System

Abstract

A system is provided.SOLUTION: A system comprising: a user information collection unit; an analysis unit configured to analyze user behavior information collected by the user information collection unit; a selection unit configured to select a news article based on user interest information analyzed by the analysis unit; a summarization unit configured to summarize the news article selected by the selection unit; a video generation unit configured to visualize the news article summarized by the summarization unit; and a distribution unit configured to distribute the video generated by the video generation unit at a designated time.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 modern society, information overload makes it difficult for users to quickly obtain the news information they need. This problem is particularly serious for adults, business people, and students with busy lifestyles, and there is a need for efficient ways to obtain information. Conventional news distribution services lack mechanisms for providing news tailored to the individual interests of users, resulting in excessive or unnecessary information for users, which causes stress in obtaining and managing information. To solve these problems, a system is needed that can efficiently provide news summaries based on the user's individual interests. [Means for solving the problem]

[0005] The present invention provides a system that identifies a user's interests by collecting and analyzing user behavioral data. Specifically, the system includes a user information collection means, an analysis means, a selection means, a summarization means, a video generation means, and a distribution means. The user information collection means collects behavioral data such as the user's browsing history, click events, and browsing time, and the analysis means identifies the user's interests using a machine learning model. The selection means then selects appropriate news articles based on the identified interests, and the summarization means summarizes the articles into content that can be distributed in a short period of time. The video generation means integrates the summarized news articles with text, images, and audio narration to create a video, and the distribution means distributes the video at a time selected by the user (e.g., morning, afternoon, or evening), allowing the user to efficiently obtain only the information they need. Furthermore, the system allows users to quickly keep up with the latest news in their areas of interest, reducing the stress of managing information.

[0006] The "user information collection means" is a mechanism for collecting user behavior data, and acquires information such as browsing history, click events, and browsing time.

[0007] "Analysis means" refers to the mechanisms used to analyze collected user behavior data and identify user interests and concerns, including text analysis and machine learning models.

[0008] The "selection means" is a mechanism for selecting appropriate news articles based on the user's interest information identified by the analysis means.

[0009] The "summarization method" is a mechanism for summarizing selected news articles into content that can be disseminated in a short amount of time. This uses a generative AI model.

[0010] The "video generation means" is a mechanism for visualizing summarized news articles, integrating text, related images, and audio narration to generate a video.

[0011] The "distribution means" is a mechanism for distributing the generated video at a time slot designated by the user, allowing the user to watch the news efficiently. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] Overall overview

[0034] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain the news information they need efficiently and quickly.

[0035] Program Structure

[0036] User information collection method

[0037] The server uses a user information collection means to collect user behavior data, which collects information such as the URL of the article the user viewed, the viewing time, the link clicked, and the scrolling speed, and stores the collected information in a database.

[0038] Analysis means

[0039] The server analyzes the collected behavioral data using analytics, such as text analytics and machine learning models, to identify which topics users are interested in.

[0040] Selection method

[0041] The server selects news articles based on the user's interest information identified by the analysis means, and filters the most recent articles in the news database to select the articles most relevant to the user.

[0042] Summary tools

[0043] The server then summarises the news articles selected by the selection means using a generative AI model (e.g., GPT-3) to create a short summary of each article that can be conveyed in under one minute.

[0044] Image generation means

[0045] The server visualizes the summarized news articles using a video generation means that integrates the text, associated images, and audio narration to create a short video.

[0046] Delivery Method

[0047] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0048] Specific examples

[0049] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0050] Collection Phase

[0051] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data is recorded.

[0052] Analysis Phase

[0053] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0054] Selection Phase

[0055] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0056] Summary Phase

[0057] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0058] Image generation phase

[0059] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0060] Delivery Phase

[0061] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0062] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The server collects user behavior data in real time. When a user browses a news site, it records the article's URL, viewing time, click events, scrolling information, etc. For example, if a user browses an article on "latest smartphone technology" and stays there for a long time, that information is stored in a database.

[0066] Step 2:

[0067] The server analyzes the collected behavioral data, using machine learning models and text analysis engines to identify which topics the user is interested in. For example, based on the user's past browsing history, it may determine that the user is interested in technology articles.

[0068] Step 3:

[0069] Based on the analysis results, the server selects relevant latest news articles from the news database, narrowing down the articles to those that match the user's interests, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0070] Step 4:

[0071] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the original article content is summarized in a short, approximately one-minute sentence. For example, "Announcement of new AI technology" is summarized as "The latest AI technology has been announced. As a result..."

[0072] Step 5:

[0073] The server then creates a video from the summarized news article. It uses a video generation framework to integrate the summary text, related images, and audio narration to create a video of less than one minute. For example, it adds images and visual effects related to the summary, "The latest AI technology has been announced."

[0074] Step 6:

[0075] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[0076] Step 7:

[0077] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[0078] Step 8:

[0079] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[0080] These processing steps allow the user to efficiently obtain the latest news that interests him or her in a short amount of time.

[0081] Example 1

[0082] 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."

[0083] In modern society, users are exposed to a huge amount of information, making it difficult to efficiently obtain the news information they need. Furthermore, because users do not have the time to browse news articles individually, there is a growing need to quickly grasp important information. However, there is a lack of systems that can select relevant news based on users' interests and behavioral history, and then present it in a format that is easily understandable.

[0084] 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.

[0085] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This allows the server to select news of interest based on the user's behavior data, summarize it, and visualize it, thereby enabling the user to efficiently obtain highly relevant news information in a short amount of time.

[0086] The "user information collection means" is a means for collecting behavioral data such as a user's browsing history, click events, browsing time, and scrolling speed.

[0087] The "analysis means" is a means for analyzing collected user behavior data using a machine learning model to identify user interests.

[0088] The "selection means" is a means for selecting news articles based on the user's interest information identified by the analysis means.

[0089] The "summarization method" is a method for summarizing selected news articles into short sentences using a generative AI model.

[0090] The "video generation means" is a means for creating a video of a summarized news article by integrating text, related images, and audio narration.

[0091] The "distribution means" is a means for distributing the generated video in accordance with a specific time period (morning, afternoon, or evening) selected by the user.

[0092] Overall overview

[0093] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes the news, and delivers it to the user. This system allows users to obtain the news information they need efficiently and quickly.

[0094] Program Structure

[0095] User information collection method

[0096] The server uses a user information collection means to collect user behavior data. This means collects information such as the URL of the article the user viewed, the viewing time, the links clicked, and the scrolling speed, and stores it in a database. Specific software used may include a web tracking tool or a data collection library.

[0097] Analysis means

[0098] The server analyzes the collected behavioral data using analytics, which can include text analysis and machine learning models to identify topics that users are interested in. Machine learning algorithms can be implemented using programming languages ​​such as Python and R.

[0099] Selection method

[0100] The server selects news articles based on the user's interests identified by the analysis means, filtering the most relevant articles for the user from the latest articles in a news database (e.g., MongoDB).

[0101] Summary tools

[0102] The server summarizes the news articles selected by the selection means using the summarization means. Using a generative AI model (e.g., GPT-3), each article is converted into a short summary that can be explained in less than one minute. An example of a prompt sentence is, "Please summarize an article about the latest AI technology announcement. Please write a short sentence that can be explained in less than one minute."

[0103] Image generation means

[0104] The server then visualizes the summarized news articles using a video generation tool that integrates the text, related images, and audio narration to create short videos. FFmpeg is used as the video generation software.

[0105] Delivery Method

[0106] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). For example, if the user selects "morning news," the server notifies the user that the generated video will be available for viewing at 7:00 a.m. After receiving the notification, the user can view the video.

[0107] Specific examples

[0108] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0109] Collection Phase

[0110] The server collects user A's behavioral data (article browsing history, click events, browsing time, scrolling speed, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data will be recorded.

[0111] Analysis Phase

[0112] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0113] Selection Phase

[0114] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0115] Summary Phase

[0116] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0117] Image generation phase

[0118] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0119] Delivery Phase

[0120] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0121] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1:

[0124] The server uses the user information collection means to collect user behavior data.

[0125] How it works: When a user visits a news site, the server collects data in real time, such as the article URL, viewing time, click events, and scrolling speed.

[0126] Input: User behavior data (article URL, viewing time, click events, scroll speed)

[0127] Output: Collected user behavior data is saved in a database

[0128] Step 2:

[0129] The server analyzes the collected behavioral data using an analysis means.

[0130] How it works: The server uses machine learning models (e.g., Python's Scikit-learn library) to analyze text and behavioral patterns to identify topics that interest the user.

[0131] Input: User behavior data collected in step 1

[0132] Output: Identification of user topics of interest

[0133] Step 3:

[0134] The server selects news articles based on the user's interest information identified by the analysis means.

[0135] What it does: The server filters the latest articles from a news database (e.g., MongoDB) that are most relevant to the user.

[0136] Input: User interest topics identified in step 2, news database

[0137] Output: A list of relevant news articles

[0138] Step 4:

[0139] The server summarizes the selected news articles using a summarizing means.

[0140] How it works: The server inputs selected news articles into a generative AI model (e.g., GPT-3) and generates summaries using prompts such as, "Please summarize an article about the latest AI technology announcement in a short sentence that can be explained in under one minute."

[0141] Input: News articles selected in step 3

[0142] Output: Summarized news article text

[0143] Step 5:

[0144] The server visualizes the summarized news article using an image generating means.

[0145] Specific operation: The server generates a video by integrating the text summary, related images, and audio narration. Specifically, it uses video generation software such as FFmpeg to create video content of less than one minute.

[0146] Input: Text of the news article summarized in step 4, associated images, and audio narration

[0147] Output: Generated video file

[0148] Step 6:

[0149] The server distributes the generated video to the user through a distribution means.

[0150] Specific operation: The server sends a notification according to the time period specified by the user (e.g., morning, afternoon, or evening) and distributes the generated video. For example, if the user selects "Morning News," the server sends a notification at 7:00 a.m. and starts distributing the video.

[0151] Input: Video file generated in step 5, user distribution settings

[0152] Output: Notifications sent to users and videos delivered

[0153] By implementing the above steps, this system is able to summarize news based on the user's interests, visualize it, and deliver it efficiently.

