System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting news information; means for summarizing the collected news information using generative artificial intelligence; means for customizing the summarized news information based on a user's interests; and means for delivering the customized news summary to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In today's information society, a huge amount of news and information is circulating, making it difficult for users to determine which information is important. As a result, users become confused by the information overload and may miss important news. It is also difficult for users to efficiently obtain news that matches their interests. The present invention aims to solve these problems and provide a system that allows users to efficiently obtain the information they need. [Means for solving the problem]

[0005] The present invention provides a system including means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on user interests, and means for delivering the customized news summaries to users. The system can acquire news articles from multiple news sources via a network and store them in a database. Furthermore, the generative artificial intelligence extracts important points from the collected news information to generate summaries, which are then delivered in a form customized based on the user's interests. This allows users to efficiently obtain important news.

[0006] "News information" refers to the latest news articles and reports obtained from news sites, RSS feeds, online media, etc.

[0007] A "means" is a method, device, or process used to accomplish a particular purpose.

[0008] "Generative AI" is a type of artificial intelligence technology that refers to systems or algorithms that have the ability to generate new information based on specific input information.

[0009] "Summarizing" refers to extracting the key points of the original information and presenting them in a concise form.

[0010] "User" means a human or end user of this system.

[0011] "Customization" means tailoring and optimizing information and services to suit the preferences and needs of individual users.

[0012] "Distribution" is the procedure or process of delivering information or content to a specific user.

[0013] A "network" is a communications infrastructure that connects multiple computers and devices and enables them to send and receive data.

[0014] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0015] "News sources" refers to websites, RSS feeds, media platforms, etc. that provide news information. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0038] News information gathering module

[0039] News article collection

[0040] The server collects the latest news articles from news sites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[0041] Specific examples

[0042] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[0043] Generative AI summary module

[0044] News Summary

[0045] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0046] Specific examples

[0047] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[0048] User Management Module

[0049] User information management and analysis

[0050] The server stores users' registration information and interest categories in a database, and then analyzes their browsing history to identify news categories that interest each individual user.

[0051] Specific examples

[0052] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[0053] Summary Delivery Module

[0054] Customize and deliver summaries

[0055] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notifications.

[0056] Specific examples

[0057] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0058] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI system creates summaries. By delivering summaries customized based on the user's interests, the system prevents confusion caused by information overload and enables efficient information acquisition.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The server schedules news gathering tasks, for example setting up a task to gather news articles from news sites and RSS feeds every morning at 9am.

[0062] Step 2:

[0063] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[0064] Step 3:

[0065] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[0066] Step 4:

[0067] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[0068] Step 5:

[0069] The server schedules generative AI summarization tasks, for example, setting up a task to summarize news articles collected over the last 24 hours every morning at 10:00.

[0070] Step 6:

[0071] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[0072] Step 7:

[0073] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[0074] Step 8:

[0075] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[0076] Step 9:

[0077] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[0078] Step 10:

[0079] The server customizes summaries based on the user's interests, using each user's profile information to select the most relevant news summaries.

[0080] Step 11:

[0081] The server schedules summary delivery tasks, for example, setting up a task to deliver a customized summary to users every morning at 9am.

[0082] Step 12:

[0083] The server sends a push notification to the user's device at the specified time, containing a link to a customized news summary for easy user access.

[0084] Step 13:

[0085] A user receives a push notification and clicks on a link, which takes them to a customized news summary.

[0086] Step 14:

[0087] The server updates the user's browsing history, recording which news summaries the user has viewed and reflecting this in the next summary customization.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] In modern society, a huge amount of news information is generated every day, and there is a demand for quickly and efficiently extracting and acquiring important information from it. Conventional news gathering systems can easily cause confusion due to information overload and overlook important points. Another problem is that it is difficult for users to easily acquire news information customized based on their own interests. There is a need for a system that can solve these issues and enable users to efficiently understand important news.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on user interests, and means for delivering the customized news summaries to users, thereby enabling efficient collection and summarization of news information, customization based on user interests, and delivery of highly relevant news.

[0093] "Means for collecting news information" refers to a system for obtaining news articles from multiple information sources via a network and storing these articles in a database.

[0094] "Means for summarizing collected news information using generative artificial intelligence" refers to a system that extracts important points from collected news information, creates a summary based on these points, and saves the summary back in a database.

[0095] "Means for customizing summarized news information based on a user's interests" refers to a mechanism that identifies the news summaries most relevant to a user's interests based on the user's registration information, categories of interest, and browsing history.

[0096] "Means for delivering customized news summaries to users" refers to a mechanism for selecting customized news summaries according to a daily, weekly, or monthly schedule and delivering them to users' devices via push notifications.

[0097] "Multiple sources" refers to multiple online sources of news articles and information, such as news sites and RSS feeds.

[0098] "Database" refers to a digital information management system for organizing and storing collected news information, generated summaries, user information, etc.

[0099] "Generative AI" refers to artificial intelligence models (e.g., GPT-3) that are used to analyze news articles, extract key points, and generate summaries.

[0100] "User Registration Information" refers to information such as personal information and interest categories provided by a User when registering with the System.

[0101] "Viewing history" refers to historical data of news articles that a user has accessed within the system.

[0102] "Relevant news" refers to the news information that is most valuable to a user based on the user's interests and past browsing history.

[0103] "Push notification" refers to a communication method for sending information in real time from a server to a user's device.

[0104] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0105] News information gathering module

[0106] Hardware and Software Configuration

[0107] The server collects the latest news articles from multiple sources, such as news websites and RSS feeds. The server is connected to the Internet via a network interface. The collection task is run every morning at 9:00 a.m. using scheduling software (e.g., cron).

[0108] Specific operation example

[0109] Every morning at 9:00, the server sends an HTTP request to the news site's API to retrieve the latest article data. It then parses the retrieved data to extract the title, body text, and publication date and time of each article, and stores them in a database management system (e.g., MySQL).

[0110] Generative AI summary module

[0111] Hardware and Software Configuration

[0112] The server retrieves news articles from the database and passes them to a generative AI (e.g., GPT-3), which analyzes each article, extracts key points, and generates a summary.

[0113] Specific operation example

[0114] A server retrieves the 10 latest news articles from a database at 10 AM and passes them to a generative artificial intelligence using the following prompt:

[0115] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0116] Article Title: (Article Title)

[0117] Article body: (Article body)

[0118] Generative artificial intelligence summarizes each article and stores the generated summaries in a database.

[0119] User Management Module

[0120] Hardware and Software Configuration

[0121] The server stores user registration information and interest categories in a database. Furthermore, the server analyzes users' browsing history to identify news categories that each user is interested in. This analysis is performed using a data analysis library (e.g., pandas).

[0122] Specific operation example

[0123] When a user registers, they enter their email address and the categories they are interested in. The server stores this information in a user table, and also tracks the user's browsing history and periodically updates the news categories they are interested in.

[0124] Summary Delivery Module

[0125] Hardware and Software Configuration

[0126] The server delivers news summaries according to a set schedule, such as daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and delivers customized summaries to the user's device via push notifications. Push notifications are delivered using a notification service (e.g., Firebase Cloud Messaging).

[0127] Specific operation example

[0128] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0129] In this way, the server efficiently collects news information, and the generative AI creates summaries. By delivering summaries customized based on the user's interests, confusion caused by information overload is prevented, enabling efficient information acquisition.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] A server schedules news gathering tasks.

[0133] Input: Task Scheduler configuration information

[0134] Data processing / calculation: Set the task to start at a specified time (9:00 every morning).

[0135] Output: The state when the schedule is completed and the task is automatically executed at the specified time.

[0136] What it does: The server uses cron to set up a news gathering task to run every morning at 9am.

[0137] Step 2:

[0138] The server sends an HTTP request to the specified news site to retrieve article data.

[0139] Input: News site API endpoint

[0140] Data processing / calculation: Sending HTTP requests and receiving responses.

[0141] Output: Latest news article data

[0142] Specific operation: The server accesses a news site such as news-example.com, sends an API request, and receives the news data returned as a response.

[0143] Step 3:

[0144] The server analyzes the received news data and stores it in a database.

[0145] Input: News article data

[0146] Data processing / calculation: Parsing data and extracting fields (title, body, publication date).

[0147] Output: News articles stored in a database

[0148] What it does: The server parses the data using Python's BeautifulSoup, extracts the article title, body text, and publication date and time, and stores them in a MySQL database.

[0149] Step 4:

[0150] The server retrieves the latest news from the database and passes it on to the generative artificial intelligence.

[0151] Input: News articles from the database

[0152] Data processing / calculation: Select the latest news articles using SQL queries.

[0153] Output: News article passed to the generative AI

[0154] What it does: The server uses an SQL query to retrieve the 10 latest news articles from the database and passes them to the generative artificial intelligence (GPT-3).

[0155] Step 5:

[0156] Generative artificial intelligence analyzes news articles, generates summaries, and returns them to the server.

[0157] Input: News article data and prompt

[0158] Data processing / calculation: News article summary generation

[0159] Output: Generated news summary

[0160] Example prompt:

[0161] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0162] Article Title: (Article Title)

[0163] Article body: (Article body)

[0164] Specific operation: The server sends a news article and a prompt to the generative AI, which then generates a summary and replies.

[0165] Step 6:

[0166] The server stores the generated summaries in a database.

[0167] Input: News summaries generated by generative artificial intelligence

[0168] Data processing / calculation: Summary data storage processing

[0169] Output: News summaries stored in a database

[0170] Specific behavior: The server stores the generated news summaries in a database.

[0171] Step 7:

[0172] The user enters registration information and interest categories from the device.

[0173] Input: User registration information and interest categories

[0174] Data processing / calculation: Validating and saving input data

[0175] Output: User profile data

[0176] Specific operation: The user enters their email address and interest categories on the device's registration screen, which is then received by the server and stored in a database.

[0177] Step 8:

[0178] The server analyzes the user's browsing history and identifies news categories of interest.

[0179] Input: User's browsing history

[0180] Data processing / calculation: Analysis of browsing history and category identification

[0181] Output: Updated user profile

[0182] What it does: The server uses Python's pandas to analyze browsing history, identify news categories of interest, and update the user profile.

[0183] Step 9:

[0184] The server selects customized news summaries and delivers them to users via push notifications.

[0185] Input: User profile and news summary

[0186] Data processing / computation: Selecting news summaries and generating notifications based on user interests

[0187] Output: Push notification sent to user device

[0188] How it works: The server uses Firebase Cloud Messaging to generate a URL link for a news summary customized based on the user's profile and sends it to the user's device via a push notification.

[0189] (Application example 1)

[0190] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] In today's world, the problem of information overload is becoming more serious, making it difficult to efficiently obtain important information from the vast amount of news available. Furthermore, there is a lack of efficient ways to collect and organize only the news that interests users. Meanwhile, there is a demand for improving the user experience by delivering relevant news summaries in a timely manner.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0193] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, and means for delivering the customized news summary to the terminal by push notification, thereby enabling highly relevant news information based on the user's interests to be efficiently summarized, further customized, and delivered in a timely manner.

[0194] "News information" refers to information such as articles, reports, commentaries, and special features that contain general news content.

[0195] "Generative artificial intelligence" is a type of artificial intelligence designed to understand, analyze, and summarize collected data.

[0196] A "summary" is a short summary of important points extracted from news information.

[0197] "User interests" refer to the specific topics or categories in which a user is interested.

[0198] "Customization" is the process of adapting or modifying content to suit the needs and interests of a particular user.

[0199] "Device" refers to a mobile device such as a smartphone or tablet.

[0200] "Push notification" is a function that sends information from a server to a device in real time.

[0201] A "server" is a computer system that processes data over a network and provides services to other devices and users.

[0202] "Means" are the methods or tools used to achieve a particular goal.

[0203] The present invention provides a system that uses generative artificial intelligence to summarize news information and delivers customized news summaries based on the user's interests to a terminal via push notification. The following components are included as means necessary to implement the present invention.

[0204] System Components

[0205] 1. How to gather news information:

[0206] The server retrieves articles from multiple news sources over a network and stores these articles in a database. The server accesses the news sources at designated times to collect the latest articles.

[0207] 2. Generative AI Summarization:

[0208] The server retrieves the collected news articles from the database and passes them to a generative AI (e.g., GPT-4), which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0209] 3. Customization based on user interests:

[0210] The server stores users' registration information and interest categories in a database, and analyzes their browsing history to identify the news categories that each user is interested in. Based on this information, the generated news summaries are customized.

[0211] 4. Push notification delivery method:

[0212] The server delivers customized news summaries to devices via push notifications based on a daily, weekly, or monthly schedule. For example, the server executes a summary delivery task every morning at 9:00, selects news summaries based on each user's profile, and sends them to devices via push notifications.

[0213] Hardware and Software

[0214] Hardware:

[0215] Server: A central system for collecting and processing data from multiple news sources.

[0216] Device: A mobile device such as a smartphone or tablet.

[0217] software:

[0218] Database: A database system such as MySQL or PostgreSQL.

[0219] Generative AI models: Artificial intelligence models for news article summarization, such as GPT-4.

[0220] Server program: Software that implements the functions of data collection, summary generation, customization, and push notification delivery.

[0221] Push notification services: Third-party push notification service providers.

[0222] Specific examples of processing

[0223] The server accesses news sources every morning at 9 a.m. and retrieves the latest articles via API. It then stores this data in a database and passes it to a generative AI, which summarizes each article and stores the summaries in a database. The server then selects the most relevant news summaries based on the user's interests and sends them to the user's smartphone using a push notification service.

[0224] Examples of prompt statements

[0225] The following prompt sentences are input into a generative artificial intelligence to summarize a news article.

[0226] Prompt statement:

[0227] "Summarize the following news article. Please use a maximum of 50 characters, a minimum of 25, and include the key points. Article content: {News article text}"

[0228] The above is a specific embodiment of the present invention. This system efficiently delivers news summaries based on the user's interests, preventing confusion caused by information overload and enabling efficient information acquisition.

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] The server accesses the news source every morning at 9:00 to retrieve the latest news articles. The input is the news source's API endpoint, and the output is the retrieved news article data. Specifically, the server sends an API request and receives a response. The received response data is analyzed to extract the article title, text, publication date, etc.

[0232] Step 2:

[0233] The server stores the extracted news article data in a database. The input is information such as the news article title, body text, and publication date, and the output is a news article entry stored in the database. Specifically, the information for each article is added as a new record in the "News Articles" table.

[0234] Step 3:

[0235] The server retrieves the latest news articles from the database at 10:00 AM and passes them to the generative AI. The input is multiple news article data retrieved from the database, and the output is a prompt to be input to the generative AI. Specifically, the text of each news article is used to generate the following prompt: "Please summarize the following news article. Please summarize in a maximum of 50 characters, a minimum of 25 characters, and include the key points. Article content: {News article text}"

[0236] Step 4:

[0237] A generative AI model summarizes a news article based on a prompt. The input is the prompt, and the output is a generated summary. Specifically, a generative AI model (e.g., GPT-4) analyzes the prompt and generates a summary. This generated summary is then stored in a database.

[0238] Step 5:

[0239] The server retrieves user information and interest categories from a database and customizes news summaries based on each user's interests. The input is the user's interest data and the generated news summaries, and the output is the customized news summaries. Specifically, it selects relevant news summaries based on each user's profile information and browsing history.

[0240] Step 6:

[0241] The server delivers customized news summaries to devices via push notifications. The input is the customized news summary and the user's device information, and the output is a push notification sent to the user's device. Specifically, a push notification service is used to notify each user of the selected news summary on their smartphone. Users can view detailed article content by tapping the notification.

[0242] The above are the specific processing steps of the system that realizes the application example. At each step, data processing and data calculation are performed based on the input data, thereby making it possible to obtain the required output.

[0243] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0244] This invention combines a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items on a daily, weekly, or monthly basis with an emotion engine that recognizes the user's emotions. This system delivers news summaries in a format that is appropriate for the user, enabling information organization and efficient information acquisition.

[0245] News information gathering module

[0246] News article collection

[0247] The server collects the latest news articles from news websites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[0248] Specific examples

[0249] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[0250] Generative AI summary module

[0251] News Summary

[0252] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0253] Specific examples

[0254] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[0255] User Management Module

[0256] User information management and analysis

[0257] The server stores users' registration information and interest categories in a database, and also analyzes users' browsing history to identify news categories that interest individual users.

