input and behavioral data to determine the user's emotional state in real time.

[0720] "Emotional data" refers to data representing the user's emotional state, generated by an emotion analysis engine, and is used for selecting and displaying information.

[0721] A "user interface" refers to the visual elements that allow a user to interact with an information system, including display elements such as screen layout and color scheme displayed on a terminal.

[0722] A "data storage device" is a hardware or software component used to store a user's history information, interest information, and emotional data.

[0723] A "glossary" is information that provides explanations of specialized or important terms used within an information resource.

[0724] "Visual display format" refers to a means of displaying information resources and additional information in a visual format such as graphs and charts, and is a means of providing information that appeals to users visually.

[0725] The embodiments for carrying out the present invention are described below. This system is operated around three parties: a server, a terminal, and a user. The server efficiently collects news articles from the internet. The collected information resources are processed by a generation processing device, and important terms and numerical information are extracted using natural language processing technology. This process may use NLTK or spaCy as programming libraries. The generation AI model uses algorithms developed by artificial intelligence providers such as OpenAI to generate relevant information based on prompt text.

[0726] Next, the device collects user interaction data (text input, click data, etc.). This data is sent to a server, where a sentiment analysis engine analyzes the user's emotions. This analysis often utilizes machine learning frameworks such as TensorFlow or PyTorch. Based on the sentiment data, the server adjusts and customizes the content of news articles and additional information to provide the user with the most relevant information.

[0727] Customized information is displayed to the user through their device. The user interface visually changes according to the user's emotional state and may include visualized data, additional listening music, and entertainment content. This allows the user to have a pleasant browsing experience with relevant information in real time.

[0728] For example, when a user is viewing news about an economic crisis, if the emotion engine identifies anxiety, the server will highlight information indicating signs of economic recovery, providing a more stable perspective. Another example of a practical prompt is, "Please generate the most relevant news articles based on my current emotional state." In this way, the system provides information optimized for each individual user.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The server retrieves news articles from the internet. The input is a pre-configured URL of a news source or feed URL. Specifically, it sends an HTTP request using a programming library (e.g., Python's requests or Scrapy) and retrieves the resulting HTML data. The output is raw HTML data.

[0732] Step 2:

[0733] The server analyzes the HTML data of the acquired news articles and extracts important terms and numerical information. The input is the HTML data obtained in step 1. Specifically, it uses natural language processing libraries (e.g., NLTK or spaCy) to split and tokenize the text data and extract important words and numbers. As a result, a list of important terms and numerical information are output.

[0734] Step 3:

[0735] The server uses a generative AI model to generate additional information based on the extracted key terms and numerical data. The input consists of the key term list and numerical data obtained in step 2. Specifically, a prompt (e.g., "Please provide background information for this article.") is sent to the generative AI model to generate relevant information. This output is the generated additional information.

[0736] Step 4:

[0737] The device collects user interaction data (e.g., keyboard input and click information) and sends it to the server. Input is the user's actions. Specifically, this data is obtained in real time via a JavaScript tracking script. The output of this process is the user interaction data.

[0738] Step 5:

[0739] The server analyzes the user's emotions using interaction data received from the terminal. The input is the user interaction data obtained in step 4. Specifically, it uses an emotion analysis engine and a machine learning framework (e.g., TensorFlow or PyTorch) to identify the user's emotional state. The output is emotion data.

[0740] Step 6:

[0741] The server customizes news articles by adjusting the content and additional information based on the generated sentiment data. The input consists of the additional information generated in step 3 and the sentiment data identified in step 5. Specifically, it selects information based on user sentiment and adjusts the display order and content via database queries and a content management system. The output is the customized news content.

[0742] Step 7:

[0743] The device displays customized news content to the user. The input is the customized content obtained in step 6. Specifically, dynamic page rendering techniques using CSS and JavaScript are used to display the information on the screen. As a result, the user can view news and visual content that suits their mood.

[0744] (Application Example 2)

[0745] 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".