[0154] (Application example 1)

[0155] 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."

[0156] Conventional news acquisition methods require users to manually search for news relevant to their interests from a vast amount of information, which requires time and effort. In addition, there is a lack of a means to quickly and efficiently understand the content of interest, making it difficult to provide useful news information to users.

[0157] 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.

[0158] In this invention, the server includes user information collection means, analysis means for analyzing user behavior information collected by the user information collection means, selection means for selecting news articles based on the user interest information analyzed by the analysis means, summarization means for summarizing the news articles selected by the selection means, image generation means for visualizing the news articles summarized by the summarization means, distribution means for distributing the images generated by the image generation means at a specified time, and display means for displaying the images distributed by the distribution means on the user's terminal. This enables users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[0159] The "user information collection means" is a means for collecting user behavior data (for example, browsing history, click events, browsing time, etc.).

[0160] "Analysis means" refers to means that uses machine learning models to analyze user behavior information and identify user interests.

[0161] The "selection means" is a means for selecting an appropriate news article based on the user interest information analyzed by the analysis means.

[0162] The "summarization means" is a means for briefly summarizing the news articles selected by the selection means using a generative AI model (e.g., a generative AI model).

[0163] The "video generating means" is a means for generating a short video by integrating text, related images, and audio narration to visualize the news article summarized by the summarizing means.

[0164] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[0165] The "display means" is a means for displaying the video distributed by the distribution means on a user's terminal (for example, a smartphone, smart glasses, a head-mounted display, etc.).

[0166] This invention is a system that summarizes news based on a user's behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain necessary news information efficiently in a short time.

[0167] System configuration

[0168] The system includes the following means:

[0169] 1. How we collect user information

[0170] 2. Analysis method

[0171] 3. Selection method

[0172] 4. Summary tools

[0173] 5. Image Generation Method

[0174] 6. Distribution Method

[0175] 7. Display means

[0176] Hardware and software used

[0177] Hardware: Servers, user devices such as smartphones, smart glasses, and head-mounted displays.

[0178] software:

[0179] requests: An HTTP request library for retrieving news article data.

[0180] openai: A generative AI model (e.g. GPT-3) library for summarizing text.

[0181] datetime: A standard library for getting the current time and sending notifications to the user.

[0182] Process Overview

[0183] 1. User information collection means: The server collects user behavior data (e.g., browsing history, click events, browsing time, etc.), which allows us to understand which topics the user is interested in.

[0184] 2. Analysis: The server analyzes the collected behavioral data to identify user interests. This analysis is performed using machine learning models.

[0185] 3. Selection method: Based on the analysis results, the server selects news articles related to the user's interests from the news database.

[0186] 4. Summarization: The server summarizes the selected news articles using a generative AI model, such as GPT-3.

[0187] Example prompt: "Write a one-minute summary of the following article:\n\nA presentation on the latest AI technology..."

[0188] 5. Video generation tool: Converts summarized news articles into short videos by integrating text, related images, and audio narration.

[0189] 6. Distribution method: The server distributes the generated video at the time specified by the user (e.g., morning, afternoon, or evening).

[0190] 7. Display method: The distributed video is displayed on the user's device such as a smartphone, smart glasses, or head-mounted display.

[0191] Specific examples

[0192] For example, if user A frequently browses articles related to "technology" on a news site, the system operates as follows:

[0193] The server collects user A's behavioral data (article browsing history, click events, viewing time, etc.).

[0194] Analyze the collected data and determine that User A has a strong interest in technology.

[0195] Latest technology-related news articles are selected and summarized using a generative AI model.

[0196] A summary of the news article is visualized to create a one-minute video that integrates text, related images, and audio narration.

[0197] If user A selects "Morning News," a video link will be sent at the set time.

[0198] This allows User A to efficiently obtain the latest technology-related news in the shortest possible time.

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1: Collect user information

[0201] The server collects behavioral data such as user browsing history, click events, and browsing time, which allows it to understand which topics users are interested in. Specifically, the server collects the URLs of pages visited by users, the length of time they stayed on the site, and the number of links they clicked, and stores this data in a database.

[0202] Input: User behavior data

[0203] Data processing / data calculation: Collecting behavioral data and storing it in a database

[0204] Output: User behavior information stored in a database

[0205] Step 2: Analyzing user behavior data

[0206] The server analyzes the collected behavioral data to identify the user's interests. The analysis uses machine learning models to identify which categories and topics the user is interested in. For example, if a user views many articles related to "technology," it is determined that the user has a strong interest in that category.

[0207] Input: User behavior information stored in a database

[0208] Data processing / data calculation: Data analysis using machine learning models

[0209] Output: User interest information

[0210] Step 3: Selecting news articles

[0211] The server selects relevant news articles from the news database based on the analyzed user interest information by filtering the latest news articles that match the user's interests.

[0212] Input: User interest information, news article database

[0213] Data processing / data calculation: filtering news articles

[0214] Output: Selected news articles

[0215] Step 4: Summarize the news article

[0216] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The generative AI model receives the article's content as a prompt, and generates a summary based on that. For example, the prompt might be, "Please summarize the following article in a way that can be explained in less than one minute."

[0217] Input: Selected news articles

[0218] Data processing / data calculation: Summary generation using generative AI models

[0219] Output: A summarized news article

[0220] Step 5: Visualize the news story

[0221] The server then creates a video of the summarized news article, which involves integrating the text, associated images, and audio narration. For example, the server reads the generated summary text as an audio narration and combines it with associated images to create a video.

[0222] Input: Summarized news article, associated images

[0223] Data processing / data calculation: text, image, and voice integration

[0224] Output: Visualized news article

[0225] Step 6: Stream and display

[0226] The server distributes the generated video at the time specified by the user. A video link is sent to the user's device, such as a smartphone, smart glasses, or head-mounted display, and the user can view the video by receiving the notification. Specifically, a notification is sent to the user's device at the specified time, and the user can open the video from the notification and view it.

[0227] Input: Animated news article, user-specified time

[0228] Data processing / data calculation: generating and sending notifications

[0229] Output: Video link notification sent to the user's device

[0230] This allows users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[0231] 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.

[0232] Overall overview

[0233] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0234] Program Structure

[0235] User information collection method

[0236] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[0237] Analysis means

[0238] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0239] Selection method

[0240] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0241] Summary tools

[0242] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[0243] Image generation means

[0244] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[0245] Delivery Method

[0246] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0247] Specific examples

[0248] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[0249] Collection Phase

[0250] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, if user A browses an article on "latest smartphone technology" and the camera and microphone detect a smile and an interested tone of voice, that information is stored in the database.

[0251] Analysis Phase

[0252] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0253] Selection Phase

[0254] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[0255] Summary Phase

[0256] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0257] Image generation phase

[0258] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0259] Delivery Phase

[0260] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0261] This system allows user A to efficiently obtain the latest news of interest with positive emotions in the shortest possible time.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The server collects user behavioral data and emotional information in real time. When a user browses a news site, the server records the article's URL, viewing time, click events, and scrolling information. It also uses a camera and microphone to recognize the user's facial expressions and tone of voice, and an emotion engine analyzes and stores the emotional information in a database. For example, if a user browses an article about the latest smartphone technology, stays there for a long time, and the camera detects a smile, the server records that information.

[0265] Step 2:

[0266] The server analyzes the collected behavioral and emotional data. It uses machine learning models and text analysis engines to identify which topics a user is interested in and what emotions they have. For example, based on a user's past browsing history, it may determine that they have a strong interest in technology-related topics and show positive emotions while viewing those articles.

[0267] Step 3:

[0268] The server selects relevant latest news articles from the news database based on the analysis results. It then filters appropriate articles based on the user's topics of interest and emotional information identified from the analysis results. For example, it selects technology-related articles that evoke positive emotions in users, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0269] Step 4:

[0270] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the article content is summarized within one minute. Furthermore, the server reflects the user's emotional information obtained through an emotion engine, creating summaries that emphasize a positive tone and key points. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone, with the following sentence: "The latest AI technology has been announced. As a result..."

[0271] Step 5:

[0272] The server then creates a video from the summarized news article. Using a video generation framework, it integrates the summary text, related images, and audio narration to create a video with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and a cheerful narration.

[0273] Step 6:

[0274] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[0275] Step 7:

[0276] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[0277] Step 8:

[0278] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[0279] These processing steps allow users to efficiently obtain the latest news that interests them, and also provide content based on emotion information.

[0280] Example 2

[0281] 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."

[0282] Conventional news delivery systems make it difficult for users to efficiently obtain content tailored to their interests and emotions. With so much news available, it is difficult to quickly grasp only the news that meets a specific user need. Furthermore, there are no systems that utilize emotional information to select news that will interest users and deliver it at the optimal time. As a result, users are often overwhelmed by the sheer volume of information, and may miss important news.

[0283] 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.

[0284] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This makes it possible to summarize optimal news articles based on the user's behavioral data and emotional information, and to distribute the summarized news in the form of images. This allows users to quickly and efficiently obtain news that matches their interests and receive it with positive emotions.

[0285] "User information collection means" refers to means for collecting user behavior data (browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[0286] The "analysis means" is a means for analyzing collected user behavioral data and emotional information to identify the user's topics of interest and emotional state.

[0287] The "selection means" is a means for selecting related news articles from the news database based on the user's interest information and emotional state identified by the analysis means.

[0288] The "summarization means" is a means for converting the news articles selected by the selection means into short summaries using a generative AI model.

[0289] The "image generating means" is a means for visualizing the news article summarized by the summarizing means by integrating the text, related images, audio narration, etc.

[0290] The "distribution means" is a means for distributing and notifying the user of the video generated by the video generation means at a specified time.

[0291] A "generative AI model" is an artificial intelligence technology used to generate news article summaries tailored to the user's preferences, such as a language generation model.

[0292] MODE FOR CARRYING OUT THE INVENTION

[0293] The system of the present invention summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0294] User information collection method

[0295] The server uses user information collection means to collect user behavioral data and emotional information. This includes the URL of the article the user viewed, the viewing time, the links clicked, scrolling information, and sensors such as the camera and microphone. This allows the server to recognize the user's facial expressions and tone of voice, analyze the emotional information using an emotion engine, and store it in a database. For example, the server can detect the smiling face and cheerful voice of a user viewing an article on "latest smartphone technology."