[0258] Specific examples

[0259] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[0260] Emotion Engine Module

[0261] Emotion Analysis

[0262] The server receives user input data (text and voice) and passes it to the emotion engine, which analyzes the data and recognizes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[0263] Text-based sentiment analysis

[0264] When a user enters feedback on a news summary, the server passes the text data to an emotion engine for emotion analysis.

[0265] Specific examples

[0266] When a user enters feedback such as "This news was very interesting," the server passes the text to the emotion engine, which analyzes the text for positive emotions and updates the user profile.

[0267] Voice-based sentiment analysis

[0268] When a user comments on a news summary through a voice interface, the server passes the voice data to an emotion engine for emotion analysis.

[0269] Specific examples

[0270] When a user gives voice feedback such as "This news was very helpful," the server passes the voice data to the emotion engine, which analyzes the positive emotion and updates the user profile.

[0271] Summary Delivery Module

[0272] Customize and deliver summaries

[0273] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests and emotional data, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notification.

[0274] Specific examples

[0275] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0276] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI creates summaries. An emotion engine is used to analyze the user's emotions, and a more customized summary is delivered. This allows users to efficiently obtain important news.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] The server schedules the news gathering task: Set up a task that gathers news articles from news sites and RSS feeds every morning at 9am.

[0280] Step 2:

[0281] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[0282] Step 3:

[0283] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[0284] Step 4:

[0285] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[0286] Step 5:

[0287] The server schedules the generative AI summarization task. It sets up a task to pass the news articles collected in the last 24 hours to the generative AI every morning at 10:00.

[0288] Step 6:

[0289] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[0290] Step 7:

[0291] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[0292] Step 8:

[0293] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[0294] Step 9:

[0295] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[0296] Step 10:

[0297] The server creates customized news summaries based on the user profile, taking into account the user's browsing history and interest categories to select the most relevant news summaries.

[0298] Step 11:

[0299] The user provides feedback on the news summary, which can be in text or audio format.

[0300] Step 12:

[0301] The server obtains the user's feedback data, and receives the text data or voice data entered by the user.

[0302] Step 13:

[0303] The server passes the feedback data to the emotion engine, which analyzes the text and voice data to recognize the user's emotions.

[0304] Step 14:

[0305] The server saves the analysis results obtained from the emotion engine in the user profile, and updates the profile with the user's emotional tendencies based on the analyzed emotion data.

[0306] Step 15:

[0307] The server takes the emotion data into account the next time it delivers a news summary, and combines the user's emotion data with the interest data to create a more customized summary.

[0308] Step 16:

[0309] The server delivers news summaries according to a daily, weekly, or monthly schedule, and sends optimized summaries to users' devices via push notifications at specified times.

[0310] Step 17:

[0311] A user receives a push notification and clicks on a link to a news summary, which displays a customized news summary.

[0312] Step 18:

[0313] The server updates the user's browsing history, recording the information about the news summaries the user has viewed and reflecting it in future summary customizations.

[0314] Example 2

[0315] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] Conventional news distribution systems provide uniform news summaries without considering the user's interests or emotions, making it difficult to efficiently provide optimal information to individual users. Another issue is the time required to collect a large number of news articles and extract important information.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0318] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, means for recognizing the user's emotions and adjusting the summaries based on the emotions, and means for delivering customized news summaries to the user, thereby enabling efficient delivery of customized news summaries that reflect the user's interests and emotions.

[0319] A "means for collecting news information" is a means for obtaining news articles from multiple news sources via a network and storing the news articles in a database.

[0320] "Generative artificial intelligence" refers to advanced natural language processing models, such as GPT-4, that are used to summarize collected news information.

[0321] The "means for customizing summarized news information based on the user's interests" is a means for analyzing the user's registration information, browsing history, etc., and adjusting the news summary to suit the user's interests.

[0322] "Means for recognizing user emotions and tailoring summaries based on those emotions" refers to means for analyzing user input data (text or voice) to identify emotions and customizing news summaries based on that emotional data.

[0323] "Means for delivering customized news summaries to users" means means for generating news summaries that reflect the user's interests and emotions based on a specific schedule and delivering them to the user's device via push notification or other means.

[0324] The present invention is a system that uses generative artificial intelligence to summarize news information and provide users with customized news summaries. This system collects news information, creates summaries using generative artificial intelligence, and delivers summaries customized based on the user's interests and emotions, allowing users to efficiently obtain important news information.

[0325] News information gathering module

[0326] News article collection

[0327] The server accesses news sites and RSS feeds every morning at 9:00 to collect the latest news articles. The server uses the APIs of major news sites to obtain a list of the latest news articles, then analyzes the title, text, and publication date and time of the retrieved news articles, and stores this data in a database for later processing.

[0328] Specific examples

[0329] The server sends a request to the API endpoint of the "news source" to get a list of new articles. For example, if the article title is "Latest Technology Announcement," the body text is "New Technology...," and the publication date is "2023-10-01," it stores these in the database.

[0330] Generative AI summary module

[0331] News summary generation

[0332] At 10:00 AM, the server retrieves the latest news articles from the database and passes them to a generative AI (e.g., GPT-4) to generate summaries, which are then stored in the database.

[0333] Specific examples

[0334] The server retrieves a news article titled "Latest Technology Announcement" from the database at 10:00 AM. It then inputs the prompt "Please summarize the following news article: 'Latest Technology Announcement...'" into the generative AI model, and stores the resulting summary "New technology was announced" in the database.

[0335] User Management Module

[0336] User information management and analysis

[0337] The server receives the user's registration information, stores the user's interest categories in a database, and tracks the user's browsing history and analyzes it to identify the news categories that interest the user.

[0338] Specific examples

[0339] When a user fills in the registration form with "email address: example@example.com" and "interest category: technology", the server stores this information in the user table. After that, every time the user visits a news summary page, the server tracks the user's browsing history and analyzes their interest categories.

[0340] Emotion Engine Module

[0341] Emotion Analysis

[0342] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[0343] Text-based sentiment analysis example

[0344] If a user enters feedback such as "This news was very informative," the server passes the text data to the emotion engine, which analyzes the text for positive emotions and stores the results in the user profile.

[0345] Voice-based emotion analysis example

[0346] If a user provides voice feedback such as "This news was really helpful," the server passes the voice data to the emotion engine, which analyzes the voice, identifies positive emotions, and stores the results in the user profile.

[0347] Summary Delivery Module

[0348] Customize and deliver summaries

[0349] The server delivers customized news summaries to users on a daily, weekly, or monthly schedule, primarily via push notifications sent directly to their devices.

[0350] Specific examples

[0351] The server executes a summary delivery task every morning at 9:00 and retrieves all users from the user table. Based on User A's profile, which states "Interest: Technology, Emotion: Positive," the server selects a summary of the "Latest Technology Announcement" and generates it as a URL link. This link is then sent to User A's device via a push notification.

[0352] The above is a specific embodiment of the present invention. The server collects news information from multiple news sources, and the generative AI generates summaries. Furthermore, an emotion engine is used to analyze the user's emotions, and the news summaries are customized based on the analysis results, allowing users to efficiently obtain news that is important and relevant to them.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1:

[0355] The server accesses news sources (news sites and RSS feeds) every morning at 9:00 and retrieves a list of the latest news articles.

[0356] Input: News source URL or API endpoint

[0357] Output: A list of news articles

[0358] Specific behavior:

[0359] The server sends HTTP requests to pre-configured news source URLs or API endpoints, parses the news article data received in response, which is often provided in JSON or XML format, extracts the retrieved news articles (title, body text, publication date and time), and stores them in a database.

[0360] Step 2:

[0361] The server retrieves the latest news articles from the database at 10 a.m. and passes them to a generative artificial intelligence to generate summaries.

[0362] Input: News articles stored in a database

[0363] Output: A summarized news article

[0364] Specific behavior:

[0365] The server retrieves multiple (e.g., 10) recent news articles from the database. It then creates a prompt to summarize the content of each news article and passes it to a generative AI (e.g., GPT-4). The generative AI generates a summary of each article based on the prompt and stores the summary results back in the database. A prompt such as "Please summarize the following news article: 'The latest technology announcement...'" is used.

[0366] Step 3:

[0367] When a user registers or logs in, the server obtains the user's interests and category information and stores it in a database.

[0368] Input: User registration information and interest categories

[0369] Output: User information stored in the database

[0370] Specific behavior:

[0371] When a user fills in a registration form with their email address and interest categories and submits it, the server receives this information and stores it in a database. For example, the information "Email address: example@example.com" and "Interest categories: Technology" are stored in the user table. In addition, the server tracks the user's browsing history and periodically updates their interest categories.

[0372] Step 4:

[0373] The server passes the text and voice data entered by the user to the emotion engine and analyzes the user's emotions.

[0374] Input: User feedback text or voice data

[0375] Output: Emotion analysis results

[0376] Specific behavior:

[0377] When a user submits feedback on a news summary, they provide text or voice data. The server passes this text data (e.g., "This news was very interesting") or voice data to the emotion engine. The emotion engine analyzes the data, identifies emotions such as positive or negative, and stores the results in the user profile.

[0378] Step 5:

[0379] The server generates and delivers customized news summaries to user terminals according to a daily, weekly, or monthly schedule.

[0380] Input: User profile information (interests, emotional data), summarized news articles

[0381] Output: A customized news summary delivered to the user

[0382] Specific behavior:

[0383] The server runs a news summary delivery task at 9:00 every morning, retrieving all users from the user table. Based on each user's profile (e.g., "Interests: Technology, Sentiment: Positive"), it selects the most appropriate news summary. It generates a URL link based on this summary and sends it to the user's device as a push notification. The user receives the push notification on their device and can click the link to view the customized news summary.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] Conventional news delivery systems could customize and deliver news based on a user's interests, but they could not consider the user's emotions. As a result, news content may not be appropriate for the user, making it difficult to improve the user experience. Furthermore, there was a lack of user feedback to adjust summary generation. The present invention aims to solve these problems and efficiently provide news summaries customized based on a user's interests and emotions.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0388] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests and emotions, means for delivering the customized news summaries to the user, means for analyzing emotions from user feedback, and means for adjusting the news feed based on the analysis results, thereby enabling delivery of personalized news summaries tailored to the user's interests and emotions.

[0389] "Means for collecting news information" refers to the technology of obtaining the latest article data from multiple information sources via the Internet and storing it in a database.

[0390] "Means for summarizing collected news information using generative artificial intelligence" is an AI technology that analyzes collected news information, extracts important points, and generates summaries.

[0391] The "means for customizing summarized news information based on user interests and emotions" is a technology that adjusts news summaries taking into account the user's registration information, interest categories, and emotion analysis results.

[0392] The "means for delivering customized news summaries to users" refers to a technology for delivering customized news summaries to specific users via push notification, email, or the like.

[0393] "Means for analyzing emotions from user feedback" refers to a technology that analyzes text and voice feedback entered by the user and recognizes the user's emotions based on that.

[0394] "Means for adjusting news feeds based on analysis results" refers to technology that readjusts news feeds to make them more likely to interest users based on the results of sentiment analysis.

[0395] The present invention proposes a system for summarizing news information using generative artificial intelligence and providing a news summary customized according to a user's interests and emotions. Specific embodiments will be described in detail below.

[0396] News information gathering

[0397] The server periodically collects the latest news articles from multiple information sources (news websites, RSS feeds, etc.) via the Internet, and stores the collected news articles in a database for later processing.

[0398] Specific examples

[0399] For example, every morning at 9:00, the server accesses major news sites to retrieve the latest article data, then analyzes this data and stores the title, text, and publication date of each article in a database.

[0400] News summary generation

[0401] The server retrieves the collected news articles from the database and passes them to a generative artificial intelligence (e.g., the T5 model from the transformers library) to generate summaries, which are then stored back in the database.

[0402] Specific examples

[0403] For example, at 10:00 AM, the server retrieves the 10 latest news articles from the database and passes them to the GAI, which then summarizes each article and stores the summaries in the database.

[0404] User interest and sentiment analysis

[0405] The server takes the feedback (text and / or voice) provided by the user and passes it to an emotion engine (e.g., the TextBlob library) for sentiment analysis. The analysis results are stored in the user profile and used to customize news summaries. The server also stores the user's registration information and interest categories in a constantly updated database.

[0406] Specific examples

[0407] If a user enters feedback such as "This news was very interesting," the server passes the text to an emotion engine to analyze positive emotions and update the user profile.

[0408] Delivering customized news summaries

[0409] The server delivers customized news summaries to specific users via push notifications or emails, and delivery schedules can be set on a daily, weekly, or monthly basis, with the news summaries customized based on the user's interests and emotional data.

[0410] Specific examples

[0411] For example, every morning at 9:00 AM, the server retrieves all users and selects a customized news summary based on each user's profile. The generated summary is then sent to the user's device as a push notification in the form of a link.

[0412] Prompt Sentence Examples

[0413] Example prompt for a generative AI to generate a news summary:

[0414] Content: Economy

[0415] Summarize.

[0416] Input: The Nikkei Stock Average has risen significantly, reaching its highest level in the past 10 years.

[0417] Prompt: Summarize this content in 50 characters or less.

[0418] By the above means, the present invention can efficiently provide news summaries customized based on the user's interests and emotions, thereby improving the user experience and providing more personalized information.

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1:

[0421] To collect news information, the server accesses multiple information sources (news sites, RSS feeds, etc.) every morning at 9:00 and retrieves the latest article data. The input is a list of news site URLs, and the output is a database entry containing the article title, text, and publication date and time. Specifically, the server sends an HTTP request to retrieve the article data, analyzes it, and stores it in the database.

[0422] Step 2:

[0423] The server retrieves news articles collected from a database and passes them to a generative AI (for example, the T5 model from the Transformers library) to generate summaries. The input is the title and body of the news article retrieved from the database, and the output is a database entry containing the generated summary text. Specifically, the server sends the body of the news article to the generative AI and uses text such as "Summarize this content. The character limit is 50 characters or less" as a prompt for the AI ​​model.

[0424] Step 3:

[0425] The server customizes news summaries by taking into account the user's interests and emotions. The input is the user's registration information and emotion data obtained from their feedback, and the output is a customized news summary. Specifically, the server analyzes the user's profile data and selects the most suitable content for the user from the generated summaries.

[0426] Step 4:

[0427] The server delivers customized news summaries to the user's device. The inputs are the customized news summaries and the user's device information, and the output is the news summaries sent via push notification or email. Specifically, the server sends the summaries to the user's device via push notification or email server.

[0428] Step 5:

[0429] Users provide feedback on news summaries, and the server collects the feedback. The input is the user's feedback text or voice data, and the output is the sentiment analysis result. Specifically, the server passes the feedback data to a sentiment engine (e.g., TextBlob library) for sentiment analysis.

[0430] Step 6:

[0431] The server readjusts the news feed based on the results of the sentiment analysis of the feedback. The inputs are the analyzed sentiment data and news summary data, and the output is an updated news feed. Specifically, the server reflects the sentiment analysis results in the user profile and uses them for future news summary distribution.

[0432] The above are the processing steps of the system program that realizes the application example. These steps realize news summary delivery based on the user's interests and emotions.

[0433] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0439] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0441] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0443] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0444] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0445] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0446] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0448] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0449] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0450] News information gathering module

[0451] News article collection

[0452] The server collects the latest news articles from news sites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[0453] Specific examples

[0454] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[0455] Generative AI summary module

[0456] News Summary

[0457] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0458] Specific examples

[0459] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[0460] User Management Module

[0461] User information management and analysis

[0462] The server stores users' registration information and interest categories in a database, and then analyzes their browsing history to identify news categories that interest each individual user.

[0463] Specific examples

[0464] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[0465] Summary Delivery Module

[0466] Customize and deliver summaries

[0467] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notifications.

[0468] Specific examples

[0469] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0470] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI system creates summaries. By delivering summaries customized based on the user's interests, the system prevents confusion caused by information overload and enables efficient information acquisition.

[0471] The processing flow will be explained below.