[0746] In modern society, the overwhelming amount of information is a major source of stress for people. Furthermore, providing appropriate information based on individual user emotions is currently difficult. While there is a demand for personalized information that resonates with users' emotions when they access news and other information, systems that effectively achieve this are not yet widely available.

[0747] 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.

[0748] In this invention, the server includes means for extracting important words and numerical information from information resources acquired by a generation processing device, means for generating additional information related to the extracted important words and numerical information using the generation processing device, means for analyzing the user's emotional input to acquire emotional information, means for customizing the additional information based on the emotional information, means for displaying the customized additional information and information resources on a terminal device, and means for changing the display layout of the user interface according to the user's emotional state. This makes it possible to provide optimal news information based on the individual emotions of each user.

[0749] A "generation processing device" is a device that extracts important words and numerical information from information resources and generates related additional information.

[0750] "Information resources" refers to all data and content that are analyzed and processed by generation and processing equipment.

[0751] "Key words" are words extracted from information resources that have particular significance in analysis and information provision.

[0752] "Numerical information" refers to data extracted from information resources and expressed as numerical values.

[0753] "Emotional input" refers to input data that indicates the user's emotions and is used for emotion analysis.

[0754] "Emotional information" refers to data that indicates a specific emotional state, which is analyzed and obtained from emotional input.

[0755] "Additional information" refers to supplementary data and information related to important words and numerical information, generated by the generation and processing unit.

[0756] "Customization" refers to the act of adjusting additional information and the user interface based on the emotional information of individual users.

[0757] A "terminal device" is an electronic device used to display customized information to users.

[0758] "User interface" is a general term for screens and operating methods that enable the exchange of information between a user and a terminal device.

[0759] "Layout" refers to the structure, color scheme, and arrangement of design elements within a user interface.

[0760] The server first collects news and information resources via a network connected to a generation processing unit. It extracts important words and numerical information from these resources and generates related additional information using a generation AI model. Natural language processing and sentiment analysis are performed using Python and TensorFlow. In particular, sentiment information is analyzed based on sentiment input obtained through the user's sentiment-based interface. This allows the server to obtain the user's emotional state in real time.

[0761] The device dynamically customizes the user interface based on emotional information provided by the server. This ensures that customized news and information are displayed in a way that is best suited to the user. For example, when a user is feeling down, the device might suggest relaxing music and prioritize displaying positive news. Smart glasses and other mobile devices are often used in this information delivery process. These devices include cameras and sensors and are equipped with the ability to collect user interaction data as emotional input.

[0762] On the device the user normally uses, a generative AI model is used to suggest recommendations and entertainment content that are tailored to the user's emotional state. An example of a prompt used here is, "If the user's emotional state is determined to be stressed, display articles and related content that can help them relax."

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server collects news and information resources via the network. It takes text information from various websites and news feeds as input, and outputs it as formatted data for analysis by a data generation and processing unit.

[0766] Step 2:

[0767] The server uses a generation and processing unit to extract important words and numerical information from the acquired information resources. This process utilizes natural language processing techniques to tokenize and tag parts of speech, and analyzes each part of the information resource. The extracted important words and numerical information are then output.

[0768] Step 3:

[0769] The server uses a generative AI model to generate additional relevant information based on the extracted key words and numerical data. Here, a pre-trained model determines the context and relevance of the information and generates textual and visual data. The input to this process is the data obtained in step 2, and the output is the generated additional information.

[0770] Step 4:

[0771] Users input emotions through their devices. Here, an emotion analysis engine acquires emotional information based on data obtained from user interactions such as text input and clicks. The input is user interaction data, and the output is emotional information.

[0772] Step 5:

[0773] The server customizes additional relevant information based on the acquired sentiment information. The sentiment information is used as a prompt to instruct the generative AI model on the optimal direction for providing information. In this step, the sentiment information and the additional information generated in step 3 are used as input, and the customized information set is output.