[0296] Analysis means

[0297] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0298] Selection method

[0299] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0300] Summary tools

[0301] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[0302] Image generation means

[0303] The server then uses a video generation tool to visualize the summarized news articles. This tool integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[0304] Delivery Method

[0305] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). After receiving the notification, the user can watch the video.

[0306] Specific examples

[0307] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional response to them, the system would operate as follows:

[0308] 1. Collection Phase:

[0309] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, when user A browses an article on "latest smartphone technology," the camera and microphone detect a smile and an interesting tone of voice, and the information is stored in a database.

[0310] 2. Analysis phase:

[0311] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0312] 3. Selection Phase:

[0313] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[0314] 4. Summary Phase:

[0315] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0316] 5. Image generation phase:

[0317] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0318] 6. Delivery Phase:

[0319] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0320] Prompt Sentence Examples

[0321] "Summarize the latest technology news in a positive tone in under one minute."

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] The server uses the user information collection means to collect user behavior data and emotion information.

[0325] Input: URLs of articles viewed by users, viewing time, links clicked, scrolling information, facial expressions and tone of voice from camera and microphone.

[0326] How it works: An application installed on the device records the user's behavioral data and simultaneously uses the camera and microphone to analyze facial expressions and tone of voice in real time.

[0327] Data processing: In the process of saving the above data in the database, it is saved in an organized format for each user.

[0328] Output: Behavioral data and emotional information stored in a database.

[0329] Step 2:

[0330] The server analyzes the collected behavioral data and emotional information using an analysis means.

[0331] Input: Behavioral data and emotional information collected in step 1.

[0332] How it works: The server uses machine learning models to analyze the content of articles viewed by the user, their browsing patterns, and even their emotional information to identify the user's interests and emotional state.

[0333] Data calculation: Machine learning algorithms are used to classify and identify topics of interest and emotional states, and output these as results.

[0334] Output: Identified user interest topics and emotional state.

[0335] Step 3:

[0336] The server selects relevant news articles from a news database based on the analysis results.

[0337] Input: User's interest topics and emotional state obtained in step 2, and a news database.

[0338] What it does: The server queries the news database to find the latest news articles that match the user's topic of interest and select them based on their emotional state.

[0339] Data processing: filtering and selection of news articles.

[0340] Output: Selected news articles.

[0341] Step 4:

[0342] The server summarizes the selected news articles.

[0343] Input: News articles selected in step 3.

[0344] How it works: The server uses a generative AI model (e.g., GPT-3) to set prompts and convert news articles into summaries that can be conveyed in under one minute, adjusting tone and key points based on the user's emotional information.

[0345] Data calculation: Generating summaries using generative AI models.

[0346] Output: A summarized news article.

[0347] Step 5:

[0348] The server visualizes the summarized news articles.

[0349] Input: The news article summarized in step 4.

[0350] What it does: Integrates text, related images, and audio narration to generate a video with a tone and expression that matches the user's emotions.

[0351] Data processing: converting text to video, integrating images and narration.

[0352] Output: A visualized news article.

[0353] Step 6:

[0354] The server distributes the generated video to the user.

[0355] Input: The video generated in step 5 and the user-specified broadcast time.

[0356] Specific operation: The server sends notifications and distributes video according to the specified time period (e.g., morning, afternoon, evening).

[0357] Data Calculation: Notification generation based on delivery schedule.

[0358] Output: The video notification and video content delivered to the user.

[0359] (Application example 2)

[0360] 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."

[0361] In today's information society, users have access to a large amount of news information, but it is difficult to efficiently understand all of the information and extract only the content that interests them. Furthermore, because the amount of news content is so vast, users often waste time on information that is not important to them. Furthermore, because how users perceive news is greatly influenced by their emotional state, there is a need to provide content that is tailored to each individual. The present invention aims to provide a system that enables efficient and appropriate summarization and delivery of news information based on user behavioral data and emotional information.

[0362] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user information collection means, an analysis means for analyzing user behavior information and emotion information collected by the user information collection means, a selection means for selecting news articles based on the user interest information and emotion information analyzed by the analysis means, a summarization means for summarizing the news articles selected by the selection means using a generative AI model, an image generation means for visualizing the news articles summarized by the summarization means, and a distribution means for distributing the images generated by the image generation means at a time specified by the user. This enables users to efficiently obtain news that is relevant to their areas of interest and leaves them with positive emotions in a minimum amount of time.

[0363] The "user information collection means" is a means for collecting a user's browsing history, click events, browsing time, and emotion information.

[0364] The "analysis means" is a means for analyzing the user behavior information and emotion information collected by the user information collection means, and identifying the user's interests and emotions.

[0365] The "selection means" is a means for selecting a news article based on the user interest information and emotion information analyzed by the analysis means.

[0366] The "summarization means" is a means for summarizing the news articles selected by the selection means using a generative AI model.

[0367] The "image generating means" is a means for visualizing the news article summarized by the summarizing means.

[0368] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[0369] A "generative AI model" is an artificial intelligence model used to generate summaries of news articles.

[0370] "Emotion information" is information that indicates the emotional state of the user, analyzed from their facial expressions and tone of voice.

[0371] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This system allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0372] Overall system configuration

[0373] User information collection method

[0374] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[0375] Analysis means

[0376] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and an emotional analysis engine are used to identify which topics the user is interested in and their emotional state. For example, if the user frequently views technology-related articles and expresses positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0377] Selection method

[0378] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0379] Summary tools

[0380] The server uses a generative AI model to summarize the news articles selected by the selection method. By inputting prompt sentences into the generative AI model, it generates a short summary that can convey the original article content in less than one minute. Furthermore, it reflects the user's emotional information obtained through an emotion engine and adjusts the tone and key points of the article. For example, for a user with a positive emotion, the summary will emphasize success stories and positive news.

[0381] Image generation means

[0382] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[0383] Delivery Method

[0384] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0385] Specific examples of hardware and software used

[0386] Hardware:

[0387] Smartphone: Uses camera and microphone to capture user facial expressions and tone of voice.

[0388] Server: Analyzes and processes data.

[0389] software:

[0390] GPT-3 (generative AI model): Generates summaries of news articles.

[0391] Sentiment analysis engine: Analyzes user emotions.

[0392] Text-to-Video Software: Convert text to video.

[0393] Specific examples

[0394] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[0395] Collection phase:

[0396] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[0397] For example, if User A views an article on "Latest Smartphone Technology" and the camera and microphone detect a smile and an interesting tone of voice, that information will be stored in the database.

[0398] Analysis phase:

[0399] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0400] Selection phase:

[0401] The server selects the latest technology-related news articles from a news database.

[0402] For example, it narrows down articles to match users' positive emotions, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0403] Summary phase:

[0404] The server summarizes the selected articles using a generative AI model.

[0405] For example, "Announcement of new AI technology" can be summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0406] Image Generation Phase:

[0407] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0408] Delivery phase:

[0409] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0410] Prompt Sentence Examples

[0411] Summarize news articles and adjust tone depending on emotional information.

[0412] User Sentiment: Positive

[0413] Article content: The latest AI technology has been announced. It is expected to revolutionize various industries, with applications in the medical and financial fields attracting particular attention.

[0414] Abstract: The latest AI technology has been unveiled, promising revolutionary changes in the fields of medicine and finance.

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] The server uses user information collection means to collect user behavioral data and emotional information. It obtains the URL of the article the user is viewing, the viewing time, click events, and scrolling information, while also recording the user's facial expressions and tone of voice in real time via a camera and microphone. This data is sent to the server and stored in a database.

[0418] Input: User browsing history, click events, browsing time, facial expressions, tone of voice

[0419] Output: Behavioral data and emotional information stored in a database

[0420] Step 2:

[0421] The server analyzes the collected behavioral data and emotional information using analytical means, using machine learning models and an emotional analysis engine to identify which topics users are interested in and their emotional state at the time.

[0422] Input: Behavioral data and emotional information

[0423] Output: User's interest topics and emotional state

[0424] Step 3:

[0425] The server selects relevant latest news articles from the news database based on the analysis results, filters news articles corresponding to the topics and emotional information that the user has expressed interest in, and selects the most relevant articles.

[0426] Input: User's interest topics and emotional state

[0427] Output: A list of selected news articles

[0428] Step 4:

[0429] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The user inputs a prompt into the generative AI model, which generates a summary that conveys the article's content in a short amount of time. The generated summary reflects the user's emotional information and adjusts the article's tone and key points.

[0430] Input: Selected news article, prompt

[0431] Output: A summarized news article

[0432] Step 5:

[0433] The server then uses the video generation means to visualize the news articles summarized by the summarization means. The server integrates the text, related images, and audio narration to create a video using a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[0434] Input: A summarized news article, associated images, and audio files

[0435] Output: Finished video

[0436] Step 6:

[0437] The server distributes the generated video to the user using a distribution method. It also sends a notification to the user at a specific time period (e.g., morning, afternoon, or evening) designated by the user, informing them that the video is available for viewing. The user receives the notification and can begin viewing the video.

[0438] Input: Finished video, user-specified duration

[0439] Output: Notification to the user and video distribution

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] 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. 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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."

[0456] Overall overview

[0457] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain the news information they need efficiently and quickly.

[0458] Program Structure

[0459] User information collection method

[0460] The server uses a user information collection means to collect user behavior data, which collects information such as the URL of the article the user viewed, the viewing time, the link clicked, and the scrolling speed, and stores the collected information in a database.

[0461] Analysis means

[0462] The server analyzes the collected behavioral data using analytics, such as text analytics and machine learning models, to identify which topics users are interested in.

[0463] Selection method

[0464] The server selects news articles based on the user's interest information identified by the analysis means, and filters the most recent articles in the news database to select the articles most relevant to the user.

[0465] Summary tools

[0466] The server then summarises the news articles selected by the selection means using a generative AI model (e.g., GPT-3) to create a short summary of each article that can be conveyed in under one minute.

[0467] Image generation means

[0468] The server visualizes the summarized news articles using a video generation means that integrates the text, associated images, and audio narration to create a short video.