[0472] Step 1:

[0473] The server schedules news gathering tasks, for example setting up a task to gather news articles from news sites and RSS feeds every morning at 9am.

[0474] Step 2:

[0475] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[0476] Step 3:

[0477] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[0478] Step 4:

[0479] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[0480] Step 5:

[0481] The server schedules generative AI summarization tasks, for example, setting up a task to summarize news articles collected over the last 24 hours every morning at 10:00.

[0482] Step 6:

[0483] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[0484] Step 7:

[0485] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[0486] Step 8:

[0487] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[0488] Step 9:

[0489] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[0490] Step 10:

[0491] The server customizes summaries based on the user's interests, using each user's profile information to select the most relevant news summaries.

[0492] Step 11:

[0493] The server schedules summary delivery tasks, for example, setting up a task to deliver a customized summary to users every morning at 9am.

[0494] Step 12:

[0495] The server sends a push notification to the user's device at the specified time, containing a link to a customized news summary for easy user access.

[0496] Step 13:

[0497] A user receives a push notification and clicks on a link, which takes them to a customized news summary.

[0498] Step 14:

[0499] The server updates the user's browsing history, recording which news summaries the user has viewed and reflecting this in the next summary customization.

[0500] Example 1

[0501] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0502] In modern society, a huge amount of news information is generated every day, and there is a demand for quickly and efficiently extracting and acquiring important information from it. Conventional news gathering systems can easily cause confusion due to information overload and overlook important points. Another problem is that it is difficult for users to easily acquire news information customized based on their own interests. There is a need for a system that can solve these issues and enable users to efficiently understand important news.

[0503] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0504] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on user interests, and means for delivering the customized news summaries to users, thereby enabling efficient collection and summarization of news information, customization based on user interests, and delivery of highly relevant news.

[0505] "Means for collecting news information" refers to a system for obtaining news articles from multiple information sources via a network and storing these articles in a database.

[0506] "Means for summarizing collected news information using generative artificial intelligence" refers to a system that extracts important points from collected news information, creates a summary based on these points, and saves the summary back in a database.

[0507] "Means for customizing summarized news information based on a user's interests" refers to a mechanism that identifies the news summaries most relevant to a user's interests based on the user's registration information, categories of interest, and browsing history.

[0508] "Means for delivering customized news summaries to users" refers to a mechanism for selecting customized news summaries according to a daily, weekly, or monthly schedule and delivering them to users' devices via push notifications.

[0509] "Multiple sources" refers to multiple online sources of news articles and information, such as news sites and RSS feeds.

[0510] "Database" refers to a digital information management system for organizing and storing collected news information, generated summaries, user information, etc.

[0511] "Generative AI" refers to artificial intelligence models (e.g., GPT-3) that are used to analyze news articles, extract key points, and generate summaries.

[0512] "User Registration Information" refers to information such as personal information and interest categories provided by a User when registering with the System.

[0513] "Viewing history" refers to historical data of news articles that a user has accessed within the system.

[0514] "Relevant news" refers to the news information that is most valuable to a user based on the user's interests and past browsing history.

[0515] "Push notification" refers to a communication method for sending information in real time from a server to a user's device.

[0516] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0517] News information gathering module

[0518] Hardware and Software Configuration

[0519] The server collects the latest news articles from multiple sources, such as news websites and RSS feeds. The server is connected to the Internet via a network interface. The collection task is run every morning at 9:00 a.m. using scheduling software (e.g., cron).

[0520] Specific operation example

[0521] Every morning at 9:00, the server sends an HTTP request to the news site's API to retrieve the latest article data. It then parses the retrieved data to extract the title, body text, and publication date and time of each article, and stores them in a database management system (e.g., MySQL).

[0522] Generative AI summary module

[0523] Hardware and Software Configuration

[0524] The server retrieves news articles from the database and passes them to a generative AI (e.g., GPT-3), which analyzes each article, extracts key points, and generates a summary.

[0525] Specific operation example

[0526] A server retrieves the 10 latest news articles from a database at 10 AM and passes them to a generative artificial intelligence using the following prompt:

[0527] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0528] Article Title: (Article Title)

[0529] Article body: (Article body)

[0530] Generative artificial intelligence summarizes each article and stores the generated summaries in a database.

[0531] User Management Module

[0532] Hardware and Software Configuration

[0533] The server stores user registration information and interest categories in a database. Furthermore, the server analyzes users' browsing history to identify news categories that each user is interested in. This analysis is performed using a data analysis library (e.g., pandas).

[0534] Specific operation example

[0535] When a user registers, they enter their email address and the categories they are interested in. The server stores this information in a user table, and also tracks the user's browsing history and periodically updates the news categories they are interested in.

[0536] Summary Delivery Module

[0537] Hardware and Software Configuration

[0538] The server delivers news summaries according to a set schedule, such as daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and delivers customized summaries to the user's device via push notifications. Push notifications are delivered using a notification service (e.g., Firebase Cloud Messaging).

[0539] Specific operation example

[0540] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0541] In this way, the server efficiently collects news information, and the generative AI creates summaries. By delivering summaries customized based on the user's interests, confusion caused by information overload is prevented, enabling efficient information acquisition.

[0542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0543] Step 1:

[0544] A server schedules news gathering tasks.

[0545] Input: Task Scheduler configuration information

[0546] Data processing / calculation: Set the task to start at a specified time (9:00 every morning).

[0547] Output: The state when the schedule is completed and the task is automatically executed at the specified time.

[0548] What it does: The server uses cron to set up a news gathering task to run every morning at 9am.

[0549] Step 2:

[0550] The server sends an HTTP request to the specified news site to retrieve article data.

[0551] Input: News site API endpoint

[0552] Data processing / calculation: Sending HTTP requests and receiving responses.

[0553] Output: Latest news article data

[0554] Specific operation: The server accesses a news site such as news-example.com, sends an API request, and receives the news data returned as a response.

[0555] Step 3:

[0556] The server analyzes the received news data and stores it in a database.

[0557] Input: News article data

[0558] Data processing / calculation: Parsing data and extracting fields (title, body, publication date).

[0559] Output: News articles stored in a database

[0560] What it does: The server parses the data using Python's BeautifulSoup, extracts the article title, body text, and publication date and time, and stores them in a MySQL database.

[0561] Step 4:

[0562] The server retrieves the latest news from the database and passes it on to the generative artificial intelligence.

[0563] Input: News articles from the database

[0564] Data processing / calculation: Select the latest news articles using SQL queries.

[0565] Output: News article passed to the generative AI

[0566] What it does: The server uses an SQL query to retrieve the 10 latest news articles from the database and passes them to the generative artificial intelligence (GPT-3).

[0567] Step 5:

[0568] Generative artificial intelligence analyzes news articles, generates summaries, and returns them to the server.

[0569] Input: News article data and prompt

[0570] Data processing / calculation: News article summary generation

[0571] Output: Generated news summary

[0572] Example prompt:

[0573] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0574] Article Title: (Article Title)

[0575] Article body: (Article body)

[0576] Specific operation: The server sends a news article and a prompt to the generative AI, which then generates a summary and replies.

[0577] Step 6:

[0578] The server stores the generated summaries in a database.

[0579] Input: News summaries generated by generative artificial intelligence

[0580] Data processing / calculation: Summary data storage processing

[0581] Output: News summaries stored in a database

[0582] Specific behavior: The server stores the generated news summaries in a database.

[0583] Step 7:

[0584] The user enters registration information and interest categories from the device.

[0585] Input: User registration information and interest categories

[0586] Data processing / calculation: Validating and saving input data

[0587] Output: User profile data

[0588] Specific operation: The user enters their email address and interest categories on the device's registration screen, which is then received by the server and stored in a database.

[0589] Step 8:

[0590] The server analyzes the user's browsing history and identifies news categories of interest.

[0591] Input: User's browsing history

[0592] Data processing / calculation: Analysis of browsing history and category identification

[0593] Output: Updated user profile

[0594] What it does: The server uses Python's pandas to analyze browsing history, identify news categories of interest, and update the user profile.

[0595] Step 9:

[0596] The server selects customized news summaries and delivers them to users via push notifications.

[0597] Input: User profile and news summary

[0598] Data processing / computation: Selecting news summaries and generating notifications based on user interests

[0599] Output: Push notification sent to user device

[0600] How it works: The server uses Firebase Cloud Messaging to generate a URL link for a news summary customized based on the user's profile and sends it to the user's device via a push notification.

[0601] (Application example 1)

[0602] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] In today's world, the problem of information overload is becoming more serious, making it difficult to efficiently obtain important information from the vast amount of news available. Furthermore, there is a lack of efficient ways to collect and organize only the news that interests users. Meanwhile, there is a demand for improving the user experience by delivering relevant news summaries in a timely manner.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0605] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, and means for delivering the customized news summary to the terminal by push notification, thereby enabling highly relevant news information based on the user's interests to be efficiently summarized, further customized, and delivered in a timely manner.

[0606] "News information" refers to information such as articles, reports, commentaries, and special features that contain general news content.

[0607] "Generative artificial intelligence" is a type of artificial intelligence designed to understand, analyze, and summarize collected data.

[0608] A "summary" is a short summary of important points extracted from news information.

[0609] "User interests" refer to the specific topics or categories in which a user is interested.

[0610] "Customization" is the process of adapting or modifying content to suit the needs and interests of a particular user.

[0611] "Device" refers to a mobile device such as a smartphone or tablet.

[0612] "Push notification" is a function that sends information from a server to a device in real time.

[0613] A "server" is a computer system that processes data over a network and provides services to other devices and users.

[0614] "Means" are the methods or tools used to achieve a particular goal.

[0615] The present invention provides a system that uses generative artificial intelligence to summarize news information and delivers customized news summaries based on the user's interests to a terminal via push notification. The following components are included as means necessary to implement the present invention.

[0616] System Components

[0617] 1. How to gather news information:

[0618] The server retrieves articles from multiple news sources over a network and stores these articles in a database. The server accesses the news sources at designated times to collect the latest articles.

[0619] 2. Generative AI Summarization:

[0620] The server retrieves the collected news articles from the database and passes them to a generative AI (e.g., GPT-4), which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0621] 3. Customization based on user interests:

[0622] The server stores users' registration information and interest categories in a database, and analyzes their browsing history to identify the news categories that each user is interested in. Based on this information, the generated news summaries are customized.

[0623] 4. Push notification delivery method:

[0624] The server delivers customized news summaries to devices via push notifications based on a daily, weekly, or monthly schedule. For example, the server executes a summary delivery task every morning at 9:00, selects news summaries based on each user's profile, and sends them to devices via push notifications.

[0625] Hardware and Software

[0626] Hardware:

[0627] Server: A central system for collecting and processing data from multiple news sources.

[0628] Device: A mobile device such as a smartphone or tablet.

[0629] software:

[0630] Database: A database system such as MySQL or PostgreSQL.

[0631] Generative AI models: Artificial intelligence models for news article summarization, such as GPT-4.

[0632] Server program: Software that implements the functions of data collection, summary generation, customization, and push notification delivery.

[0633] Push notification services: Third-party push notification service providers.

[0634] Specific examples of processing

[0635] The server accesses news sources every morning at 9 a.m. and retrieves the latest articles via API. It then stores this data in a database and passes it to a generative AI, which summarizes each article and stores the summaries in a database. The server then selects the most relevant news summaries based on the user's interests and sends them to the user's smartphone using a push notification service.

[0636] Examples of prompt statements

[0637] The following prompt sentences are input into a generative artificial intelligence to summarize a news article.

[0638] Prompt statement:

[0639] "Summarize the following news article. Please use a maximum of 50 characters, a minimum of 25, and include the key points. Article content: {News article text}"

[0640] The above is a specific embodiment of the present invention. This system efficiently delivers news summaries based on the user's interests, preventing confusion caused by information overload and enabling efficient information acquisition.

[0641] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0642] Step 1:

[0643] The server accesses the news source every morning at 9:00 to retrieve the latest news articles. The input is the news source's API endpoint, and the output is the retrieved news article data. Specifically, the server sends an API request and receives a response. The received response data is analyzed to extract the article title, text, publication date, etc.

[0644] Step 2:

[0645] The server stores the extracted news article data in a database. The input is information such as the news article title, body text, and publication date, and the output is a news article entry stored in the database. Specifically, the information for each article is added as a new record in the "News Articles" table.

[0646] Step 3:

[0647] The server retrieves the latest news articles from the database at 10:00 AM and passes them to the generative AI. The input is multiple news article data retrieved from the database, and the output is a prompt to be input to the generative AI. Specifically, the text of each news article is used to generate the following prompt: "Please summarize the following news article. Please summarize in a maximum of 50 characters, a minimum of 25 characters, and include the key points. Article content: {News article text}"

[0648] Step 4:

[0649] A generative AI model summarizes a news article based on a prompt. The input is the prompt, and the output is a generated summary. Specifically, a generative AI model (e.g., GPT-4) analyzes the prompt and generates a summary. This generated summary is then stored in a database.

[0650] Step 5:

[0651] The server retrieves user information and interest categories from a database and customizes news summaries based on each user's interests. The input is the user's interest data and the generated news summaries, and the output is the customized news summaries. Specifically, it selects relevant news summaries based on each user's profile information and browsing history.

[0652] Step 6:

[0653] The server delivers customized news summaries to devices via push notifications. The input is the customized news summary and the user's device information, and the output is a push notification sent to the user's device. Specifically, a push notification service is used to notify each user of the selected news summary on their smartphone. Users can view detailed article content by tapping the notification.

[0654] The above are the specific processing steps of the system that realizes the application example. At each step, data processing and data calculation are performed based on the input data, thereby making it possible to obtain the required output.

[0655] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0656] This invention combines a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items on a daily, weekly, or monthly basis with an emotion engine that recognizes the user's emotions. This system delivers news summaries in a format that is appropriate for the user, enabling information organization and efficient information acquisition.

[0657] News information gathering module

[0658] News article collection

[0659] The server collects the latest news articles from news websites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[0660] Specific examples

[0661] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[0662] Generative AI summary module

[0663] News Summary

[0664] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0665] Specific examples

[0666] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[0667] User Management Module

[0668] User information management and analysis

[0669] The server stores users' registration information and interest categories in a database, and also analyzes users' browsing history to identify news categories that interest individual users.

[0670] Specific examples

[0671] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[0672] Emotion Engine Module

[0673] Emotion Analysis

[0674] The server receives user input data (text and voice) and passes it to the emotion engine, which analyzes the data and recognizes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[0675] Text-based sentiment analysis

[0676] When a user enters feedback on a news summary, the server passes the text data to an emotion engine for emotion analysis.

[0677] Specific examples

[0678] When a user enters feedback such as "This news was very interesting," the server passes the text to the emotion engine, which analyzes the text for positive emotions and updates the user profile.

[0679] Voice-based sentiment analysis

[0680] When a user comments on a news summary through a voice interface, the server passes the voice data to an emotion engine for emotion analysis.

[0681] Specific examples

[0682] When a user gives voice feedback such as "This news was very helpful," the server passes the voice data to the emotion engine, which analyzes the positive emotion and updates the user profile.

[0683] Summary Delivery Module

[0684] Customize and deliver summaries

[0685] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests and emotional data, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notification.

[0686] Specific examples

[0687] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0688] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI creates summaries. An emotion engine is used to analyze the user's emotions, and a more customized summary is delivered. This allows users to efficiently obtain important news.

[0689] The processing flow will be explained below.

[0690] Step 1:

[0691] The server schedules the news gathering task: Set up a task that gathers news articles from news sites and RSS feeds every morning at 9am.

[0692] Step 2:

[0693] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[0694] Step 3:

[0695] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[0696] Step 4:

[0697] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[0698] Step 5:

[0699] The server schedules the generative AI summarization task. It sets up a task to pass the news articles collected in the last 24 hours to the generative AI every morning at 10:00.

[0700] Step 6:

[0701] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[0702] Step 7:

[0703] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[0704] Step 8:

[0705] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[0706] Step 9:

[0707] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[0708] Step 10:

[0709] The server creates customized news summaries based on the user profile, taking into account the user's browsing history and interest categories to select the most relevant news summaries.