[0774] Step 6:

[0775] The terminal displays customized information received from the server on the user interface. The terminal presents additional information with a layout adapted to the user's emotions, delivering it to the user in a visually easy-to-understand format. The input for this step is a customized set of information, and the output is the final display to the user.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] A means for extracting important words and numerical information from information resources acquired by a generation processing device,

[0800] A means for generating additional information related to extracted important words and numerical information using a generation processing device,

[0801] Means for adjusting additional information based on the user's history and interests,

[0802] Means for displaying adjusted additional information and information resources on a terminal device,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, characterized in that historical information and interest information are recorded in a data storage device and used to adjust additional information for subsequent visits.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that the generated additional information includes a glossary and a visual display format.

[0808] "Example 1"

[0809] (Claim 1)

[0810] A means for obtaining data resources from news sources and extracting important words and numerical data using natural language processing technology,

[0811] A means for generating related information based on extracted words and numerical data,

[0812] A means of personalizing relevant information according to the user's history and interest data,

[0813] Means for displaying individualized related information and data resources on a data display device,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, characterized in that user history and interest data are stored on a storage medium and applied to subsequent personalization of related information.

[0817] (Claim 3)

[0818] The system according to claim 1, characterized in that the generated related information includes a glossary of terms and a graphic display format.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means for extracting important words and numerical information from information resources acquired by a generation processing device,

[0822] A means for generating additional information related to extracted important words and numerical information using a generation processing device,

[0823] A means of presenting content to users in a visual form on a visual display device,

[0824] Means for adjusting additional information based on the user's history and interests,

[0825] Means for displaying adjusted additional information and information resources on a terminal device,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, characterized in that historical information and interest information are recorded in a data storage device and used to adjust additional information for subsequent visits.

[0829] (Claim 3)

[0830] The system according to claim 1, characterized in that the generated additional information includes a glossary and a visual display format, and the system provides relevant information to the user through a display device.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means for extracting important terms and numerical information from information resources acquired by a generation and processing device,

[0834] Means for generating additional information related to extracted key terms and numerical information using a generation processing device,

[0835] A means for generating emotional data using an emotional analysis engine that analyzes the emotional state of users,

[0836] A means of adjusting additional information based on the generated emotional data,

[0837] A means for displaying adjusted additional information and information resources on a terminal device and dynamically providing a user interface that responds to the user's emotions,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, characterized in that historical information, interest information, and sentiment data are recorded in a data storage device and used to adjust additional information for subsequent visits.

[0841] (Claim 3)

[0842] The system according to claim 1, characterized in that the generated additional information includes a glossary of terms and a visual display format, and includes recommendations for music and entertainment content tailored to the user's emotional state.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means for extracting important words and numerical information from information resources acquired by a generation processing device,

[0846] A means for generating additional information related to extracted important words and numerical information using a generation processing device,

[0847] A means of obtaining emotional information by analyzing the emotional input of users,

[0848] A means of customizing additional information based on emotional information,

[0849] Means for displaying customized additional information and information resources on a terminal device,

[0850] A means of changing the display layout of the user interface according to the user's emotional state,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, characterized in that historical information and emotional information are recorded in a data storage device and used for customizing additional information in subsequent instances.

[0854] (Claim 3)

[0855] The system according to claim 1, characterized in that the generated additional information includes a glossary of terms, a visual display format, and suggestions for entertainment content related to emotional states. [Explanation of Symbols]

[0856] 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 extracting important words and numerical information from information resources acquired by a generation processing device, A means for generating additional information related to extracted important words and numerical information using a generation processing device, Means for adjusting additional information based on the user's history and interests, Means for displaying adjusted additional information and information resources on a terminal device, A system that includes this.

2. The system according to claim 1, characterized in that historical information and interest information are recorded in a data storage device and used to adjust additional information for subsequent visits.

3. The system according to claim 1, characterized in that the generated additional information includes a glossary of terms and a visual display format.

Images