[0469] Delivery Method

[0470] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0471] Specific examples

[0472] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0473] Collection Phase

[0474] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data is recorded.

[0475] Analysis Phase

[0476] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0477] Selection Phase

[0478] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0479] Summary Phase

[0480] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0481] Image generation phase

[0482] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0483] Delivery Phase

[0484] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0485] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0486] The processing flow will be explained below.

[0487] Step 1:

[0488] The server collects user behavior data in real time. When a user browses a news site, it records the article's URL, viewing time, click events, scrolling information, etc. For example, if a user browses an article on "latest smartphone technology" and stays there for a long time, that information is stored in a database.

[0489] Step 2:

[0490] The server analyzes the collected behavioral data, using machine learning models and text analysis engines to identify which topics the user is interested in. For example, based on the user's past browsing history, it may determine that the user is interested in technology articles.

[0491] Step 3:

[0492] Based on the analysis results, the server selects relevant latest news articles from the news database, narrowing down the articles to those that match the user's interests, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0493] Step 4:

[0494] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the original article content is summarized in a short, approximately one-minute sentence. For example, "Announcement of new AI technology" is summarized as "The latest AI technology has been announced. As a result..."

[0495] Step 5:

[0496] The server then creates a video from the summarized news article. It uses a video generation framework to integrate the summary text, related images, and audio narration to create a video of less than one minute. For example, it adds images and visual effects related to the summary, "The latest AI technology has been announced."

[0497] Step 6:

[0498] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[0499] Step 7:

[0500] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[0501] Step 8:

[0502] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[0503] These processing steps allow the user to efficiently obtain the latest news that interests him or her in a short amount of time.

[0504] Example 1

[0505] 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."

[0506] In modern society, users are exposed to a huge amount of information, making it difficult to efficiently obtain the news information they need. Furthermore, because users do not have the time to browse news articles individually, there is a growing need to quickly grasp important information. However, there is a lack of systems that can select relevant news based on users' interests and behavioral history, and then present it in a format that is easily understandable.

[0507] 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.

[0508] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This allows the server to select news of interest based on the user's behavior data, summarize it, and visualize it, thereby enabling the user to efficiently obtain highly relevant news information in a short amount of time.

[0509] The "user information collection means" is a means for collecting behavioral data such as a user's browsing history, click events, browsing time, and scrolling speed.

[0510] The "analysis means" is a means for analyzing collected user behavior data using a machine learning model to identify user interests.

[0511] The "selection means" is a means for selecting news articles based on the user's interest information identified by the analysis means.

[0512] The "summarization method" is a method for summarizing selected news articles into short sentences using a generative AI model.

[0513] The "video generation means" is a means for creating a video of a summarized news article by integrating text, related images, and audio narration.

[0514] The "distribution means" is a means for distributing the generated video in accordance with a specific time period (morning, afternoon, or evening) selected by the user.

[0515] Overall overview

[0516] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes the news, and delivers it to the user. This system allows users to obtain the news information they need efficiently and quickly.

[0517] Program Structure

[0518] User information collection method

[0519] The server uses a user information collection means to collect user behavior data. This means collects information such as the URL of the article the user viewed, the viewing time, the links clicked, and the scrolling speed, and stores it in a database. Specific software used may include a web tracking tool or a data collection library.

[0520] Analysis means

[0521] The server analyzes the collected behavioral data using analytics, which can include text analysis and machine learning models to identify topics that users are interested in. Machine learning algorithms can be implemented using programming languages ​​such as Python and R.

[0522] Selection method

[0523] The server selects news articles based on the user's interests identified by the analysis means, filtering the most relevant articles for the user from the latest articles in a news database (e.g., MongoDB).

[0524] Summary tools

[0525] The server summarizes the news articles selected by the selection means using the summarization means. Using a generative AI model (e.g., GPT-3), each article is converted into a short summary that can be explained in less than one minute. An example of a prompt sentence is, "Please summarize an article about the latest AI technology announcement. Please write a short sentence that can be explained in less than one minute."

[0526] Image generation means

[0527] The server then visualizes the summarized news articles using a video generation tool that integrates the text, related images, and audio narration to create short videos. FFmpeg is used as the video generation software.

[0528] Delivery Method

[0529] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). For example, if the user selects "morning news," the server notifies the user that the generated video will be available for viewing at 7:00 a.m. After receiving the notification, the user can view the video.

[0530] Specific examples

[0531] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0532] Collection Phase

[0533] The server collects user A's behavioral data (article browsing history, click events, browsing time, scrolling speed, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data will be recorded.

[0534] Analysis Phase

[0535] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0536] Selection Phase

[0537] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0538] Summary Phase

[0539] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0540] Image generation phase

[0541] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0542] Delivery Phase

[0543] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0544] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] The server uses the user information collection means to collect user behavior data.

[0548] How it works: When a user visits a news site, the server collects data in real time, such as the article URL, viewing time, click events, and scrolling speed.

[0549] Input: User behavior data (article URL, viewing time, click events, scroll speed)

[0550] Output: Collected user behavior data is saved in a database

[0551] Step 2:

[0552] The server analyzes the collected behavioral data using an analysis means.

[0553] How it works: The server uses machine learning models (e.g., Python's Scikit-learn library) to analyze text and behavioral patterns to identify topics that interest the user.

[0554] Input: User behavior data collected in step 1

[0555] Output: Identification of user topics of interest

[0556] Step 3:

[0557] The server selects news articles based on the user's interest information identified by the analysis means.

[0558] What it does: The server filters the latest articles from a news database (e.g., MongoDB) that are most relevant to the user.

[0559] Input: User interest topics identified in step 2, news database

[0560] Output: A list of relevant news articles

[0561] Step 4:

[0562] The server summarizes the selected news articles using a summarizing means.

[0563] How it works: The server inputs selected news articles into a generative AI model (e.g., GPT-3) and generates summaries using prompts such as, "Please summarize an article about the latest AI technology announcement in a short sentence that can be explained in under one minute."

[0564] Input: News articles selected in step 3

[0565] Output: Summarized news article text

[0566] Step 5:

[0567] The server visualizes the summarized news article using an image generating means.

[0568] Specific operation: The server generates a video by integrating the text summary, related images, and audio narration. Specifically, it uses video generation software such as FFmpeg to create video content of less than one minute.

[0569] Input: Text of the news article summarized in step 4, associated images, and audio narration

[0570] Output: Generated video file

[0571] Step 6:

[0572] The server distributes the generated video to the user through a distribution means.

[0573] Specific operation: The server sends a notification according to the time period specified by the user (e.g., morning, afternoon, or evening) and distributes the generated video. For example, if the user selects "Morning News," the server sends a notification at 7:00 a.m. and starts distributing the video.

[0574] Input: Video file generated in step 5, user distribution settings

[0575] Output: Notifications sent to users and videos delivered

[0576] By implementing the above steps, this system is able to summarize news based on the user's interests, visualize it, and deliver it efficiently.

[0577] (Application example 1)

[0578] 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."

[0579] Conventional news acquisition methods require users to manually search for news relevant to their interests from a vast amount of information, which requires time and effort. In addition, there is a lack of a means to quickly and efficiently understand the content of interest, making it difficult to provide useful news information to users.

[0580] 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.

[0581] In this invention, the server includes user information collection means, analysis means for analyzing user behavior information collected by the user information collection means, selection means for selecting news articles based on the user interest information analyzed by the analysis means, summarization means for summarizing the news articles selected by the selection means, image generation means for visualizing the news articles summarized by the summarization means, distribution means for distributing the images generated by the image generation means at a specified time, and display means for displaying the images distributed by the distribution means on the user's terminal. This enables users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[0582] The "user information collection means" is a means for collecting user behavior data (for example, browsing history, click events, browsing time, etc.).

[0583] "Analysis means" refers to means that uses machine learning models to analyze user behavior information and identify user interests.

[0584] The "selection means" is a means for selecting an appropriate news article based on the user interest information analyzed by the analysis means.

[0585] The "summarization means" is a means for briefly summarizing the news articles selected by the selection means using a generative AI model (e.g., a generative AI model).

[0586] The "video generating means" is a means for generating a short video by integrating text, related images, and audio narration to visualize the news article summarized by the summarizing means.

[0587] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[0588] The "display means" is a means for displaying the video distributed by the distribution means on a user's terminal (for example, a smartphone, smart glasses, a head-mounted display, etc.).

[0589] This invention is a system that summarizes news based on a user's behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain necessary news information efficiently in a short time.

[0590] System configuration

[0591] The system includes the following means:

[0592] 1. How we collect user information

[0593] 2. Analysis method

[0594] 3. Selection method

[0595] 4. Summary tools

[0596] 5. Image Generation Method

[0597] 6. Distribution Method

[0598] 7. Display means

[0599] Hardware and software used

[0600] Hardware: Servers, user devices such as smartphones, smart glasses, and head-mounted displays.

[0601] software:

[0602] requests: An HTTP request library for retrieving news article data.

[0603] openai: A generative AI model (e.g. GPT-3) library for summarizing text.

[0604] datetime: A standard library for getting the current time and sending notifications to the user.

[0605] Process Overview

[0606] 1. User information collection means: The server collects user behavior data (e.g., browsing history, click events, browsing time, etc.), which allows us to understand which topics the user is interested in.

[0607] 2. Analysis: The server analyzes the collected behavioral data to identify user interests. This analysis is performed using machine learning models.

[0608] 3. Selection method: Based on the analysis results, the server selects news articles related to the user's interests from the news database.

[0609] 4. Summarization: The server summarizes the selected news articles using a generative AI model, such as GPT-3.

[0610] Example prompt: "Write a one-minute summary of the following article:\n\nA presentation on the latest AI technology..."

[0611] 5. Video generation tool: Converts summarized news articles into short videos by integrating text, related images, and audio narration.

[0612] 6. Distribution method: The server distributes the generated video at the time specified by the user (e.g., morning, afternoon, or evening).

[0613] 7. Display method: The distributed video is displayed on the user's device such as a smartphone, smart glasses, or head-mounted display.