[0710] Step 11:

[0711] The user provides feedback on the news summary, which can be in text or audio format.

[0712] Step 12:

[0713] The server obtains the user's feedback data, and receives the text data or voice data entered by the user.

[0714] Step 13:

[0715] The server passes the feedback data to the emotion engine, which analyzes the text and voice data to recognize the user's emotions.

[0716] Step 14:

[0717] The server saves the analysis results obtained from the emotion engine in the user profile, and updates the profile with the user's emotional tendencies based on the analyzed emotion data.

[0718] Step 15:

[0719] The server takes the emotion data into account the next time it delivers a news summary, and combines the user's emotion data with the interest data to create a more customized summary.

[0720] Step 16:

[0721] The server delivers news summaries according to a daily, weekly, or monthly schedule, and sends optimized summaries to users' devices via push notifications at specified times.

[0722] Step 17:

[0723] A user receives a push notification and clicks on a link to a news summary, which displays a customized news summary.

[0724] Step 18:

[0725] The server updates the user's browsing history, recording the information about the news summaries the user has viewed and reflecting it in future summary customizations.

[0726] Example 2

[0727] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0728] Conventional news distribution systems provide uniform news summaries without considering the user's interests or emotions, making it difficult to efficiently provide optimal information to individual users. Another issue is the time required to collect a large number of news articles and extract important information.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0730] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, means for recognizing the user's emotions and adjusting the summaries based on the emotions, and means for delivering customized news summaries to the user, thereby enabling efficient delivery of customized news summaries that reflect the user's interests and emotions.

[0731] A "means for collecting news information" is a means for obtaining news articles from multiple news sources via a network and storing the news articles in a database.

[0732] "Generative artificial intelligence" refers to advanced natural language processing models, such as GPT-4, that are used to summarize collected news information.

[0733] The "means for customizing summarized news information based on the user's interests" is a means for analyzing the user's registration information, browsing history, etc., and adjusting the news summary to suit the user's interests.

[0734] "Means for recognizing user emotions and tailoring summaries based on those emotions" refers to means for analyzing user input data (text or voice) to identify emotions and customizing news summaries based on that emotional data.

[0735] "Means for delivering customized news summaries to users" means means for generating news summaries that reflect the user's interests and emotions based on a specific schedule and delivering them to the user's device via push notification or other means.

[0736] The present invention is a system that uses generative artificial intelligence to summarize news information and provide users with customized news summaries. This system collects news information, creates summaries using generative artificial intelligence, and delivers summaries customized based on the user's interests and emotions, allowing users to efficiently obtain important news information.

[0737] News information gathering module

[0738] News article collection

[0739] The server accesses news sites and RSS feeds every morning at 9:00 to collect the latest news articles. The server uses the APIs of major news sites to obtain a list of the latest news articles, then analyzes the title, text, and publication date and time of the retrieved news articles, and stores this data in a database for later processing.

[0740] Specific examples

[0741] The server sends a request to the API endpoint of the "news source" to get a list of new articles. For example, if the article title is "Latest Technology Announcement," the body text is "New Technology...," and the publication date is "2023-10-01," it stores these in the database.

[0742] Generative AI summary module

[0743] News summary generation

[0744] At 10:00 AM, the server retrieves the latest news articles from the database and passes them to a generative AI (e.g., GPT-4) to generate summaries, which are then stored in the database.

[0745] Specific examples

[0746] The server retrieves a news article titled "Latest Technology Announcement" from the database at 10:00 AM. It then inputs the prompt "Please summarize the following news article: 'Latest Technology Announcement...'" into the generative AI model, and stores the resulting summary "New technology was announced" in the database.

[0747] User Management Module

[0748] User information management and analysis

[0749] The server receives the user's registration information, stores the user's interest categories in a database, and tracks the user's browsing history and analyzes it to identify the news categories that interest the user.

[0750] Specific examples

[0751] When a user fills in the registration form with "email address: example@example.com" and "interest category: technology", the server stores this information in the user table. After that, every time the user visits a news summary page, the server tracks the user's browsing history and analyzes their interest categories.

[0752] Emotion Engine Module

[0753] Emotion Analysis

[0754] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[0755] Text-based sentiment analysis example

[0756] If a user enters feedback such as "This news was very informative," the server passes the text data to the emotion engine, which analyzes the text for positive emotions and stores the results in the user profile.

[0757] Voice-based emotion analysis example

[0758] If a user provides voice feedback such as "This news was really helpful," the server passes the voice data to the emotion engine, which analyzes the voice, identifies positive emotions, and stores the results in the user profile.

[0759] Summary Delivery Module

[0760] Customize and deliver summaries

[0761] The server delivers customized news summaries to users on a daily, weekly, or monthly schedule, primarily via push notifications sent directly to their devices.

[0762] Specific examples

[0763] The server executes a summary delivery task every morning at 9:00 and retrieves all users from the user table. Based on User A's profile, which states "Interest: Technology, Emotion: Positive," the server selects a summary of the "Latest Technology Announcement" and generates it as a URL link. This link is then sent to User A's device via a push notification.

[0764] The above is a specific embodiment of the present invention. The server collects news information from multiple news sources, and the generative AI generates summaries. Furthermore, an emotion engine is used to analyze the user's emotions, and the news summaries are customized based on the analysis results, allowing users to efficiently obtain news that is important and relevant to them.

[0765] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0766] Step 1:

[0767] The server accesses news sources (news sites and RSS feeds) every morning at 9:00 and retrieves a list of the latest news articles.

[0768] Input: News source URL or API endpoint

[0769] Output: A list of news articles

[0770] Specific behavior:

[0771] The server sends HTTP requests to pre-configured news source URLs or API endpoints, parses the news article data received in response, which is often provided in JSON or XML format, extracts the retrieved news articles (title, body text, publication date and time), and stores them in a database.

[0772] Step 2:

[0773] The server retrieves the latest news articles from the database at 10 a.m. and passes them to a generative artificial intelligence to generate summaries.

[0774] Input: News articles stored in a database

[0775] Output: A summarized news article

[0776] Specific behavior:

[0777] The server retrieves multiple (e.g., 10) recent news articles from the database. It then creates a prompt to summarize the content of each news article and passes it to a generative AI (e.g., GPT-4). The generative AI generates a summary of each article based on the prompt and stores the summary results back in the database. A prompt such as "Please summarize the following news article: 'The latest technology announcement...'" is used.

[0778] Step 3:

[0779] When a user registers or logs in, the server obtains the user's interests and category information and stores it in a database.

[0780] Input: User registration information and interest categories

[0781] Output: User information stored in the database

[0782] Specific behavior:

[0783] When a user fills in a registration form with their email address and interest categories and submits it, the server receives this information and stores it in a database. For example, the information "Email address: example@example.com" and "Interest categories: Technology" are stored in the user table. In addition, the server tracks the user's browsing history and periodically updates their interest categories.

[0784] Step 4:

[0785] The server passes the text and voice data entered by the user to the emotion engine and analyzes the user's emotions.

[0786] Input: User feedback text or voice data

[0787] Output: Emotion analysis results

[0788] Specific behavior:

[0789] When a user submits feedback on a news summary, they provide text or voice data. The server passes this text data (e.g., "This news was very interesting") or voice data to the emotion engine. The emotion engine analyzes the data, identifies emotions such as positive or negative, and stores the results in the user profile.

[0790] Step 5:

[0791] The server generates and delivers customized news summaries to user terminals according to a daily, weekly, or monthly schedule.

[0792] Input: User profile information (interests, emotional data), summarized news articles

[0793] Output: A customized news summary delivered to the user

[0794] Specific behavior:

[0795] The server runs a news summary delivery task at 9:00 every morning, retrieving all users from the user table. Based on each user's profile (e.g., "Interests: Technology, Sentiment: Positive"), it selects the most appropriate news summary. It generates a URL link based on this summary and sends it to the user's device as a push notification. The user receives the push notification on their device and can click the link to view the customized news summary.

[0796] (Application example 2)

[0797] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0798] Conventional news delivery systems could customize and deliver news based on a user's interests, but they could not consider the user's emotions. As a result, news content may not be appropriate for the user, making it difficult to improve the user experience. Furthermore, there was a lack of user feedback to adjust summary generation. The present invention aims to solve these problems and efficiently provide news summaries customized based on a user's interests and emotions.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0800] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests and emotions, means for delivering the customized news summaries to the user, means for analyzing emotions from user feedback, and means for adjusting the news feed based on the analysis results, thereby enabling delivery of personalized news summaries tailored to the user's interests and emotions.

[0801] "Means for collecting news information" refers to the technology of obtaining the latest article data from multiple information sources via the Internet and storing it in a database.

[0802] "Means for summarizing collected news information using generative artificial intelligence" is an AI technology that analyzes collected news information, extracts important points, and generates summaries.

[0803] The "means for customizing summarized news information based on user interests and emotions" is a technology that adjusts news summaries taking into account the user's registration information, interest categories, and emotion analysis results.

[0804] The "means for delivering customized news summaries to users" refers to a technology for delivering customized news summaries to specific users via push notification, email, or the like.

[0805] "Means for analyzing emotions from user feedback" refers to a technology that analyzes text and voice feedback entered by the user and recognizes the user's emotions based on that.

[0806] "Means for adjusting news feeds based on analysis results" refers to technology that readjusts news feeds to make them more likely to interest users based on the results of sentiment analysis.

[0807] The present invention proposes a system for summarizing news information using generative artificial intelligence and providing a news summary customized according to a user's interests and emotions. Specific embodiments will be described in detail below.

[0808] News information gathering

[0809] The server periodically collects the latest news articles from multiple information sources (news websites, RSS feeds, etc.) via the Internet, and stores the collected news articles in a database for later processing.

[0810] Specific examples

[0811] For example, every morning at 9:00, the server accesses major news sites to retrieve the latest article data, then analyzes this data and stores the title, text, and publication date of each article in a database.

[0812] News summary generation

[0813] The server retrieves the collected news articles from the database and passes them to a generative artificial intelligence (e.g., the T5 model from the transformers library) to generate summaries, which are then stored back in the database.

[0814] Specific examples

[0815] For example, at 10:00 AM, the server retrieves the 10 latest news articles from the database and passes them to the GAI, which then summarizes each article and stores the summaries in the database.

[0816] User interest and sentiment analysis

[0817] The server takes the feedback (text and / or voice) provided by the user and passes it to an emotion engine (e.g., the TextBlob library) for sentiment analysis. The analysis results are stored in the user profile and used to customize news summaries. The server also stores the user's registration information and interest categories in a constantly updated database.

[0818] Specific examples

[0819] If a user enters feedback such as "This news was very interesting," the server passes the text to an emotion engine to analyze positive emotions and update the user profile.

[0820] Delivering customized news summaries

[0821] The server delivers customized news summaries to specific users via push notifications or emails, and delivery schedules can be set on a daily, weekly, or monthly basis, with the news summaries customized based on the user's interests and emotional data.

[0822] Specific examples

[0823] For example, every morning at 9:00 AM, the server retrieves all users and selects a customized news summary based on each user's profile. The generated summary is then sent to the user's device as a push notification in the form of a link.

[0824] Prompt Sentence Examples

[0825] Example prompt for a generative AI to generate a news summary:

[0826] Content: Economy

[0827] Summarize.

[0828] Input: The Nikkei Stock Average has risen significantly, reaching its highest level in the past 10 years.

[0829] Prompt: Summarize this content in 50 characters or less.

[0830] By the above means, the present invention can efficiently provide news summaries customized based on the user's interests and emotions, thereby improving the user experience and providing more personalized information.

[0831] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] To collect news information, the server accesses multiple information sources (news sites, RSS feeds, etc.) every morning at 9:00 and retrieves the latest article data. The input is a list of news site URLs, and the output is a database entry containing the article title, text, and publication date and time. Specifically, the server sends an HTTP request to retrieve the article data, analyzes it, and stores it in the database.

[0834] Step 2:

[0835] The server retrieves news articles collected from a database and passes them to a generative AI (for example, the T5 model from the Transformers library) to generate summaries. The input is the title and body of the news article retrieved from the database, and the output is a database entry containing the generated summary text. Specifically, the server sends the body of the news article to the generative AI and uses text such as "Summarize this content. The character limit is 50 characters or less" as a prompt for the AI ​​model.

[0836] Step 3:

[0837] The server customizes news summaries by taking into account the user's interests and emotions. The input is the user's registration information and emotion data obtained from their feedback, and the output is a customized news summary. Specifically, the server analyzes the user's profile data and selects the most suitable content for the user from the generated summaries.

[0838] Step 4:

[0839] The server delivers customized news summaries to the user's device. The inputs are the customized news summaries and the user's device information, and the output is the news summaries sent via push notification or email. Specifically, the server sends the summaries to the user's device via push notification or email server.

[0840] Step 5:

[0841] Users provide feedback on news summaries, and the server collects the feedback. The input is the user's feedback text or voice data, and the output is the sentiment analysis result. Specifically, the server passes the feedback data to a sentiment engine (e.g., TextBlob library) for sentiment analysis.

[0842] Step 6:

[0843] The server readjusts the news feed based on the results of the sentiment analysis of the feedback. The inputs are the analyzed sentiment data and news summary data, and the output is an updated news feed. Specifically, the server reflects the sentiment analysis results in the user profile and uses them for future news summary distribution.

[0844] The above are the processing steps of the system program that realizes the application example. These steps realize news summary delivery based on the user's interests and emotions.

[0845] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0846] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0848] [Third embodiment]

[0849] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0850] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0851] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0852] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0853] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0854] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0855] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0856] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0857] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0858] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0859] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0860] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0861] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0862] News information gathering module

[0863] News article collection

[0864] The server collects the latest news articles from news sites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[0865] Specific examples

[0866] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[0867] Generative AI summary module

[0868] News Summary

[0869] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[0870] Specific examples

[0871] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[0872] User Management Module

[0873] User information management and analysis

[0874] The server stores users' registration information and interest categories in a database, and then analyzes their browsing history to identify news categories that interest each individual user.

[0875] Specific examples

[0876] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[0877] Summary Delivery Module

[0878] Customize and deliver summaries

[0879] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notifications.

[0880] Specific examples

[0881] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0882] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI system creates summaries. By delivering summaries customized based on the user's interests, the system prevents confusion caused by information overload and enables efficient information acquisition.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] The server schedules news gathering tasks, for example setting up a task to gather news articles from news sites and RSS feeds every morning at 9am.

[0886] Step 2:

[0887] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[0888] Step 3:

[0889] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[0890] Step 4:

[0891] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[0892] Step 5:

[0893] The server schedules generative AI summarization tasks, for example, setting up a task to summarize news articles collected over the last 24 hours every morning at 10:00.

[0894] Step 6:

[0895] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[0896] Step 7:

[0897] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[0898] Step 8:

[0899] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[0900] Step 9:

[0901] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[0902] Step 10:

[0903] The server customizes summaries based on the user's interests, using each user's profile information to select the most relevant news summaries.

[0904] Step 11:

[0905] The server schedules summary delivery tasks, for example, setting up a task to deliver a customized summary to users every morning at 9am.

[0906] Step 12:

[0907] The server sends a push notification to the user's device at the specified time, containing a link to a customized news summary for easy user access.

[0908] Step 13:

[0909] A user receives a push notification and clicks on a link, which takes them to a customized news summary.

[0910] Step 14:

[0911] The server updates the user's browsing history, recording which news summaries the user has viewed and reflecting this in the next summary customization.

[0912] Example 1

[0913] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0914] In modern society, a huge amount of news information is generated every day, and there is a demand for quickly and efficiently extracting and acquiring important information from it. Conventional news gathering systems can easily cause confusion due to information overload and overlook important points. Another problem is that it is difficult for users to easily acquire news information customized based on their own interests. There is a need for a system that can solve these issues and enable users to efficiently understand important news.

[0915] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0916] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on user interests, and means for delivering the customized news summaries to users, thereby enabling efficient collection and summarization of news information, customization based on user interests, and delivery of highly relevant news.

[0917] "Means for collecting news information" refers to a system for obtaining news articles from multiple information sources via a network and storing these articles in a database.

[0918] "Means for summarizing collected news information using generative artificial intelligence" refers to a system that extracts important points from collected news information, creates a summary based on these points, and saves the summary back in a database.

[0919] "Means for customizing summarized news information based on a user's interests" refers to a mechanism that identifies the news summaries most relevant to a user's interests based on the user's registration information, categories of interest, and browsing history.

[0920] "Means for delivering customized news summaries to users" refers to a mechanism for selecting customized news summaries according to a daily, weekly, or monthly schedule and delivering them to users' devices via push notifications.

[0921] "Multiple sources" refers to multiple online sources of news articles and information, such as news sites and RSS feeds.

[0922] "Database" refers to a digital information management system for organizing and storing collected news information, generated summaries, user information, etc.

[0923] "Generative AI" refers to artificial intelligence models (e.g., GPT-3) that are used to analyze news articles, extract key points, and generate summaries.

[0924] "User Registration Information" refers to information such as personal information and interest categories provided by a User when registering with the System.

[0925] "Viewing history" refers to historical data of news articles that a user has accessed within the system.

[0926] "Relevant news" refers to the news information that is most valuable to a user based on the user's interests and past browsing history.

[0927] "Push notification" refers to a communication method for sending information in real time from a server to a user's device.

[0928] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[0929] News information gathering module

[0930] Hardware and Software Configuration

[0931] The server collects the latest news articles from multiple sources, such as news websites and RSS feeds. The server is connected to the Internet via a network interface. The collection task is run every morning at 9:00 a.m. using scheduling software (e.g., cron).

[0932] Specific operation example

[0933] Every morning at 9:00, the server sends an HTTP request to the news site's API to retrieve the latest article data. It then parses the retrieved data to extract the title, body text, and publication date and time of each article, and stores them in a database management system (e.g., MySQL).

[0934] Generative AI summary module

[0935] Hardware and Software Configuration

[0936] The server retrieves news articles from the database and passes them to a generative AI (e.g., GPT-3), which analyzes each article, extracts key points, and generates a summary.

[0937] Specific operation example

[0938] A server retrieves the 10 latest news articles from a database at 10 AM and passes them to a generative artificial intelligence using the following prompt:

[0939] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0940] Article Title: (Article Title)

[0941] Article body: (Article body)

[0942] Generative artificial intelligence summarizes each article and stores the generated summaries in a database.

[0943] User Management Module

[0944] Hardware and Software Configuration

[0945] The server stores user registration information and interest categories in a database. Furthermore, the server analyzes users' browsing history to identify news categories that each user is interested in. This analysis is performed using a data analysis library (e.g., pandas).

[0946] Specific operation example

[0947] When a user registers, they enter their email address and the categories they are interested in. The server stores this information in a user table, and also tracks the user's browsing history and periodically updates the news categories they are interested in.

[0948] Summary Delivery Module

[0949] Hardware and Software Configuration

[0950] The server delivers news summaries according to a set schedule, such as daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and delivers customized summaries to the user's device via push notifications. Push notifications are delivered using a notification service (e.g., Firebase Cloud Messaging).

[0951] Specific operation example

[0952] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[0953] In this way, the server efficiently collects news information, and the generative AI creates summaries. By delivering summaries customized based on the user's interests, confusion caused by information overload is prevented, enabling efficient information acquisition.

[0954] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0955] Step 1:

[0956] A server schedules news gathering tasks.

[0957] Input: Task Scheduler configuration information

[0958] Data processing / calculation: Set the task to start at a specified time (9:00 every morning).

[0959] Output: The state when the schedule is completed and the task is automatically executed at the specified time.

[0960] What it does: The server uses cron to set up a news gathering task to run every morning at 9am.

[0961] Step 2:

[0962] The server sends an HTTP request to the specified news site to retrieve article data.

[0963] Input: News site API endpoint

[0964] Data processing / calculation: Sending HTTP requests and receiving responses.

[0965] Output: Latest news article data

[0966] Specific operation: The server accesses a news site such as news-example.com, sends an API request, and receives the news data returned as a response.

[0967] Step 3:

[0968] The server analyzes the received news data and stores it in a database.

[0969] Input: News article data

[0970] Data processing / calculation: Parsing data and extracting fields (title, body, publication date).

[0971] Output: News articles stored in a database

[0972] What it does: The server parses the data using Python's BeautifulSoup, extracts the article title, body text, and publication date and time, and stores them in a MySQL database.

[0973] Step 4:

[0974] The server retrieves the latest news from the database and passes it on to the generative artificial intelligence.

[0975] Input: News articles from the database

[0976] Data processing / calculation: Select the latest news articles using SQL queries.

[0977] Output: News article passed to the generative AI

[0978] What it does: The server uses an SQL query to retrieve the 10 latest news articles from the database and passes them to the generative artificial intelligence (GPT-3).

[0979] Step 5:

[0980] Generative artificial intelligence analyzes news articles, generates summaries, and returns them to the server.

[0981] Input: News article data and prompt

[0982] Data processing / calculation: News article summary generation

[0983] Output: Generated news summary

[0984] Example prompt:

[0985] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[0986] Article Title: (Article Title)

[0987] Article body: (Article body)

[0988] Specific operation: The server sends a news article and a prompt to the generative AI, which then generates a summary and replies.

[0989] Step 6:

[0990] The server stores the generated summaries in a database.

[0991] Input: News summaries generated by generative artificial intelligence

[0992] Data processing / calculation: Summary data storage processing

[0993] Output: News summaries stored in a database

[0994] Specific behavior: The server stores the generated news summaries in a database.

[0995] Step 7:

[0996] The user enters registration information and interest categories from the device.

[0997] Input: User registration information and interest categories

[0998] Data processing / calculation: Validating and saving input data

[0999] Output: User profile data

[1000] Specific operation: The user enters their email address and interest categories on the device's registration screen, which is then received by the server and stored in a database.

[1001] Step 8:

[1002] The server analyzes the user's browsing history and identifies news categories of interest.

[1003] Input: User's browsing history

[1004] Data processing / calculation: Analysis of browsing history and category identification

[1005] Output: Updated user profile

[1006] What it does: The server uses Python's pandas to analyze browsing history, identify news categories of interest, and update the user profile.

[1007] Step 9:

[1008] The server selects customized news summaries and delivers them to users via push notifications.

[1009] Input: User profile and news summary

[1010] Data processing / computation: Selecting news summaries and generating notifications based on user interests

[1011] Output: Push notification sent to user device

[1012] How it works: The server uses Firebase Cloud Messaging to generate a URL link for a news summary customized based on the user's profile and sends it to the user's device via a push notification.

[1013] (Application example 1)

[1014] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1015] In today's world, the problem of information overload is becoming more serious, making it difficult to efficiently obtain important information from the vast amount of news available. Furthermore, there is a lack of efficient ways to collect and organize only the news that interests users. Meanwhile, there is a demand for improving the user experience by delivering relevant news summaries in a timely manner.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1017] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, and means for delivering the customized news summary to the terminal by push notification, thereby enabling highly relevant news information based on the user's interests to be efficiently summarized, further customized, and delivered in a timely manner.

[1018] "News information" refers to information such as articles, reports, commentaries, and special features that contain general news content.

[1019] "Generative artificial intelligence" is a type of artificial intelligence designed to understand, analyze, and summarize collected data.

[1020] A "summary" is a short summary of important points extracted from news information.

[1021] "User interests" refer to the specific topics or categories in which a user is interested.

[1022] "Customization" is the process of adapting or modifying content to suit the needs and interests of a particular user.

[1023] "Device" refers to a mobile device such as a smartphone or tablet.

[1024] "Push notification" is a function that sends information from a server to a device in real time.

[1025] A "server" is a computer system that processes data over a network and provides services to other devices and users.

[1026] "Means" are the methods or tools used to achieve a particular goal.

[1027] The present invention provides a system that uses generative artificial intelligence to summarize news information and delivers customized news summaries based on the user's interests to a terminal via push notification. The following components are included as means necessary to implement the present invention.

[1028] System Components

[1029] 1. How to gather news information:

[1030] The server retrieves articles from multiple news sources over a network and stores these articles in a database. The server accesses the news sources at designated times to collect the latest articles.

[1031] 2. Generative AI Summarization:

[1032] The server retrieves the collected news articles from the database and passes them to a generative AI (e.g., GPT-4), which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[1033] 3. Customization based on user interests:

[1034] The server stores users' registration information and interest categories in a database, and analyzes their browsing history to identify the news categories that each user is interested in. Based on this information, the generated news summaries are customized.

[1035] 4. Push notification delivery method:

[1036] The server delivers customized news summaries to devices via push notifications based on a daily, weekly, or monthly schedule. For example, the server executes a summary delivery task every morning at 9:00, selects news summaries based on each user's profile, and sends them to devices via push notifications.

[1037] Hardware and Software

[1038] Hardware:

[1039] Server: A central system for collecting and processing data from multiple news sources.

[1040] Device: A mobile device such as a smartphone or tablet.

[1041] software:

[1042] Database: A database system such as MySQL or PostgreSQL.

[1043] Generative AI models: Artificial intelligence models for news article summarization, such as GPT-4.

[1044] Server program: Software that implements the functions of data collection, summary generation, customization, and push notification delivery.

[1045] Push notification services: Third-party push notification service providers.

[1046] Specific examples of processing

[1047] The server accesses news sources every morning at 9 a.m. and retrieves the latest articles via API. It then stores this data in a database and passes it to a generative AI, which summarizes each article and stores the summaries in a database. The server then selects the most relevant news summaries based on the user's interests and sends them to the user's smartphone using a push notification service.

[1048] Examples of prompt statements

[1049] The following prompt sentences are input into a generative artificial intelligence to summarize a news article.

[1050] Prompt statement:

[1051] "Summarize the following news article. Please use a maximum of 50 characters, a minimum of 25, and include the key points. Article content: {News article text}"

[1052] The above is a specific embodiment of the present invention. This system efficiently delivers news summaries based on the user's interests, preventing confusion caused by information overload and enabling efficient information acquisition.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The server accesses the news source every morning at 9:00 to retrieve the latest news articles. The input is the news source's API endpoint, and the output is the retrieved news article data. Specifically, the server sends an API request and receives a response. The received response data is analyzed to extract the article title, text, publication date, etc.

[1056] Step 2:

[1057] The server stores the extracted news article data in a database. The input is information such as the news article title, body text, and publication date, and the output is a news article entry stored in the database. Specifically, the information for each article is added as a new record in the "News Articles" table.

[1058] Step 3:

[1059] The server retrieves the latest news articles from the database at 10:00 AM and passes them to the generative AI. The input is multiple news article data retrieved from the database, and the output is a prompt to be input to the generative AI. Specifically, the text of each news article is used to generate the following prompt: "Please summarize the following news article. Please summarize in a maximum of 50 characters, a minimum of 25 characters, and include the key points. Article content: {News article text}"

[1060] Step 4:

[1061] A generative AI model summarizes a news article based on a prompt. The input is the prompt, and the output is a generated summary. Specifically, a generative AI model (e.g., GPT-4) analyzes the prompt and generates a summary. This generated summary is then stored in a database.

[1062] Step 5:

[1063] The server retrieves user information and interest categories from a database and customizes news summaries based on each user's interests. The input is the user's interest data and the generated news summaries, and the output is the customized news summaries. Specifically, it selects relevant news summaries based on each user's profile information and browsing history.

[1064] Step 6:

[1065] The server delivers customized news summaries to devices via push notifications. The input is the customized news summary and the user's device information, and the output is a push notification sent to the user's device. Specifically, a push notification service is used to notify each user of the selected news summary on their smartphone. Users can view detailed article content by tapping the notification.

[1066] The above are the specific processing steps of the system that realizes the application example. At each step, data processing and data calculation are performed based on the input data, thereby making it possible to obtain the required output.

[1067] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1068] This invention combines a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items on a daily, weekly, or monthly basis with an emotion engine that recognizes the user's emotions. This system delivers news summaries in a format that is appropriate for the user, enabling information organization and efficient information acquisition.

[1069] News information gathering module

[1070] News article collection

[1071] The server collects the latest news articles from news websites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[1072] Specific examples

[1073] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[1074] Generative AI summary module

[1075] News Summary

[1076] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[1077] Specific examples

[1078] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[1079] User Management Module

[1080] User information management and analysis

[1081] The server stores users' registration information and interest categories in a database, and also analyzes users' browsing history to identify news categories that interest individual users.

[1082] Specific examples

[1083] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[1084] Emotion Engine Module

[1085] Emotion Analysis

[1086] The server receives user input data (text and voice) and passes it to the emotion engine, which analyzes the data and recognizes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[1087] Text-based sentiment analysis

[1088] When a user enters feedback on a news summary, the server passes the text data to an emotion engine for emotion analysis.

[1089] Specific examples

[1090] When a user enters feedback such as "This news was very interesting," the server passes the text to the emotion engine, which analyzes the text for positive emotions and updates the user profile.

[1091] Voice-based sentiment analysis

[1092] When a user comments on a news summary through a voice interface, the server passes the voice data to an emotion engine for emotion analysis.

[1093] Specific examples

[1094] When a user gives voice feedback such as "This news was very helpful," the server passes the voice data to the emotion engine, which analyzes the positive emotion and updates the user profile.

[1095] Summary Delivery Module

[1096] Customize and deliver summaries

[1097] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests and emotional data, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notification.

[1098] Specific examples

[1099] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[1100] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI creates summaries. An emotion engine is used to analyze the user's emotions, and a more customized summary is delivered. This allows users to efficiently obtain important news.

[1101] The processing flow will be explained below.

[1102] Step 1:

[1103] The server schedules the news gathering task: Set up a task that gathers news articles from news sites and RSS feeds every morning at 9am.

[1104] Step 2:

[1105] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[1106] Step 3:

[1107] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[1108] Step 4:

[1109] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[1110] Step 5:

[1111] The server schedules the generative AI summarization task. It sets up a task to pass the news articles collected in the last 24 hours to the generative AI every morning at 10:00.

[1112] Step 6:

[1113] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[1114] Step 7:

[1115] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[1116] Step 8:

[1117] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[1118] Step 9:

[1119] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[1120] Step 10:

[1121] The server creates customized news summaries based on the user profile, taking into account the user's browsing history and interest categories to select the most relevant news summaries.

[1122] Step 11:

[1123] The user provides feedback on the news summary, which can be in text or audio format.

[1124] Step 12:

[1125] The server obtains the user's feedback data, and receives the text data or voice data entered by the user.

[1126] Step 13:

[1127] The server passes the feedback data to the emotion engine, which analyzes the text and voice data to recognize the user's emotions.

[1128] Step 14:

[1129] The server saves the analysis results obtained from the emotion engine in the user profile, and updates the profile with the user's emotional tendencies based on the analyzed emotion data.

[1130] Step 15:

[1131] The server takes the emotion data into account the next time it delivers a news summary, and combines the user's emotion data with the interest data to create a more customized summary.

[1132] Step 16:

[1133] The server delivers news summaries according to a daily, weekly, or monthly schedule, and sends optimized summaries to users' devices via push notifications at specified times.

[1134] Step 17:

[1135] A user receives a push notification and clicks on a link to a news summary, which displays a customized news summary.

[1136] Step 18:

[1137] The server updates the user's browsing history, recording the information about the news summaries the user has viewed and reflecting it in future summary customizations.

[1138] Example 2

[1139] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1140] Conventional news distribution systems provide uniform news summaries without considering the user's interests or emotions, making it difficult to efficiently provide optimal information to individual users. Another issue is the time required to collect a large number of news articles and extract important information.

[1141] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1142] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, means for recognizing the user's emotions and adjusting the summaries based on the emotions, and means for delivering customized news summaries to the user, thereby enabling efficient delivery of customized news summaries that reflect the user's interests and emotions.

[1143] A "means for collecting news information" is a means for obtaining news articles from multiple news sources via a network and storing the news articles in a database.

[1144] "Generative artificial intelligence" refers to advanced natural language processing models, such as GPT-4, that are used to summarize collected news information.

[1145] The "means for customizing summarized news information based on the user's interests" is a means for analyzing the user's registration information, browsing history, etc., and adjusting the news summary to suit the user's interests.