[0614] Specific examples

[0615] For example, if user A frequently browses articles related to "technology" on a news site, the system operates as follows:

[0616] The server collects user A's behavioral data (article browsing history, click events, viewing time, etc.).

[0617] Analyze the collected data and determine that User A has a strong interest in technology.

[0618] Latest technology-related news articles are selected and summarized using a generative AI model.

[0619] A summary of the news article is visualized to create a one-minute video that integrates text, related images, and audio narration.

[0620] If user A selects "Morning News," a video link will be sent at the set time.

[0621] This allows User A to efficiently obtain the latest technology-related news in the shortest possible time.

[0622] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0623] Step 1: Collect user information

[0624] The server collects behavioral data such as user browsing history, click events, and browsing time, which allows it to understand which topics users are interested in. Specifically, the server collects the URLs of pages visited by users, the length of time they stayed on the site, and the number of links they clicked, and stores this data in a database.

[0625] Input: User behavior data

[0626] Data processing / data calculation: Collecting behavioral data and storing it in a database

[0627] Output: User behavior information stored in a database

[0628] Step 2: Analyzing user behavior data

[0629] The server analyzes the collected behavioral data to identify the user's interests. The analysis uses machine learning models to identify which categories and topics the user is interested in. For example, if a user views many articles related to "technology," it is determined that the user has a strong interest in that category.

[0630] Input: User behavior information stored in a database

[0631] Data processing / data calculation: Data analysis using machine learning models

[0632] Output: User interest information

[0633] Step 3: Selecting news articles

[0634] The server selects relevant news articles from the news database based on the analyzed user interest information by filtering the latest news articles that match the user's interests.

[0635] Input: User interest information, news article database

[0636] Data processing / data calculation: filtering news articles

[0637] Output: Selected news articles

[0638] Step 4: Summarize the news article

[0639] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The generative AI model receives the article's content as a prompt, and generates a summary based on that. For example, the prompt might be, "Please summarize the following article in a way that can be explained in less than one minute."

[0640] Input: Selected news articles

[0641] Data processing / data calculation: Summary generation using generative AI models

[0642] Output: A summarized news article

[0643] Step 5: Visualize the news story

[0644] The server then creates a video of the summarized news article, which involves integrating the text, associated images, and audio narration. For example, the server reads the generated summary text as an audio narration and combines it with associated images to create a video.

[0645] Input: Summarized news article, associated images

[0646] Data processing / data calculation: text, image, and voice integration

[0647] Output: Visualized news article

[0648] Step 6: Stream and display

[0649] The server distributes the generated video at the time specified by the user. A video link is sent to the user's device, such as a smartphone, smart glasses, or head-mounted display, and the user can view the video by receiving the notification. Specifically, a notification is sent to the user's device at the specified time, and the user can open the video from the notification and view it.

[0650] Input: Animated news article, user-specified time

[0651] Data processing / data calculation: generating and sending notifications

[0652] Output: Video link notification sent to the user's device

[0653] This allows users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[0654] 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.

[0655] Overall overview

[0656] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0657] Program Structure

[0658] User information collection method

[0659] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[0660] Analysis means

[0661] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0662] Selection method

[0663] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0664] Summary tools

[0665] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[0666] Image generation means

[0667] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[0668] Delivery Method

[0669] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0670] Specific examples

[0671] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[0672] Collection Phase

[0673] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, if user A browses an article on "latest smartphone technology" and the camera and microphone detect a smile and an interested tone of voice, that information is stored in the database.

[0674] Analysis Phase

[0675] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0676] Selection Phase

[0677] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[0678] Summary Phase

[0679] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0680] Image generation phase

[0681] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0682] Delivery Phase

[0683] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0684] This system allows user A to efficiently obtain the latest news of interest with positive emotions in the shortest possible time.

[0685] The processing flow will be explained below.

[0686] Step 1:

[0687] The server collects user behavioral data and emotional information in real time. When a user browses a news site, the server records the article's URL, viewing time, click events, and scrolling information. It also uses a camera and microphone to recognize the user's facial expressions and tone of voice, and an emotion engine analyzes and stores the emotional information in a database. For example, if a user browses an article about the latest smartphone technology, stays there for a long time, and the camera detects a smile, the server records that information.

[0688] Step 2:

[0689] The server analyzes the collected behavioral and emotional data. It uses machine learning models and text analysis engines to identify which topics a user is interested in and what emotions they have. For example, based on a user's past browsing history, it may determine that they have a strong interest in technology-related topics and show positive emotions while viewing those articles.

[0690] Step 3:

[0691] The server selects relevant latest news articles from the news database based on the analysis results. It then filters appropriate articles based on the user's topics of interest and emotional information identified from the analysis results. For example, it selects technology-related articles that evoke positive emotions in users, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0692] Step 4:

[0693] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the article content is summarized within one minute. Furthermore, the server reflects the user's emotional information obtained through an emotion engine, creating summaries that emphasize a positive tone and key points. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone, with the following sentence: "The latest AI technology has been announced. As a result..."

[0694] Step 5:

[0695] The server then creates a video from the summarized news article. Using a video generation framework, it integrates the summary text, related images, and audio narration to create a video with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and a cheerful narration.

[0696] Step 6:

[0697] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[0698] Step 7:

[0699] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[0700] Step 8:

[0701] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[0702] These processing steps allow users to efficiently obtain the latest news that interests them, and also provide content based on emotion information.

[0703] Example 2

[0704] 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."

[0705] Conventional news delivery systems make it difficult for users to efficiently obtain content tailored to their interests and emotions. With so much news available, it is difficult to quickly grasp only the news that meets a specific user need. Furthermore, there are no systems that utilize emotional information to select news that will interest users and deliver it at the optimal time. As a result, users are often overwhelmed by the sheer volume of information, and may miss important news.

[0706] 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.

[0707] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This makes it possible to summarize optimal news articles based on the user's behavioral data and emotional information, and to distribute the summarized news in the form of images. This allows users to quickly and efficiently obtain news that matches their interests and receive it with positive emotions.

[0708] "User information collection means" refers to means for collecting user behavior data (browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[0709] The "analysis means" is a means for analyzing collected user behavioral data and emotional information to identify the user's topics of interest and emotional state.

[0710] The "selection means" is a means for selecting related news articles from the news database based on the user's interest information and emotional state identified by the analysis means.

[0711] The "summarization means" is a means for converting the news articles selected by the selection means into short summaries using a generative AI model.

[0712] The "image generating means" is a means for visualizing the news article summarized by the summarizing means by integrating the text, related images, audio narration, etc.

[0713] The "distribution means" is a means for distributing and notifying the user of the video generated by the video generation means at a specified time.

[0714] A "generative AI model" is an artificial intelligence technology used to generate news article summaries tailored to the user's preferences, such as a language generation model.

[0715] MODE FOR CARRYING OUT THE INVENTION

[0716] The system of the present invention summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0717] User information collection method

[0718] The server uses user information collection means to collect user behavioral data and emotional information. This includes the URL of the article the user viewed, the viewing time, the links clicked, scrolling information, and sensors such as the camera and microphone. This allows the server to recognize the user's facial expressions and tone of voice, analyze the emotional information using an emotion engine, and store it in a database. For example, the server can detect the smiling face and cheerful voice of a user viewing an article on "latest smartphone technology."

[0719] Analysis means

[0720] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0721] Selection method

[0722] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0723] Summary tools

[0724] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[0725] Image generation means

[0726] The server then uses a video generation tool to visualize the summarized news articles. This tool integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[0727] Delivery Method

[0728] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). After receiving the notification, the user can watch the video.

[0729] Specific examples

[0730] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional response to them, the system would operate as follows:

[0731] 1. Collection Phase:

[0732] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, when user A browses an article on "latest smartphone technology," the camera and microphone detect a smile and an interesting tone of voice, and the information is stored in a database.

[0733] 2. Analysis phase:

[0734] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0735] 3. Selection Phase:

[0736] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[0737] 4. Summary Phase:

[0738] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0739] 5. Image generation phase:

[0740] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0741] 6. Delivery Phase:

[0742] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0743] Prompt Sentence Examples

[0744] "Summarize the latest technology news in a positive tone in under one minute."

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] The server uses the user information collection means to collect user behavior data and emotion information.

[0748] Input: URLs of articles viewed by users, viewing time, links clicked, scrolling information, facial expressions and tone of voice from camera and microphone.

[0749] How it works: An application installed on the device records the user's behavioral data and simultaneously uses the camera and microphone to analyze facial expressions and tone of voice in real time.

[0750] Data processing: In the process of saving the above data in the database, it is saved in an organized format for each user.

[0751] Output: Behavioral data and emotional information stored in a database.

[0752] Step 2:

[0753] The server analyzes the collected behavioral data and emotional information using an analysis means.

[0754] Input: Behavioral data and emotional information collected in step 1.

[0755] How it works: The server uses machine learning models to analyze the content of articles viewed by the user, their browsing patterns, and even their emotional information to identify the user's interests and emotional state.

[0756] Data calculation: Machine learning algorithms are used to classify and identify topics of interest and emotional states, and output these as results.

[0757] Output: Identified user interest topics and emotional state.

[0758] Step 3:

[0759] The server selects relevant news articles from a news database based on the analysis results.

[0760] Input: User's interest topics and emotional state obtained in step 2, and a news database.

[0761] What it does: The server queries the news database to find the latest news articles that match the user's topic of interest and select them based on their emotional state.

[0762] Data processing: filtering and selection of news articles.

[0763] Output: Selected news articles.

[0764] Step 4:

[0765] The server summarizes the selected news articles.

[0766] Input: News articles selected in step 3.

[0767] How it works: The server uses a generative AI model (e.g., GPT-3) to set prompts and convert news articles into summaries that can be conveyed in under one minute, adjusting tone and key points based on the user's emotional information.

[0768] Data calculation: Generating summaries using generative AI models.

[0769] Output: A summarized news article.

[0770] Step 5:

[0771] The server visualizes the summarized news articles.

[0772] Input: The news article summarized in step 4.

[0773] What it does: Integrates text, related images, and audio narration to generate a video with a tone and expression that matches the user's emotions.

[0774] Data processing: converting text to video, integrating images and narration.

[0775] Output: A visualized news article.

[0776] Step 6:

[0777] The server distributes the generated video to the user.

[0778] Input: The video generated in step 5 and the user-specified broadcast time.

[0779] Specific operation: The server sends notifications and distributes video according to the specified time period (e.g., morning, afternoon, evening).

[0780] Data Calculation: Notification generation based on delivery schedule.

[0781] Output: The video notification and video content delivered to the user.

[0782] (Application example 2)

[0783] 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."

[0784] In today's information society, users have access to a large amount of news information, but it is difficult to efficiently understand all of the information and extract only the content that interests them. Furthermore, because the amount of news content is so vast, users often waste time on information that is not important to them. Furthermore, because how users perceive news is greatly influenced by their emotional state, there is a need to provide content that is tailored to each individual. The present invention aims to provide a system that enables efficient and appropriate summarization and delivery of news information based on user behavioral data and emotional information.

[0785] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user information collection means, an analysis means for analyzing user behavior information and emotion information collected by the user information collection means, a selection means for selecting news articles based on the user interest information and emotion information analyzed by the analysis means, a summarization means for summarizing the news articles selected by the selection means using a generative AI model, an image generation means for visualizing the news articles summarized by the summarization means, and a distribution means for distributing the images generated by the image generation means at a time specified by the user. This enables users to efficiently obtain news that is relevant to their areas of interest and leaves them with positive emotions in a minimum amount of time.

[0786] The "user information collection means" is a means for collecting a user's browsing history, click events, browsing time, and emotion information.

[0787] The "analysis means" is a means for analyzing the user behavior information and emotion information collected by the user information collection means, and identifying the user's interests and emotions.

[0788] The "selection means" is a means for selecting a news article based on the user interest information and emotion information analyzed by the analysis means.

[0789] The "summarization means" is a means for summarizing the news articles selected by the selection means using a generative AI model.

[0790] The "image generating means" is a means for visualizing the news article summarized by the summarizing means.

[0791] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[0792] A "generative AI model" is an artificial intelligence model used to generate summaries of news articles.

[0793] "Emotion information" is information that indicates the emotional state of the user, analyzed from their facial expressions and tone of voice.

[0794] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This system allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[0795] Overall system configuration

[0796] User information collection method

[0797] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[0798] Analysis means

[0799] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and an emotional analysis engine are used to identify which topics the user is interested in and their emotional state. For example, if the user frequently views technology-related articles and expresses positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[0800] Selection method

[0801] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[0802] Summary tools

[0803] The server uses a generative AI model to summarize the news articles selected by the selection method. By inputting prompt sentences into the generative AI model, it generates a short summary that can convey the original article content in less than one minute. Furthermore, it reflects the user's emotional information obtained through an emotion engine and adjusts the tone and key points of the article. For example, for a user with a positive emotion, the summary will emphasize success stories and positive news.

[0804] Image generation means

[0805] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[0806] Delivery Method

[0807] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0808] Specific examples of hardware and software used

[0809] Hardware:

[0810] Smartphone: Uses camera and microphone to capture user facial expressions and tone of voice.

[0811] Server: Analyzes and processes data.

[0812] software:

[0813] GPT-3 (generative AI model): Generates summaries of news articles.

[0814] Sentiment analysis engine: Analyzes user emotions.

[0815] Text-to-Video Software: Convert text to video.

[0816] Specific examples

[0817] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[0818] Collection phase:

[0819] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[0820] For example, if User A views an article on "Latest Smartphone Technology" and the camera and microphone detect a smile and an interesting tone of voice, that information will be stored in the database.

[0821] Analysis phase:

[0822] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[0823] Selection phase:

[0824] The server selects the latest technology-related news articles from a news database.

[0825] For example, it narrows down articles to match users' positive emotions, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0826] Summary phase:

[0827] The server summarizes the selected articles using a generative AI model.

[0828] For example, "Announcement of new AI technology" can be summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[0829] Image Generation Phase:

[0830] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[0831] Delivery phase:

[0832] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0833] Prompt Sentence Examples

[0834] Summarize news articles and adjust tone depending on emotional information.

[0835] User Sentiment: Positive

[0836] Article content: The latest AI technology has been announced. It is expected to revolutionize various industries, with applications in the medical and financial fields attracting particular attention.

[0837] Abstract: The latest AI technology has been unveiled, promising revolutionary changes in the fields of medicine and finance.

[0838] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0839] Step 1:

[0840] The server uses user information collection means to collect user behavioral data and emotional information. It obtains the URL of the article the user is viewing, the viewing time, click events, and scrolling information, while also recording the user's facial expressions and tone of voice in real time via a camera and microphone. This data is sent to the server and stored in a database.

[0841] Input: User browsing history, click events, browsing time, facial expressions, tone of voice

[0842] Output: Behavioral data and emotional information stored in a database

[0843] Step 2:

[0844] The server analyzes the collected behavioral data and emotional information using analytical means, using machine learning models and an emotional analysis engine to identify which topics users are interested in and their emotional state at the time.

[0845] Input: Behavioral data and emotional information

[0846] Output: User's interest topics and emotional state

[0847] Step 3:

[0848] The server selects relevant latest news articles from the news database based on the analysis results, filters news articles corresponding to the topics and emotional information that the user has expressed interest in, and selects the most relevant articles.

[0849] Input: User's interest topics and emotional state

[0850] Output: A list of selected news articles

[0851] Step 4:

[0852] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The user inputs a prompt into the generative AI model, which generates a summary that conveys the article's content in a short amount of time. The generated summary reflects the user's emotional information and adjusts the article's tone and key points.

[0853] Input: Selected news article, prompt

[0854] Output: A summarized news article

[0855] Step 5:

[0856] The server then uses the video generation means to visualize the news articles summarized by the summarization means. The server integrates the text, related images, and audio narration to create a video using a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[0857] Input: A summarized news article, associated images, and audio files

[0858] Output: Finished video

[0859] Step 6:

[0860] The server distributes the generated video to the user using a distribution method. It also sends a notification to the user at a specific time period (e.g., morning, afternoon, or evening) designated by the user, informing them that the video is available for viewing. The user receives the notification and can begin viewing the video.

[0861] Input: Finished video, user-specified duration

[0862] Output: Notification to the user and video distribution

[0863] 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.

[0864] 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.

[0865] 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.

[0866] [Third embodiment]

[0867] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0868] 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.

[0869] 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).

[0870] 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.

[0871] 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.

[0872] 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).

[0873] 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. 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] Overall overview

[0880] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain the news information they need efficiently and quickly.

[0881] Program Structure

[0882] User information collection method

[0883] The server uses a user information collection means to collect user behavior data, which collects information such as the URL of the article the user viewed, the viewing time, the link clicked, and the scrolling speed, and stores the collected information in a database.

[0884] Analysis means

[0885] The server analyzes the collected behavioral data using analytics, such as text analytics and machine learning models, to identify which topics users are interested in.

[0886] Selection method

[0887] The server selects news articles based on the user's interest information identified by the analysis means, and filters the most recent articles in the news database to select the articles most relevant to the user.

[0888] Summary tools

[0889] The server then summarises the news articles selected by the selection means using a generative AI model (e.g., GPT-3) to create a short summary of each article that can be conveyed in under one minute.

[0890] Image generation means

[0891] The server visualizes the summarized news articles using a video generation means that integrates the text, associated images, and audio narration to create a short video.

[0892] Delivery Method

[0893] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[0894] Specific examples

[0895] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0896] Collection Phase

[0897] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data is recorded.

[0898] Analysis Phase

[0899] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0900] Selection Phase

[0901] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0902] Summary Phase

[0903] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0904] Image generation phase

[0905] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0906] Delivery Phase

[0907] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0908] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0909] The processing flow will be explained below.

[0910] Step 1:

[0911] The server collects user behavior data in real time. When a user browses a news site, it records the article's URL, viewing time, click events, scrolling information, etc. For example, if a user browses an article on "latest smartphone technology" and stays there for a long time, that information is stored in a database.

[0912] Step 2:

[0913] The server analyzes the collected behavioral data, using machine learning models and text analysis engines to identify which topics the user is interested in. For example, based on the user's past browsing history, it may determine that the user is interested in technology articles.

[0914] Step 3:

[0915] Based on the analysis results, the server selects relevant latest news articles from the news database, narrowing down the articles to those that match the user's interests, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0916] Step 4:

[0917] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the original article content is summarized in a short, approximately one-minute sentence. For example, "Announcement of new AI technology" is summarized as "The latest AI technology has been announced. As a result..."

[0918] Step 5:

[0919] The server then creates a video from the summarized news article. It uses a video generation framework to integrate the summary text, related images, and audio narration to create a video of less than one minute. For example, it adds images and visual effects related to the summary, "The latest AI technology has been announced."

[0920] Step 6:

[0921] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[0922] Step 7:

[0923] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[0924] Step 8:

[0925] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[0926] These processing steps allow the user to efficiently obtain the latest news that interests him or her in a short amount of time.

[0927] Example 1

[0928] 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."

[0929] In modern society, users are exposed to a huge amount of information, making it difficult to efficiently obtain the news information they need. Furthermore, because users do not have the time to browse news articles individually, there is a growing need to quickly grasp important information. However, there is a lack of systems that can select relevant news based on users' interests and behavioral history, and then present it in a format that is easily understandable.

[0930] 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.

[0931] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This allows the server to select news of interest based on the user's behavior data, summarize it, and visualize it, thereby enabling the user to efficiently obtain highly relevant news information in a short amount of time.

[0932] The "user information collection means" is a means for collecting behavioral data such as a user's browsing history, click events, browsing time, and scrolling speed.

[0933] The "analysis means" is a means for analyzing collected user behavior data using a machine learning model to identify user interests.

[0934] The "selection means" is a means for selecting news articles based on the user's interest information identified by the analysis means.

[0935] The "summarization method" is a method for summarizing selected news articles into short sentences using a generative AI model.

[0936] The "video generation means" is a means for creating a video of a summarized news article by integrating text, related images, and audio narration.

[0937] The "distribution means" is a means for distributing the generated video in accordance with a specific time period (morning, afternoon, or evening) selected by the user.