[1146] "Means for recognizing user emotions and tailoring summaries based on those emotions" refers to means for analyzing user input data (text or voice) to identify emotions and customizing news summaries based on that emotional data.

[1147] "Means for delivering customized news summaries to users" means means for generating news summaries that reflect the user's interests and emotions based on a specific schedule and delivering them to the user's device via push notification or other means.

[1148] The present invention is a system that uses generative artificial intelligence to summarize news information and provide users with customized news summaries. This system collects news information, creates summaries using generative artificial intelligence, and delivers summaries customized based on the user's interests and emotions, allowing users to efficiently obtain important news information.

[1149] News information gathering module

[1150] News article collection

[1151] The server accesses news sites and RSS feeds every morning at 9:00 to collect the latest news articles. The server uses the APIs of major news sites to obtain a list of the latest news articles, then analyzes the title, text, and publication date and time of the retrieved news articles, and stores this data in a database for later processing.

[1152] Specific examples

[1153] The server sends a request to the API endpoint of the "news source" to get a list of new articles. For example, if the article title is "Latest Technology Announcement," the body text is "New Technology...," and the publication date is "2023-10-01," it stores these in the database.

[1154] Generative AI summary module

[1155] News summary generation

[1156] At 10:00 AM, the server retrieves the latest news articles from the database and passes them to a generative AI (e.g., GPT-4) to generate summaries, which are then stored in the database.

[1157] Specific examples

[1158] The server retrieves a news article titled "Latest Technology Announcement" from the database at 10:00 AM. It then inputs the prompt "Please summarize the following news article: 'Latest Technology Announcement...'" into the generative AI model, and stores the resulting summary "New technology was announced" in the database.

[1159] User Management Module

[1160] User information management and analysis

[1161] The server receives the user's registration information, stores the user's interest categories in a database, and tracks the user's browsing history and analyzes it to identify the news categories that interest the user.

[1162] Specific examples

[1163] When a user fills in the registration form with "email address: example@example.com" and "interest category: technology", the server stores this information in the user table. After that, every time the user visits a news summary page, the server tracks the user's browsing history and analyzes their interest categories.

[1164] Emotion Engine Module

[1165] Emotion Analysis

[1166] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[1167] Text-based sentiment analysis example

[1168] If a user enters feedback such as "This news was very informative," the server passes the text data to the emotion engine, which analyzes the text for positive emotions and stores the results in the user profile.

[1169] Voice-based emotion analysis example

[1170] If a user provides voice feedback such as "This news was really helpful," the server passes the voice data to the emotion engine, which analyzes the voice, identifies positive emotions, and stores the results in the user profile.

[1171] Summary Delivery Module

[1172] Customize and deliver summaries

[1173] The server delivers customized news summaries to users on a daily, weekly, or monthly schedule, primarily via push notifications sent directly to their devices.

[1174] Specific examples

[1175] The server executes a summary delivery task every morning at 9:00 and retrieves all users from the user table. Based on User A's profile, which states "Interest: Technology, Emotion: Positive," the server selects a summary of the "Latest Technology Announcement" and generates it as a URL link. This link is then sent to User A's device via a push notification.

[1176] The above is a specific embodiment of the present invention. The server collects news information from multiple news sources, and the generative AI generates summaries. Furthermore, an emotion engine is used to analyze the user's emotions, and the news summaries are customized based on the analysis results, allowing users to efficiently obtain news that is important and relevant to them.

[1177] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] The server accesses news sources (news sites and RSS feeds) every morning at 9:00 and retrieves a list of the latest news articles.

[1180] Input: News source URL or API endpoint

[1181] Output: A list of news articles

[1182] Specific behavior:

[1183] The server sends HTTP requests to pre-configured news source URLs or API endpoints, parses the news article data received in response, which is often provided in JSON or XML format, extracts the retrieved news articles (title, body text, publication date and time), and stores them in a database.

[1184] Step 2:

[1185] The server retrieves the latest news articles from the database at 10 a.m. and passes them to a generative artificial intelligence to generate summaries.

[1186] Input: News articles stored in a database

[1187] Output: A summarized news article

[1188] Specific behavior:

[1189] The server retrieves multiple (e.g., 10) recent news articles from the database. It then creates a prompt to summarize the content of each news article and passes it to a generative AI (e.g., GPT-4). The generative AI generates a summary of each article based on the prompt and stores the summary results back in the database. A prompt such as "Please summarize the following news article: 'The latest technology announcement...'" is used.

[1190] Step 3:

[1191] When a user registers or logs in, the server obtains the user's interests and category information and stores it in a database.

[1192] Input: User registration information and interest categories

[1193] Output: User information stored in the database

[1194] Specific behavior:

[1195] When a user fills in a registration form with their email address and interest categories and submits it, the server receives this information and stores it in a database. For example, the information "Email address: example@example.com" and "Interest categories: Technology" are stored in the user table. In addition, the server tracks the user's browsing history and periodically updates their interest categories.

[1196] Step 4:

[1197] The server passes the text and voice data entered by the user to the emotion engine and analyzes the user's emotions.

[1198] Input: User feedback text or voice data

[1199] Output: Emotion analysis results

[1200] Specific behavior:

[1201] When a user submits feedback on a news summary, they provide text or voice data. The server passes this text data (e.g., "This news was very interesting") or voice data to the emotion engine. The emotion engine analyzes the data, identifies emotions such as positive or negative, and stores the results in the user profile.

[1202] Step 5:

[1203] The server generates and delivers customized news summaries to user terminals according to a daily, weekly, or monthly schedule.

[1204] Input: User profile information (interests, emotional data), summarized news articles

[1205] Output: A customized news summary delivered to the user

[1206] Specific behavior:

[1207] The server runs a news summary delivery task at 9:00 every morning, retrieving all users from the user table. Based on each user's profile (e.g., "Interests: Technology, Sentiment: Positive"), it selects the most appropriate news summary. It generates a URL link based on this summary and sends it to the user's device as a push notification. The user receives the push notification on their device and can click the link to view the customized news summary.

[1208] (Application example 2)

[1209] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1210] Conventional news delivery systems could customize and deliver news based on a user's interests, but they could not consider the user's emotions. As a result, news content may not be appropriate for the user, making it difficult to improve the user experience. Furthermore, there was a lack of user feedback to adjust summary generation. The present invention aims to solve these problems and efficiently provide news summaries customized based on a user's interests and emotions.

[1211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1212] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests and emotions, means for delivering the customized news summaries to the user, means for analyzing emotions from user feedback, and means for adjusting the news feed based on the analysis results, thereby enabling delivery of personalized news summaries tailored to the user's interests and emotions.

[1213] "Means for collecting news information" refers to the technology of obtaining the latest article data from multiple information sources via the Internet and storing it in a database.

[1214] "Means for summarizing collected news information using generative artificial intelligence" is an AI technology that analyzes collected news information, extracts important points, and generates summaries.

[1215] The "means for customizing summarized news information based on user interests and emotions" is a technology that adjusts news summaries taking into account the user's registration information, interest categories, and emotion analysis results.

[1216] The "means for delivering customized news summaries to users" refers to a technology for delivering customized news summaries to specific users via push notification, email, or the like.

[1217] "Means for analyzing emotions from user feedback" refers to a technology that analyzes text and voice feedback entered by the user and recognizes the user's emotions based on that.

[1218] "Means for adjusting news feeds based on analysis results" refers to technology that readjusts news feeds to make them more likely to interest users based on the results of sentiment analysis.

[1219] The present invention proposes a system for summarizing news information using generative artificial intelligence and providing a news summary customized according to a user's interests and emotions. Specific embodiments will be described in detail below.

[1220] News information gathering

[1221] The server periodically collects the latest news articles from multiple information sources (news websites, RSS feeds, etc.) via the Internet, and stores the collected news articles in a database for later processing.

[1222] Specific examples

[1223] For example, every morning at 9:00, the server accesses major news sites to retrieve the latest article data, then analyzes this data and stores the title, text, and publication date of each article in a database.

[1224] News summary generation

[1225] The server retrieves the collected news articles from the database and passes them to a generative artificial intelligence (e.g., the T5 model from the transformers library) to generate summaries, which are then stored back in the database.

[1226] Specific examples

[1227] For example, at 10:00 AM, the server retrieves the 10 latest news articles from the database and passes them to the GAI, which then summarizes each article and stores the summaries in the database.

[1228] User interest and sentiment analysis

[1229] The server takes the feedback (text and / or voice) provided by the user and passes it to an emotion engine (e.g., the TextBlob library) for sentiment analysis. The analysis results are stored in the user profile and used to customize news summaries. The server also stores the user's registration information and interest categories in a constantly updated database.

[1230] Specific examples

[1231] If a user enters feedback such as "This news was very interesting," the server passes the text to an emotion engine to analyze positive emotions and update the user profile.

[1232] Delivering customized news summaries

[1233] The server delivers customized news summaries to specific users via push notifications or emails, and delivery schedules can be set on a daily, weekly, or monthly basis, with the news summaries customized based on the user's interests and emotional data.

[1234] Specific examples

[1235] For example, every morning at 9:00 AM, the server retrieves all users and selects a customized news summary based on each user's profile. The generated summary is then sent to the user's device as a push notification in the form of a link.

[1236] Prompt Sentence Examples

[1237] Example prompt for a generative AI to generate a news summary:

[1238] Content: Economy

[1239] Summarize.

[1240] Input: The Nikkei Stock Average has risen significantly, reaching its highest level in the past 10 years.

[1241] Prompt: Summarize this content in 50 characters or less.

[1242] By the above means, the present invention can efficiently provide news summaries customized based on the user's interests and emotions, thereby improving the user experience and providing more personalized information.

[1243] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1244] Step 1:

[1245] To collect news information, the server accesses multiple information sources (news sites, RSS feeds, etc.) every morning at 9:00 and retrieves the latest article data. The input is a list of news site URLs, and the output is a database entry containing the article title, text, and publication date and time. Specifically, the server sends an HTTP request to retrieve the article data, analyzes it, and stores it in the database.

[1246] Step 2:

[1247] The server retrieves news articles collected from a database and passes them to a generative AI (for example, the T5 model from the Transformers library) to generate summaries. The input is the title and body of the news article retrieved from the database, and the output is a database entry containing the generated summary text. Specifically, the server sends the body of the news article to the generative AI and uses text such as "Summarize this content. The character limit is 50 characters or less" as a prompt for the AI ​​model.

[1248] Step 3:

[1249] The server customizes news summaries by taking into account the user's interests and emotions. The input is the user's registration information and emotion data obtained from their feedback, and the output is a customized news summary. Specifically, the server analyzes the user's profile data and selects the most suitable content for the user from the generated summaries.

[1250] Step 4:

[1251] The server delivers customized news summaries to the user's device. The inputs are the customized news summaries and the user's device information, and the output is the news summaries sent via push notification or email. Specifically, the server sends the summaries to the user's device via push notification or email server.

[1252] Step 5:

[1253] Users provide feedback on news summaries, and the server collects the feedback. The input is the user's feedback text or voice data, and the output is the sentiment analysis result. Specifically, the server passes the feedback data to a sentiment engine (e.g., TextBlob library) for sentiment analysis.

[1254] Step 6:

[1255] The server readjusts the news feed based on the results of the sentiment analysis of the feedback. The inputs are the analyzed sentiment data and news summary data, and the output is an updated news feed. Specifically, the server reflects the sentiment analysis results in the user profile and uses them for future news summary distribution.

[1256] The above are the processing steps of the system program that realizes the application example. These steps realize news summary delivery based on the user's interests and emotions.

[1257] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1258] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1259] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1260] [Fourth embodiment]

[1261] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1262] 7, a 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.

[1263] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1264] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1265] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1266] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1267] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1268] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1269] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1270] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1271] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1272] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1273] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1274] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[1275] News information gathering module

[1276] News article collection

[1277] The server collects the latest news articles from news sites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[1278] Specific examples

[1279] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[1280] Generative AI summary module

[1281] News Summary

[1282] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[1283] Specific examples

[1284] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[1285] User Management Module

[1286] User information management and analysis

[1287] The server stores users' registration information and interest categories in a database, and then analyzes their browsing history to identify news categories that interest each individual user.

[1288] Specific examples

[1289] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[1290] Summary Delivery Module

[1291] Customize and deliver summaries

[1292] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notifications.

[1293] Specific examples

[1294] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[1295] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI system creates summaries. By delivering summaries customized based on the user's interests, the system prevents confusion caused by information overload and enables efficient information acquisition.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The server schedules news gathering tasks, for example setting up a task to gather news articles from news sites and RSS feeds every morning at 9am.

[1299] Step 2:

[1300] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[1301] Step 3:

[1302] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[1303] Step 4:

[1304] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[1305] Step 5:

[1306] The server schedules generative AI summarization tasks, for example, setting up a task to summarize news articles collected over the last 24 hours every morning at 10:00.

[1307] Step 6:

[1308] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[1309] Step 7:

[1310] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[1311] Step 8:

[1312] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[1313] Step 9:

[1314] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[1315] Step 10:

[1316] The server customizes summaries based on the user's interests, using each user's profile information to select the most relevant news summaries.

[1317] Step 11:

[1318] The server schedules summary delivery tasks, for example, setting up a task to deliver a customized summary to users every morning at 9am.

[1319] Step 12:

[1320] The server sends a push notification to the user's device at the specified time, containing a link to a customized news summary for easy user access.

[1321] Step 13:

[1322] A user receives a push notification and clicks on a link, which takes them to a customized news summary.

[1323] Step 14:

[1324] The server updates the user's browsing history, recording which news summaries the user has viewed and reflecting this in the next summary customization.

[1325] Example 1

[1326] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1327] In modern society, a huge amount of news information is generated every day, and there is a demand for quickly and efficiently extracting and acquiring important information from it. Conventional news gathering systems can easily cause confusion due to information overload and overlook important points. Another problem is that it is difficult for users to easily acquire news information customized based on their own interests. There is a need for a system that can solve these issues and enable users to efficiently understand important news.

[1328] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1329] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on user interests, and means for delivering the customized news summaries to users, thereby enabling efficient collection and summarization of news information, customization based on user interests, and delivery of highly relevant news.

[1330] "Means for collecting news information" refers to a system for obtaining news articles from multiple information sources via a network and storing these articles in a database.

[1331] "Means for summarizing collected news information using generative artificial intelligence" refers to a system that extracts important points from collected news information, creates a summary based on these points, and saves the summary back in a database.

[1332] "Means for customizing summarized news information based on a user's interests" refers to a mechanism that identifies the news summaries most relevant to a user's interests based on the user's registration information, categories of interest, and browsing history.

[1333] "Means for delivering customized news summaries to users" refers to a mechanism for selecting customized news summaries according to a daily, weekly, or monthly schedule and delivering them to users' devices via push notifications.

[1334] "Multiple sources" refers to multiple online sources of news articles and information, such as news sites and RSS feeds.

[1335] "Database" refers to a digital information management system for organizing and storing collected news information, generated summaries, user information, etc.

[1336] "Generative AI" refers to artificial intelligence models (e.g., GPT-3) that are used to analyze news articles, extract key points, and generate summaries.

[1337] "User Registration Information" refers to information such as personal information and interest categories provided by a User when registering with the System.

[1338] "Viewing history" refers to historical data of news articles that a user has accessed within the system.

[1339] "Relevant news" refers to the news information that is most valuable to a user based on the user's interests and past browsing history.

[1340] "Push notification" refers to a communication method for sending information in real time from a server to a user's device.

[1341] This invention is a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items by day, week, or month. This system delivers news summaries in a format suited to the user, enabling information organization and efficient information acquisition.

[1342] News information gathering module

[1343] Hardware and Software Configuration

[1344] The server collects the latest news articles from multiple sources, such as news websites and RSS feeds. The server is connected to the Internet via a network interface. The collection task is run every morning at 9:00 a.m. using scheduling software (e.g., cron).

[1345] Specific operation example

[1346] Every morning at 9:00, the server sends an HTTP request to the news site's API to retrieve the latest article data. It then parses the retrieved data to extract the title, body text, and publication date and time of each article, and stores them in a database management system (e.g., MySQL).

[1347] Generative AI summary module

[1348] Hardware and Software Configuration

[1349] The server retrieves news articles from the database and passes them to a generative AI (e.g., GPT-3), which analyzes each article, extracts key points, and generates a summary.

[1350] Specific operation example

[1351] A server retrieves the 10 latest news articles from a database at 10 AM and passes them to a generative artificial intelligence using the following prompt:

[1352] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[1353] Article Title: (Article Title)

[1354] Article body: (Article body)

[1355] Generative artificial intelligence summarizes each article and stores the generated summaries in a database.

[1356] User Management Module

[1357] Hardware and Software Configuration

[1358] The server stores user registration information and interest categories in a database. Furthermore, the server analyzes users' browsing history to identify news categories that each user is interested in. This analysis is performed using a data analysis library (e.g., pandas).

[1359] Specific operation example

[1360] When a user registers, they enter their email address and the categories they are interested in. The server stores this information in a user table, and also tracks the user's browsing history and periodically updates the news categories they are interested in.

[1361] Summary Delivery Module

[1362] Hardware and Software Configuration

[1363] The server delivers news summaries according to a set schedule, such as daily, weekly, or monthly. Based on the user's interests, the server selects the most relevant news and delivers customized summaries to the user's device via push notifications. Push notifications are delivered using a notification service (e.g., Firebase Cloud Messaging).

[1364] Specific operation example

[1365] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[1366] In this way, the server efficiently collects news information, and the generative AI creates summaries. By delivering summaries customized based on the user's interests, confusion caused by information overload is prevented, enabling efficient information acquisition.

[1367] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1368] Step 1:

[1369] A server schedules news gathering tasks.

[1370] Input: Task Scheduler configuration information

[1371] Data processing / calculation: Set the task to start at a specified time (9:00 every morning).

[1372] Output: The state when the schedule is completed and the task is automatically executed at the specified time.

[1373] What it does: The server uses cron to set up a news gathering task to run every morning at 9am.

[1374] Step 2:

[1375] The server sends an HTTP request to the specified news site to retrieve article data.

[1376] Input: News site API endpoint

[1377] Data processing / calculation: Sending HTTP requests and receiving responses.

[1378] Output: Latest news article data

[1379] Specific operation: The server accesses a news site such as news-example.com, sends an API request, and receives the news data returned as a response.

[1380] Step 3:

[1381] The server analyzes the received news data and stores it in a database.

[1382] Input: News article data

[1383] Data processing / calculation: Parsing data and extracting fields (title, body, publication date).

[1384] Output: News articles stored in a database

[1385] What it does: The server parses the data using Python's BeautifulSoup, extracts the article title, body text, and publication date and time, and stores them in a MySQL database.

[1386] Step 4:

[1387] The server retrieves the latest news from the database and passes it on to the generative artificial intelligence.

[1388] Input: News articles from the database

[1389] Data processing / calculation: Select the latest news articles using SQL queries.

[1390] Output: News article passed to the generative AI

[1391] What it does: The server uses an SQL query to retrieve the 10 latest news articles from the database and passes them to the generative artificial intelligence (GPT-3).

[1392] Step 5:

[1393] Generative artificial intelligence analyzes news articles, generates summaries, and returns them to the server.

[1394] Input: News article data and prompt

[1395] Data processing / calculation: News article summary generation

[1396] Output: Generated news summary

[1397] Example prompt:

[1398] You are a journalist. Read the news article below and summarize the most important points in five sentences.

[1399] Article Title: (Article Title)

[1400] Article body: (Article body)

[1401] Specific operation: The server sends a news article and a prompt to the generative AI, which then generates a summary and replies.

[1402] Step 6:

[1403] The server stores the generated summaries in a database.

[1404] Input: News summaries generated by generative artificial intelligence

[1405] Data processing / calculation: Summary data storage processing

[1406] Output: News summaries stored in a database

[1407] Specific behavior: The server stores the generated news summaries in a database.

[1408] Step 7:

[1409] The user enters registration information and interest categories from the device.

[1410] Input: User registration information and interest categories

[1411] Data processing / calculation: Validating and saving input data

[1412] Output: User profile data

[1413] Specific operation: The user enters their email address and interest categories on the device's registration screen, which is then received by the server and stored in a database.

[1414] Step 8:

[1415] The server analyzes the user's browsing history and identifies news categories of interest.

[1416] Input: User's browsing history

[1417] Data processing / calculation: Analysis of browsing history and category identification

[1418] Output: Updated user profile

[1419] What it does: The server uses Python's pandas to analyze browsing history, identify news categories of interest, and update the user profile.

[1420] Step 9:

[1421] The server selects customized news summaries and delivers them to users via push notifications.

[1422] Input: User profile and news summary

[1423] Data processing / computation: Selecting news summaries and generating notifications based on user interests

[1424] Output: Push notification sent to user device

[1425] How it works: The server uses Firebase Cloud Messaging to generate a URL link for a news summary customized based on the user's profile and sends it to the user's device via a push notification.

[1426] (Application example 1)

[1427] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1428] In today's world, the problem of information overload is becoming more serious, making it difficult to efficiently obtain important information from the vast amount of news available. Furthermore, there is a lack of efficient ways to collect and organize only the news that interests users. Meanwhile, there is a demand for improving the user experience by delivering relevant news summaries in a timely manner.

[1429] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1430] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, and means for delivering the customized news summary to the terminal by push notification, thereby enabling highly relevant news information based on the user's interests to be efficiently summarized, further customized, and delivered in a timely manner.

[1431] "News information" refers to information such as articles, reports, commentaries, and special features that contain general news content.

[1432] "Generative artificial intelligence" is a type of artificial intelligence designed to understand, analyze, and summarize collected data.

[1433] A "summary" is a short summary of important points extracted from news information.

[1434] "User interests" refer to the specific topics or categories in which a user is interested.

[1435] "Customization" is the process of adapting or modifying content to suit the needs and interests of a particular user.

[1436] "Device" refers to a mobile device such as a smartphone or tablet.

[1437] "Push notification" is a function that sends information from a server to a device in real time.

[1438] A "server" is a computer system that processes data over a network and provides services to other devices and users.

[1439] "Means" are the methods or tools used to achieve a particular goal.

[1440] The present invention provides a system that uses generative artificial intelligence to summarize news information and delivers customized news summaries based on the user's interests to a terminal via push notification. The following components are included as means necessary to implement the present invention.

[1441] System Components

[1442] 1. How to gather news information:

[1443] The server retrieves articles from multiple news sources over a network and stores these articles in a database. The server accesses the news sources at designated times to collect the latest articles.

[1444] 2. Generative AI Summarization:

[1445] The server retrieves the collected news articles from the database and passes them to a generative AI (e.g., GPT-4), which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[1446] 3. Customization based on user interests:

[1447] The server stores users' registration information and interest categories in a database, and analyzes their browsing history to identify the news categories that each user is interested in. Based on this information, the generated news summaries are customized.

[1448] 4. Push notification delivery method:

[1449] The server delivers customized news summaries to devices via push notifications based on a daily, weekly, or monthly schedule. For example, the server executes a summary delivery task every morning at 9:00, selects news summaries based on each user's profile, and sends them to devices via push notifications.

[1450] Hardware and Software

[1451] Hardware:

[1452] Server: A central system for collecting and processing data from multiple news sources.

[1453] Device: A mobile device such as a smartphone or tablet.

[1454] software:

[1455] Database: A database system such as MySQL or PostgreSQL.

[1456] Generative AI models: Artificial intelligence models for news article summarization, such as GPT-4.

[1457] Server program: Software that implements the functions of data collection, summary generation, customization, and push notification delivery.

[1458] Push notification services: Third-party push notification service providers.

[1459] Specific examples of processing

[1460] The server accesses news sources every morning at 9 a.m. and retrieves the latest articles via API. It then stores this data in a database and passes it to a generative AI, which summarizes each article and stores the summaries in a database. The server then selects the most relevant news summaries based on the user's interests and sends them to the user's smartphone using a push notification service.

[1461] Examples of prompt statements

[1462] The following prompt sentences are input into a generative artificial intelligence to summarize a news article.

[1463] Prompt statement:

[1464] "Summarize the following news article. Please use a maximum of 50 characters, a minimum of 25, and include the key points. Article content: {News article text}"

[1465] The above is a specific embodiment of the present invention. This system efficiently delivers news summaries based on the user's interests, preventing confusion caused by information overload and enabling efficient information acquisition.

[1466] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1467] Step 1:

[1468] The server accesses the news source every morning at 9:00 to retrieve the latest news articles. The input is the news source's API endpoint, and the output is the retrieved news article data. Specifically, the server sends an API request and receives a response. The received response data is analyzed to extract the article title, text, publication date, etc.

[1469] Step 2:

[1470] The server stores the extracted news article data in a database. The input is information such as the news article title, body text, and publication date, and the output is a news article entry stored in the database. Specifically, the information for each article is added as a new record in the "News Articles" table.

[1471] Step 3:

[1472] The server retrieves the latest news articles from the database at 10:00 AM and passes them to the generative AI. The input is multiple news article data retrieved from the database, and the output is a prompt to be input to the generative AI. Specifically, the text of each news article is used to generate the following prompt: "Please summarize the following news article. Please summarize in a maximum of 50 characters, a minimum of 25 characters, and include the key points. Article content: {News article text}"

[1473] Step 4:

[1474] A generative AI model summarizes a news article based on a prompt. The input is the prompt, and the output is a generated summary. Specifically, a generative AI model (e.g., GPT-4) analyzes the prompt and generates a summary. This generated summary is then stored in a database.

[1475] Step 5:

[1476] The server retrieves user information and interest categories from a database and customizes news summaries based on each user's interests. The input is the user's interest data and the generated news summaries, and the output is the customized news summaries. Specifically, it selects relevant news summaries based on each user's profile information and browsing history.

[1477] Step 6:

[1478] The server delivers customized news summaries to devices via push notifications. The input is the customized news summary and the user's device information, and the output is a push notification sent to the user's device. Specifically, a push notification service is used to notify each user of the selected news summary on their smartphone. Users can view detailed article content by tapping the notification.

[1479] The above are the specific processing steps of the system that realizes the application example. At each step, data processing and data calculation are performed based on the input data, thereby making it possible to obtain the required output.

[1480] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1481] This invention combines a system that uses generative artificial intelligence to summarize news information and provide summaries of major news items on a daily, weekly, or monthly basis with an emotion engine that recognizes the user's emotions. This system delivers news summaries in a format that is appropriate for the user, enabling information organization and efficient information acquisition.

[1482] News information gathering module

[1483] News article collection

[1484] The server collects the latest news articles from news websites and RSS feeds. For example, you can set up a task to run every morning at 9:00 AM, accessing major news sources and retrieving news articles. This news information is stored in a database for further processing.

[1485] Specific examples

[1486] The server accesses the news site "news-example.com" every morning at 9:00 and retrieves the latest article data. It then analyzes this data and stores the title, body text, and publication date and time of each article in a database.

[1487] Generative AI summary module

[1488] News Summary

[1489] The server retrieves the collected news articles from the database and passes them to the generative AI, which analyzes each article, extracts key points, and generates a summary, which is then stored back in the database.

[1490] Specific examples

[1491] At 10:00 AM, the server retrieves the 10 most recent news articles from the database and passes them to the generative AI, which then summarizes each article and stores the generated summaries in the database.

[1492] User Management Module

[1493] User information management and analysis

[1494] The server stores users' registration information and interest categories in a database, and also analyzes users' browsing history to identify news categories that interest individual users.

[1495] Specific examples

[1496] When a user registers, they enter their email address and the categories they are interested in, and the server saves this information in the user table. The server tracks browsing history, analyzes the news categories they are interested in, and periodically updates them.

[1497] Emotion Engine Module

[1498] Emotion Analysis

[1499] The server receives user input data (text and voice) and passes it to the emotion engine, which analyzes the data and recognizes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[1500] Text-based sentiment analysis

[1501] When a user enters feedback on a news summary, the server passes the text data to an emotion engine for emotion analysis.

[1502] Specific examples

[1503] When a user enters feedback such as "This news was very interesting," the server passes the text to the emotion engine, which analyzes the text for positive emotions and updates the user profile.

[1504] Voice-based sentiment analysis

[1505] When a user comments on a news summary through a voice interface, the server passes the voice data to an emotion engine for emotion analysis.

[1506] Specific examples

[1507] When a user gives voice feedback such as "This news was very helpful," the server passes the voice data to the emotion engine, which analyzes the positive emotion and updates the user profile.

[1508] Summary Delivery Module

[1509] Customize and deliver summaries

[1510] The server delivers news summaries according to a set schedule, which can be daily, weekly, or monthly. Based on the user's interests and emotional data, the server selects the most relevant news and customizes the summaries. The customized summaries are then delivered to the user's device via push notification.

[1511] Specific examples

[1512] The server runs a summary delivery task every morning at 9:00, retrieves all users from the user table, selects a customized news summary based on each user's profile, generates it as a URL link, and sends the link to the user's device via push notification.

[1513] The above is a specific embodiment of the present invention. The server collects news from multiple news sources, and a generative AI creates summaries. An emotion engine is used to analyze the user's emotions, and a more customized summary is delivered. This allows users to efficiently obtain important news.

[1514] The processing flow will be explained below.

[1515] Step 1:

[1516] The server schedules the news gathering task: Set up a task that gathers news articles from news sites and RSS feeds every morning at 9am.

[1517] Step 2:

[1518] The server accesses news sites and RSS feeds at designated times to retrieve the latest news articles from major news sources in HTML or XML format.

[1519] Step 3:

[1520] The server analyzes the news data it has acquired, extracting information such as the article title, text, and publication date based on the HTML structure and XML tags.

[1521] Step 4:

[1522] The server stores the analyzed news data in a database. The news article table stores the title, text, publication date, etc. of each article.

[1523] Step 5:

[1524] The server schedules the generative AI summarization task. It sets up a task to pass the news articles collected in the last 24 hours to the generative AI every morning at 10:00.

[1525] Step 6:

[1526] The server retrieves the latest news articles from the database, and queries the news articles collected over a specified period.

[1527] Step 7:

[1528] The server passes the retrieved news article to the generative AI and instructs it to generate a summary. The generative AI analyzes the news article, extracts key points, and creates a summary.

[1529] Step 8:

[1530] The server saves the generated summary in the database, and saves information such as the article ID, summary text, and creation date and time in the summary table.

[1531] Step 9:

[1532] The server performs user management tasks, such as identifying news categories of interest based on user registration information and browsing history.

[1533] Step 10:

[1534] The server creates customized news summaries based on the user profile, taking into account the user's browsing history and interest categories to select the most relevant news summaries.

[1535] Step 11:

[1536] The user provides feedback on the news summary, which can be in text or audio format.

[1537] Step 12:

[1538] The server obtains the user's feedback data, and receives the text data or voice data entered by the user.

[1539] Step 13:

[1540] The server passes the feedback data to the emotion engine, which analyzes the text and voice data to recognize the user's emotions.

[1541] Step 14:

[1542] The server saves the analysis results obtained from the emotion engine in the user profile, and updates the profile with the user's emotional tendencies based on the analyzed emotion data.

[1543] Step 15:

[1544] The server takes the emotion data into account the next time it delivers a news summary, and combines the user's emotion data with the interest data to create a more customized summary.

[1545] Step 16:

[1546] The server delivers news summaries according to a daily, weekly, or monthly schedule, and sends optimized summaries to users' devices via push notifications at specified times.

[1547] Step 17:

[1548] A user receives a push notification and clicks on a link to a news summary, which displays a customized news summary.

[1549] Step 18:

[1550] The server updates the user's browsing history, recording the information about the news summaries the user has viewed and reflecting it in future summary customizations.

[1551] Example 2

[1552] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1553] Conventional news distribution systems provide uniform news summaries without considering the user's interests or emotions, making it difficult to efficiently provide optimal information to individual users. Another issue is the time required to collect a large number of news articles and extract important information.

[1554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1555] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests, means for recognizing the user's emotions and adjusting the summaries based on the emotions, and means for delivering customized news summaries to the user, thereby enabling efficient delivery of customized news summaries that reflect the user's interests and emotions.

[1556] A "means for collecting news information" is a means for obtaining news articles from multiple news sources via a network and storing the news articles in a database.