[0938] Overall overview

[0939] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes the news, and delivers it to the user. This system allows users to obtain the news information they need efficiently and quickly.

[0940] Program Structure

[0941] User information collection method

[0942] The server uses a user information collection means to collect user behavior data. This means collects information such as the URL of the article the user viewed, the viewing time, the links clicked, and the scrolling speed, and stores it in a database. Specific software used may include a web tracking tool or a data collection library.

[0943] Analysis means

[0944] The server analyzes the collected behavioral data using analytics, which can include text analysis and machine learning models to identify topics that users are interested in. Machine learning algorithms can be implemented using programming languages ​​such as Python and R.

[0945] Selection method

[0946] The server selects news articles based on the user's interests identified by the analysis means, filtering the most relevant articles for the user from the latest articles in a news database (e.g., MongoDB).

[0947] Summary tools

[0948] The server summarizes the news articles selected by the selection means using the summarization means. Using a generative AI model (e.g., GPT-3), each article is converted into a short summary that can be explained in less than one minute. An example of a prompt sentence is, "Please summarize an article about the latest AI technology announcement. Please write a short sentence that can be explained in less than one minute."

[0949] Image generation means

[0950] The server then visualizes the summarized news articles using a video generation tool that integrates the text, related images, and audio narration to create short videos. FFmpeg is used as the video generation software.

[0951] Delivery Method

[0952] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). For example, if the user selects "morning news," the server notifies the user that the generated video will be available for viewing at 7:00 a.m. After receiving the notification, the user can view the video.

[0953] Specific examples

[0954] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[0955] Collection Phase

[0956] The server collects user A's behavioral data (article browsing history, click events, browsing time, scrolling speed, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data will be recorded.

[0957] Analysis Phase

[0958] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[0959] Selection Phase

[0960] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[0961] Summary Phase

[0962] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[0963] Image generation phase

[0964] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[0965] Delivery Phase

[0966] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[0967] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[0968] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0969] Step 1:

[0970] The server uses the user information collection means to collect user behavior data.

[0971] How it works: When a user visits a news site, the server collects data in real time, such as the article URL, viewing time, click events, and scrolling speed.

[0972] Input: User behavior data (article URL, viewing time, click events, scroll speed)

[0973] Output: Collected user behavior data is saved in a database

[0974] Step 2:

[0975] The server analyzes the collected behavioral data using an analysis means.

[0976] How it works: The server uses machine learning models (e.g., Python's Scikit-learn library) to analyze text and behavioral patterns to identify topics that interest the user.

[0977] Input: User behavior data collected in step 1

[0978] Output: Identification of user topics of interest

[0979] Step 3:

[0980] The server selects news articles based on the user's interest information identified by the analysis means.

[0981] What it does: The server filters the latest articles from a news database (e.g., MongoDB) that are most relevant to the user.

[0982] Input: User interest topics identified in step 2, news database

[0983] Output: A list of relevant news articles

[0984] Step 4:

[0985] The server summarizes the selected news articles using a summarizing means.

[0986] How it works: The server inputs selected news articles into a generative AI model (e.g., GPT-3) and generates summaries using prompts such as, "Please summarize an article about the latest AI technology announcement in a short sentence that can be explained in under one minute."

[0987] Input: News articles selected in step 3

[0988] Output: Summarized news article text

[0989] Step 5:

[0990] The server visualizes the summarized news article using an image generating means.

[0991] Specific operation: The server generates a video by integrating the text summary, related images, and audio narration. Specifically, it uses video generation software such as FFmpeg to create video content of less than one minute.

[0992] Input: Text of the news article summarized in step 4, associated images, and audio narration

[0993] Output: Generated video file

[0994] Step 6:

[0995] The server distributes the generated video to the user through a distribution means.

[0996] Specific operation: The server sends a notification according to the time period specified by the user (e.g., morning, afternoon, or evening) and distributes the generated video. For example, if the user selects "Morning News," the server sends a notification at 7:00 a.m. and starts distributing the video.

[0997] Input: Video file generated in step 5, user distribution settings

[0998] Output: Notifications sent to users and videos delivered

[0999] By implementing the above steps, this system is able to summarize news based on the user's interests, visualize it, and deliver it efficiently.

[1000] (Application example 1)

[1001] 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."

[1002] Conventional news acquisition methods require users to manually search for news relevant to their interests from a vast amount of information, which requires time and effort. In addition, there is a lack of a means to quickly and efficiently understand the content of interest, making it difficult to provide useful news information to users.

[1003] 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.

[1004] In this invention, the server includes user information collection means, analysis means for analyzing user behavior information collected by the user information collection means, selection means for selecting news articles based on the user interest information analyzed by the analysis means, summarization means for summarizing the news articles selected by the selection means, image generation means for visualizing the news articles summarized by the summarization means, distribution means for distributing the images generated by the image generation means at a specified time, and display means for displaying the images distributed by the distribution means on the user's terminal. This enables users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[1005] The "user information collection means" is a means for collecting user behavior data (for example, browsing history, click events, browsing time, etc.).

[1006] "Analysis means" refers to means that uses machine learning models to analyze user behavior information and identify user interests.

[1007] The "selection means" is a means for selecting an appropriate news article based on the user interest information analyzed by the analysis means.

[1008] The "summarization means" is a means for briefly summarizing the news articles selected by the selection means using a generative AI model (e.g., a generative AI model).

[1009] The "video generating means" is a means for generating a short video by integrating text, related images, and audio narration to visualize the news article summarized by the summarizing means.

[1010] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[1011] The "display means" is a means for displaying the video distributed by the distribution means on a user's terminal (for example, a smartphone, smart glasses, a head-mounted display, etc.).

[1012] This invention is a system that summarizes news based on a user's behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain necessary news information efficiently in a short time.

[1013] System configuration

[1014] The system includes the following means:

[1015] 1. How we collect user information

[1016] 2. Analysis method

[1017] 3. Selection method

[1018] 4. Summary tools

[1019] 5. Image Generation Method

[1020] 6. Distribution Method

[1021] 7. Display means

[1022] Hardware and software used

[1023] Hardware: Servers, user devices such as smartphones, smart glasses, and head-mounted displays.

[1024] software:

[1025] requests: An HTTP request library for retrieving news article data.

[1026] openai: A generative AI model (e.g. GPT-3) library for summarizing text.

[1027] datetime: A standard library for getting the current time and sending notifications to the user.

[1028] Process Overview

[1029] 1. User information collection means: The server collects user behavior data (e.g., browsing history, click events, browsing time, etc.), which allows us to understand which topics the user is interested in.

[1030] 2. Analysis: The server analyzes the collected behavioral data to identify user interests. This analysis is performed using machine learning models.

[1031] 3. Selection method: Based on the analysis results, the server selects news articles related to the user's interests from the news database.

[1032] 4. Summarization: The server summarizes the selected news articles using a generative AI model, such as GPT-3.

[1033] Example prompt: "Write a one-minute summary of the following article:\n\nA presentation on the latest AI technology..."

[1034] 5. Video generation tool: Converts summarized news articles into short videos by integrating text, related images, and audio narration.

[1035] 6. Distribution method: The server distributes the generated video at the time specified by the user (e.g., morning, afternoon, or evening).

[1036] 7. Display method: The distributed video is displayed on the user's device such as a smartphone, smart glasses, or head-mounted display.

[1037] Specific examples

[1038] For example, if user A frequently browses articles related to "technology" on a news site, the system operates as follows:

[1039] The server collects user A's behavioral data (article browsing history, click events, viewing time, etc.).

[1040] Analyze the collected data and determine that User A has a strong interest in technology.

[1041] Latest technology-related news articles are selected and summarized using a generative AI model.

[1042] A summary of the news article is visualized to create a one-minute video that integrates text, related images, and audio narration.

[1043] If user A selects "Morning News," a video link will be sent at the set time.

[1044] This allows User A to efficiently obtain the latest technology-related news in the shortest possible time.

[1045] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1046] Step 1: Collect user information

[1047] The server collects behavioral data such as user browsing history, click events, and browsing time, which allows it to understand which topics users are interested in. Specifically, the server collects the URLs of pages visited by users, the length of time they stayed on the site, and the number of links they clicked, and stores this data in a database.

[1048] Input: User behavior data

[1049] Data processing / data calculation: Collecting behavioral data and storing it in a database

[1050] Output: User behavior information stored in a database

[1051] Step 2: Analyzing user behavior data

[1052] The server analyzes the collected behavioral data to identify the user's interests. The analysis uses machine learning models to identify which categories and topics the user is interested in. For example, if a user views many articles related to "technology," it is determined that the user has a strong interest in that category.

[1053] Input: User behavior information stored in a database

[1054] Data processing / data calculation: Data analysis using machine learning models

[1055] Output: User interest information

[1056] Step 3: Selecting news articles

[1057] The server selects relevant news articles from the news database based on the analyzed user interest information by filtering the latest news articles that match the user's interests.

[1058] Input: User interest information, news article database

[1059] Data processing / data calculation: filtering news articles

[1060] Output: Selected news articles

[1061] Step 4: Summarize the news article

[1062] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The generative AI model receives the article's content as a prompt, and generates a summary based on that. For example, the prompt might be, "Please summarize the following article in a way that can be explained in less than one minute."

[1063] Input: Selected news articles

[1064] Data processing / data calculation: Summary generation using generative AI models

[1065] Output: A summarized news article

[1066] Step 5: Visualize the news story

[1067] The server then creates a video of the summarized news article, which involves integrating the text, associated images, and audio narration. For example, the server reads the generated summary text as an audio narration and combines it with associated images to create a video.

[1068] Input: Summarized news article, associated images

[1069] Data processing / data calculation: text, image, and voice integration

[1070] Output: Visualized news article

[1071] Step 6: Stream and display

[1072] The server distributes the generated video at the time specified by the user. A video link is sent to the user's device, such as a smartphone, smart glasses, or head-mounted display, and the user can view the video by receiving the notification. Specifically, a notification is sent to the user's device at the specified time, and the user can open the video from the notification and view it.

[1073] Input: Animated news article, user-specified time

[1074] Data processing / data calculation: generating and sending notifications

[1075] Output: Video link notification sent to the user's device

[1076] This allows users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[1077] 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.