[1557] "Generative artificial intelligence" refers to advanced natural language processing models, such as GPT-4, that are used to summarize collected news information.

[1558] The "means for customizing summarized news information based on the user's interests" is a means for analyzing the user's registration information, browsing history, etc., and adjusting the news summary to suit the user's interests.

[1559] "Means for recognizing user emotions and tailoring summaries based on those emotions" refers to means for analyzing user input data (text or voice) to identify emotions and customizing news summaries based on that emotional data.

[1560] "Means for delivering customized news summaries to users" means means for generating news summaries that reflect the user's interests and emotions based on a specific schedule and delivering them to the user's device via push notification or other means.

[1561] The present invention is a system that uses generative artificial intelligence to summarize news information and provide users with customized news summaries. This system collects news information, creates summaries using generative artificial intelligence, and delivers summaries customized based on the user's interests and emotions, allowing users to efficiently obtain important news information.

[1562] News information gathering module

[1563] News article collection

[1564] The server accesses news sites and RSS feeds every morning at 9:00 to collect the latest news articles. The server uses the APIs of major news sites to obtain a list of the latest news articles, then analyzes the title, text, and publication date and time of the retrieved news articles, and stores this data in a database for later processing.

[1565] Specific examples

[1566] The server sends a request to the API endpoint of the "news source" to get a list of new articles. For example, if the article title is "Latest Technology Announcement," the body text is "New Technology...," and the publication date is "2023-10-01," it stores these in the database.

[1567] Generative AI summary module

[1568] News summary generation

[1569] At 10:00 AM, the server retrieves the latest news articles from the database and passes them to a generative AI (e.g., GPT-4) to generate summaries, which are then stored in the database.

[1570] Specific examples

[1571] The server retrieves a news article titled "Latest Technology Announcement" from the database at 10:00 AM. It then inputs the prompt "Please summarize the following news article: 'Latest Technology Announcement...'" into the generative AI model, and stores the resulting summary "New technology was announced" in the database.

[1572] User Management Module

[1573] User information management and analysis

[1574] The server receives the user's registration information, stores the user's interest categories in a database, and tracks the user's browsing history and analyzes it to identify the news categories that interest the user.

[1575] Specific examples

[1576] When a user fills in the registration form with "email address: example@example.com" and "interest category: technology", the server stores this information in the user table. After that, every time the user visits a news summary page, the server tracks the user's browsing history and analyzes their interest categories.

[1577] Emotion Engine Module

[1578] Emotion Analysis

[1579] The server passes the text and voice data entered by the user to the emotion engine, which analyzes the user's emotions. The analysis results are stored in the user profile and used to customize news summaries.

[1580] Text-based sentiment analysis example

[1581] If a user enters feedback such as "This news was very informative," the server passes the text data to the emotion engine, which analyzes the text for positive emotions and stores the results in the user profile.

[1582] Voice-based emotion analysis example

[1583] If a user provides voice feedback such as "This news was really helpful," the server passes the voice data to the emotion engine, which analyzes the voice, identifies positive emotions, and stores the results in the user profile.

[1584] Summary Delivery Module

[1585] Customize and deliver summaries

[1586] The server delivers customized news summaries to users on a daily, weekly, or monthly schedule, primarily via push notifications sent directly to their devices.

[1587] Specific examples

[1588] The server executes a summary delivery task every morning at 9:00 and retrieves all users from the user table. Based on User A's profile, which states "Interest: Technology, Emotion: Positive," the server selects a summary of the "Latest Technology Announcement" and generates it as a URL link. This link is then sent to User A's device via a push notification.

[1589] The above is a specific embodiment of the present invention. The server collects news information from multiple news sources, and the generative AI generates summaries. Furthermore, an emotion engine is used to analyze the user's emotions, and the news summaries are customized based on the analysis results, allowing users to efficiently obtain news that is important and relevant to them.

[1590] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1591] Step 1:

[1592] The server accesses news sources (news sites and RSS feeds) every morning at 9:00 and retrieves a list of the latest news articles.

[1593] Input: News source URL or API endpoint

[1594] Output: A list of news articles

[1595] Specific behavior:

[1596] The server sends HTTP requests to pre-configured news source URLs or API endpoints, parses the news article data received in response, which is often provided in JSON or XML format, extracts the retrieved news articles (title, body text, publication date and time), and stores them in a database.

[1597] Step 2:

[1598] The server retrieves the latest news articles from the database at 10 a.m. and passes them to a generative artificial intelligence to generate summaries.

[1599] Input: News articles stored in a database

[1600] Output: A summarized news article

[1601] Specific behavior:

[1602] The server retrieves multiple (e.g., 10) recent news articles from the database. It then creates a prompt to summarize the content of each news article and passes it to a generative AI (e.g., GPT-4). The generative AI generates a summary of each article based on the prompt and stores the summary results back in the database. A prompt such as "Please summarize the following news article: 'The latest technology announcement...'" is used.

[1603] Step 3:

[1604] When a user registers or logs in, the server obtains the user's interests and category information and stores it in a database.

[1605] Input: User registration information and interest categories

[1606] Output: User information stored in the database

[1607] Specific behavior:

[1608] When a user fills in a registration form with their email address and interest categories and submits it, the server receives this information and stores it in a database. For example, the information "Email address: example@example.com" and "Interest categories: Technology" are stored in the user table. In addition, the server tracks the user's browsing history and periodically updates their interest categories.

[1609] Step 4:

[1610] The server passes the text and voice data entered by the user to the emotion engine and analyzes the user's emotions.

[1611] Input: User feedback text or voice data

[1612] Output: Emotion analysis results

[1613] Specific behavior:

[1614] When a user submits feedback on a news summary, they provide text or voice data. The server passes this text data (e.g., "This news was very interesting") or voice data to the emotion engine. The emotion engine analyzes the data, identifies emotions such as positive or negative, and stores the results in the user profile.

[1615] Step 5:

[1616] The server generates and delivers customized news summaries to user terminals according to a daily, weekly, or monthly schedule.

[1617] Input: User profile information (interests, emotional data), summarized news articles

[1618] Output: A customized news summary delivered to the user

[1619] Specific behavior:

[1620] The server runs a news summary delivery task at 9:00 every morning, retrieving all users from the user table. Based on each user's profile (e.g., "Interests: Technology, Sentiment: Positive"), it selects the most appropriate news summary. It generates a URL link based on this summary and sends it to the user's device as a push notification. The user receives the push notification on their device and can click the link to view the customized news summary.

[1621] (Application example 2)

[1622] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1623] Conventional news delivery systems could customize and deliver news based on a user's interests, but they could not consider the user's emotions. As a result, news content may not be appropriate for the user, making it difficult to improve the user experience. Furthermore, there was a lack of user feedback to adjust summary generation. The present invention aims to solve these problems and efficiently provide news summaries customized based on a user's interests and emotions.

[1624] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1625] In this invention, the server includes means for collecting news information, means for summarizing the collected news information using generative artificial intelligence, means for customizing the summarized news information based on a user's interests and emotions, means for delivering the customized news summaries to the user, means for analyzing emotions from user feedback, and means for adjusting the news feed based on the analysis results, thereby enabling delivery of personalized news summaries tailored to the user's interests and emotions.

[1626] "Means for collecting news information" refers to the technology of obtaining the latest article data from multiple information sources via the Internet and storing it in a database.

[1627] "Means for summarizing collected news information using generative artificial intelligence" is an AI technology that analyzes collected news information, extracts important points, and generates summaries.

[1628] The "means for customizing summarized news information based on user interests and emotions" is a technology that adjusts news summaries taking into account the user's registration information, interest categories, and emotion analysis results.

[1629] The "means for delivering customized news summaries to users" refers to a technology for delivering customized news summaries to specific users via push notification, email, or the like.

[1630] "Means for analyzing emotions from user feedback" refers to a technology that analyzes text and voice feedback entered by the user and recognizes the user's emotions based on that.

[1631] "Means for adjusting news feeds based on analysis results" refers to technology that readjusts news feeds to make them more likely to interest users based on the results of sentiment analysis.

[1632] The present invention proposes a system for summarizing news information using generative artificial intelligence and providing a news summary customized according to a user's interests and emotions. Specific embodiments will be described in detail below.

[1633] News information gathering

[1634] The server periodically collects the latest news articles from multiple information sources (news websites, RSS feeds, etc.) via the Internet, and stores the collected news articles in a database for later processing.

[1635] Specific examples

[1636] For example, every morning at 9:00, the server accesses major news sites to retrieve the latest article data, then analyzes this data and stores the title, text, and publication date of each article in a database.

[1637] News summary generation

[1638] The server retrieves the collected news articles from the database and passes them to a generative artificial intelligence (e.g., the T5 model from the transformers library) to generate summaries, which are then stored back in the database.

[1639] Specific examples

[1640] For example, at 10:00 AM, the server retrieves the 10 latest news articles from the database and passes them to the GAI, which then summarizes each article and stores the summaries in the database.

[1641] User interest and sentiment analysis

[1642] The server takes the feedback (text and / or voice) provided by the user and passes it to an emotion engine (e.g., the TextBlob library) for sentiment analysis. The analysis results are stored in the user profile and used to customize news summaries. The server also stores the user's registration information and interest categories in a constantly updated database.

[1643] Specific examples

[1644] If a user enters feedback such as "This news was very interesting," the server passes the text to an emotion engine to analyze positive emotions and update the user profile.

[1645] Delivering customized news summaries

[1646] The server delivers customized news summaries to specific users via push notifications or emails, and delivery schedules can be set on a daily, weekly, or monthly basis, with the news summaries customized based on the user's interests and emotional data.

[1647] Specific examples

[1648] For example, every morning at 9:00 AM, the server retrieves all users and selects a customized news summary based on each user's profile. The generated summary is then sent to the user's device as a push notification in the form of a link.

[1649] Prompt Sentence Examples

[1650] Example prompt for a generative AI to generate a news summary:

[1651] Content: Economy

[1652] Summarize.

[1653] Input: The Nikkei Stock Average has risen significantly, reaching its highest level in the past 10 years.

[1654] Prompt: Summarize this content in 50 characters or less.

[1655] By the above means, the present invention can efficiently provide news summaries customized based on the user's interests and emotions, thereby improving the user experience and providing more personalized information.

[1656] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1657] Step 1:

[1658] To collect news information, the server accesses multiple information sources (news sites, RSS feeds, etc.) every morning at 9:00 and retrieves the latest article data. The input is a list of news site URLs, and the output is a database entry containing the article title, text, and publication date and time. Specifically, the server sends an HTTP request to retrieve the article data, analyzes it, and stores it in the database.

[1659] Step 2:

[1660] The server retrieves news articles collected from a database and passes them to a generative AI (for example, the T5 model from the Transformers library) to generate summaries. The input is the title and body of the news article retrieved from the database, and the output is a database entry containing the generated summary text. Specifically, the server sends the body of the news article to the generative AI and uses text such as "Summarize this content. The character limit is 50 characters or less" as a prompt for the AI ​​model.

[1661] Step 3:

[1662] The server customizes news summaries by taking into account the user's interests and emotions. The input is the user's registration information and emotion data obtained from their feedback, and the output is a customized news summary. Specifically, the server analyzes the user's profile data and selects the most suitable content for the user from the generated summaries.

[1663] Step 4:

[1664] The server delivers customized news summaries to the user's device. The inputs are the customized news summaries and the user's device information, and the output is the news summaries sent via push notification or email. Specifically, the server sends the summaries to the user's device via push notification or email server.

[1665] Step 5:

[1666] Users provide feedback on news summaries, and the server collects the feedback. The input is the user's feedback text or voice data, and the output is the sentiment analysis result. Specifically, the server passes the feedback data to a sentiment engine (e.g., TextBlob library) for sentiment analysis.

[1667] Step 6:

[1668] The server readjusts the news feed based on the results of the sentiment analysis of the feedback. The inputs are the analyzed sentiment data and news summary data, and the output is an updated news feed. Specifically, the server reflects the sentiment analysis results in the user profile and uses them for future news summary distribution.

[1669] The above are the processing steps of the system program that realizes the application example. These steps realize news summary delivery based on the user's interests and emotions.

[1670] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1671] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1672] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1673] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1674] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1675] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1676] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1677] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1678] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1679] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1680] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1681] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1682] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1683] 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.

[1684] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1685] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1686] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1687] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1688] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1689] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1690] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1691] The following is further disclosed regarding the above embodiment.

[1692] (Claim 1)

[1693] A means of gathering news information;

[1694] A means for summarizing collected news information using generative artificial intelligence;

[1695] means for customizing the summarized news information based on the user's interests;

[1696] means for delivering customized news summaries to users;

[1697] A system including:

[1698] (Claim 2)

[1699] 2. The system of claim 1, wherein the means for collecting news information acquires news articles from a plurality of news sources via a network and stores these news articles in a database.

[1700] (Claim 3)

[1701] The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information and creates a summary based on these points.

[1702] "Example 1"

[1703] (Claim 1)

[1704] A means of gathering news information;

[1705] A means for summarizing collected news information using generative artificial intelligence;

[1706] means for customizing the summarized news information based on the user's interests;

[1707] means for delivering customized news summaries to users;

[1708] A system including:

[1709] (Claim 2)

[1710] 2. The system according to claim 1, wherein the means for collecting news information acquires articles from a plurality of information sources via a network and stores these articles in a database.

[1711] (Claim 3)

[1712] The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information, creates a summary based on these points, and re-stores the generated summary in the database.

[1713] (Claim 4)

[1714] A means for storing user registration information and categories of interest in a database, and further analyzing user browsing history to identify categories of interest to individual users;

[1715] 10. The system of claim 1, comprising:

[1716] (Claim 5)

[1717] 10. The system of claim 1, wherein the means for delivering customized summaries according to a daily, weekly, or monthly schedule selects the most relevant news based on the user's interests and delivers them to the device via push notification.

[1718] "Application Example 1"

[1719] (Claim 1)

[1720] A means of gathering news information;

[1721] A means for summarizing collected news information using generative artificial intelligence;

[1722] means for customizing the summarized news information based on the user's interests;

[1723] A means to deliver customized news summaries to devices via push notifications;

[1724] A system including:

[1725] (Claim 2)

[1726] 2. The system according to claim 1, wherein the means for collecting news information acquires articles from a plurality of information sources via a network and stores these articles in a database.

[1727] (Claim 3)

[1728] The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information and creates a summary based on these points.

[1729] "Example 2: Combining Emotion Engines"

[1730] (Claim 1)

[1731] A means of gathering news information;

[1732] A means for summarizing collected news information using generative artificial intelligence;

[1733] means for customizing the summarized news information based on the user's interests;

[1734] means for recognizing a user's emotion and adjusting the summary based on the emotion;

[1735] means for delivering customized news summaries to users;

[1736] A system including:

[1737] (Claim 2)

[1738] 2. The system of claim 1, wherein the means for collecting news information acquires news articles from a plurality of news sources via a network and stores these news articles in a database.

[1739] (Claim 3)

[1740] The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information and creates a summary based on these points.

[1741] "Application example 2 when combining emotion engines"

[1742] (Claim 1)

[1743] A means of gathering news information;

[1744] A means for summarizing collected news information using generative artificial intelligence;

[1745] means for customizing the summarized news information based on the user's interests and emotions;

[1746] means for delivering customized news summaries to users;

[1747] A means for analyzing emotions from user feedback;

[1748] a means of adjusting the news feed based on the analysis results;

[1749] A system including:

[1750] (Claim 2)

[1751] 2. The system according to claim 1, wherein the means for collecting news information acquires article data from a plurality of information sources via a network and stores the article data in a database.

[1752] (Claim 3)

[1753] The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information and creates a summary based on these points. [Explanation of symbols]

[1754] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of gathering news information; A means for summarizing collected news information using generative artificial intelligence; means for customizing the summarized news information based on the user's interests; means for delivering customized news summaries to users; A system including:

2. 2. The system of claim 1, wherein the means for collecting news information acquires news articles from a plurality of news sources via a network and stores these news articles in a database.

3. The system of claim 1, wherein the means for summarizing collected news information using generative artificial intelligence extracts important points from the collected news information and creates a summary based on these points.

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