[1078] Overall overview

[1079] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1080] Program Structure

[1081] User information collection method

[1082] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[1083] Analysis means

[1084] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1085] Selection method

[1086] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1087] Summary tools

[1088] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[1089] Image generation means

[1090] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[1091] Delivery Method

[1092] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[1093] Specific examples

[1094] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[1095] Collection Phase

[1096] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, if user A browses an article on "latest smartphone technology" and the camera and microphone detect a smile and an interested tone of voice, that information is stored in the database.

[1097] Analysis Phase

[1098] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1099] Selection Phase

[1100] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[1101] Summary Phase

[1102] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1103] Image generation phase

[1104] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1105] Delivery Phase

[1106] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1107] This system allows user A to efficiently obtain the latest news of interest with positive emotions in the shortest possible time.

[1108] The processing flow will be explained below.

[1109] Step 1:

[1110] The server collects user behavioral data and emotional information in real time. When a user browses a news site, the server records the article's URL, viewing time, click events, and scrolling information. It also uses a camera and microphone to recognize the user's facial expressions and tone of voice, and an emotion engine analyzes and stores the emotional information in a database. For example, if a user browses an article about the latest smartphone technology, stays there for a long time, and the camera detects a smile, the server records that information.

[1111] Step 2:

[1112] The server analyzes the collected behavioral and emotional data. It uses machine learning models and text analysis engines to identify which topics a user is interested in and what emotions they have. For example, based on a user's past browsing history, it may determine that they have a strong interest in technology-related topics and show positive emotions while viewing those articles.

[1113] Step 3:

[1114] The server selects relevant latest news articles from the news database based on the analysis results. It then filters appropriate articles based on the user's topics of interest and emotional information identified from the analysis results. For example, it selects technology-related articles that evoke positive emotions in users, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1115] Step 4:

[1116] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the article content is summarized within one minute. Furthermore, the server reflects the user's emotional information obtained through an emotion engine, creating summaries that emphasize a positive tone and key points. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone, with the following sentence: "The latest AI technology has been announced. As a result..."

[1117] Step 5:

[1118] The server then creates a video from the summarized news article. Using a video generation framework, it integrates the summary text, related images, and audio narration to create a video with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and a cheerful narration.

[1119] Step 6:

[1120] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[1121] Step 7:

[1122] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[1123] Step 8:

[1124] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[1125] These processing steps allow users to efficiently obtain the latest news that interests them, and also provide content based on emotion information.

[1126] Example 2

[1127] 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."

[1128] Conventional news delivery systems make it difficult for users to efficiently obtain content tailored to their interests and emotions. With so much news available, it is difficult to quickly grasp only the news that meets a specific user need. Furthermore, there are no systems that utilize emotional information to select news that will interest users and deliver it at the optimal time. As a result, users are often overwhelmed by the sheer volume of information, and may miss important news.

[1129] 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.

[1130] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This makes it possible to summarize optimal news articles based on the user's behavioral data and emotional information, and to distribute the summarized news in the form of images. This allows users to quickly and efficiently obtain news that matches their interests and receive it with positive emotions.

[1131] "User information collection means" refers to means for collecting user behavior data (browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[1132] The "analysis means" is a means for analyzing collected user behavioral data and emotional information to identify the user's topics of interest and emotional state.

[1133] The "selection means" is a means for selecting related news articles from the news database based on the user's interest information and emotional state identified by the analysis means.

[1134] The "summarization means" is a means for converting the news articles selected by the selection means into short summaries using a generative AI model.

[1135] The "image generating means" is a means for visualizing the news article summarized by the summarizing means by integrating the text, related images, audio narration, etc.

[1136] The "distribution means" is a means for distributing and notifying the user of the video generated by the video generation means at a specified time.

[1137] A "generative AI model" is an artificial intelligence technology used to generate news article summaries tailored to the user's preferences, such as a language generation model.

[1138] MODE FOR CARRYING OUT THE INVENTION

[1139] The system of the present invention summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1140] User information collection method

[1141] The server uses user information collection means to collect user behavioral data and emotional information. This includes the URL of the article the user viewed, the viewing time, the links clicked, scrolling information, and sensors such as the camera and microphone. This allows the server to recognize the user's facial expressions and tone of voice, analyze the emotional information using an emotion engine, and store it in a database. For example, the server can detect the smiling face and cheerful voice of a user viewing an article on "latest smartphone technology."

[1142] Analysis means

[1143] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1144] Selection method

[1145] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1146] Summary tools

[1147] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[1148] Image generation means

[1149] The server then uses a video generation tool to visualize the summarized news articles. This tool integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[1150] Delivery Method

[1151] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). After receiving the notification, the user can watch the video.

[1152] Specific examples

[1153] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional response to them, the system would operate as follows:

[1154] 1. Collection Phase:

[1155] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, when user A browses an article on "latest smartphone technology," the camera and microphone detect a smile and an interesting tone of voice, and the information is stored in a database.

[1156] 2. Analysis phase:

[1157] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1158] 3. Selection Phase:

[1159] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[1160] 4. Summary Phase:

[1161] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1162] 5. Image generation phase:

[1163] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1164] 6. Delivery Phase:

[1165] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1166] Prompt Sentence Examples

[1167] "Summarize the latest technology news in a positive tone in under one minute."

[1168] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1169] Step 1:

[1170] The server uses the user information collection means to collect user behavior data and emotion information.

[1171] Input: URLs of articles viewed by users, viewing time, links clicked, scrolling information, facial expressions and tone of voice from camera and microphone.

[1172] How it works: An application installed on the device records the user's behavioral data and simultaneously uses the camera and microphone to analyze facial expressions and tone of voice in real time.

[1173] Data processing: In the process of saving the above data in the database, it is saved in an organized format for each user.

[1174] Output: Behavioral data and emotional information stored in a database.

[1175] Step 2:

[1176] The server analyzes the collected behavioral data and emotional information using an analysis means.

[1177] Input: Behavioral data and emotional information collected in step 1.

[1178] How it works: The server uses machine learning models to analyze the content of articles viewed by the user, their browsing patterns, and even their emotional information to identify the user's interests and emotional state.

[1179] Data calculation: Machine learning algorithms are used to classify and identify topics of interest and emotional states, and output these as results.

[1180] Output: Identified user interest topics and emotional state.

[1181] Step 3:

[1182] The server selects relevant news articles from a news database based on the analysis results.

[1183] Input: User's interest topics and emotional state obtained in step 2, and a news database.

[1184] What it does: The server queries the news database to find the latest news articles that match the user's topic of interest and select them based on their emotional state.

[1185] Data processing: filtering and selection of news articles.

[1186] Output: Selected news articles.

[1187] Step 4:

[1188] The server summarizes the selected news articles.

[1189] Input: News articles selected in step 3.

[1190] How it works: The server uses a generative AI model (e.g., GPT-3) to set prompts and convert news articles into summaries that can be conveyed in under one minute, adjusting tone and key points based on the user's emotional information.

[1191] Data calculation: Generating summaries using generative AI models.

[1192] Output: A summarized news article.

[1193] Step 5:

[1194] The server visualizes the summarized news articles.

[1195] Input: The news article summarized in step 4.

[1196] What it does: Integrates text, related images, and audio narration to generate a video with a tone and expression that matches the user's emotions.

[1197] Data processing: converting text to video, integrating images and narration.

[1198] Output: A visualized news article.

[1199] Step 6:

[1200] The server distributes the generated video to the user.

[1201] Input: The video generated in step 5 and the user-specified broadcast time.

[1202] Specific operation: The server sends notifications and distributes video according to the specified time period (e.g., morning, afternoon, evening).

[1203] Data Calculation: Notification generation based on delivery schedule.

[1204] Output: The video notification and video content delivered to the user.

[1205] (Application example 2)

[1206] 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."

[1207] In today's information society, users have access to a large amount of news information, but it is difficult to efficiently understand all of the information and extract only the content that interests them. Furthermore, because the amount of news content is so vast, users often waste time on information that is not important to them. Furthermore, because how users perceive news is greatly influenced by their emotional state, there is a need to provide content that is tailored to each individual. The present invention aims to provide a system that enables efficient and appropriate summarization and delivery of news information based on user behavioral data and emotional information.

[1208] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user information collection means, an analysis means for analyzing user behavior information and emotion information collected by the user information collection means, a selection means for selecting news articles based on the user interest information and emotion information analyzed by the analysis means, a summarization means for summarizing the news articles selected by the selection means using a generative AI model, an image generation means for visualizing the news articles summarized by the summarization means, and a distribution means for distributing the images generated by the image generation means at a time specified by the user. This enables users to efficiently obtain news that is relevant to their areas of interest and leaves them with positive emotions in a minimum amount of time.

[1209] The "user information collection means" is a means for collecting a user's browsing history, click events, browsing time, and emotion information.

[1210] The "analysis means" is a means for analyzing the user behavior information and emotion information collected by the user information collection means, and identifying the user's interests and emotions.

[1211] The "selection means" is a means for selecting a news article based on the user interest information and emotion information analyzed by the analysis means.

[1212] The "summarization means" is a means for summarizing the news articles selected by the selection means using a generative AI model.

[1213] The "image generating means" is a means for visualizing the news article summarized by the summarizing means.

[1214] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[1215] A "generative AI model" is an artificial intelligence model used to generate summaries of news articles.

[1216] "Emotion information" is information that indicates the emotional state of the user, analyzed from their facial expressions and tone of voice.

[1217] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This system allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1218] Overall system configuration

[1219] User information collection method

[1220] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[1221] Analysis means

[1222] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and an emotional analysis engine are used to identify which topics the user is interested in and their emotional state. For example, if the user frequently views technology-related articles and expresses positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1223] Selection method

[1224] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1225] Summary tools

[1226] The server uses a generative AI model to summarize the news articles selected by the selection method. By inputting prompt sentences into the generative AI model, it generates a short summary that can convey the original article content in less than one minute. Furthermore, it reflects the user's emotional information obtained through an emotion engine and adjusts the tone and key points of the article. For example, for a user with a positive emotion, the summary will emphasize success stories and positive news.

[1227] Image generation means

[1228] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[1229] Delivery Method

[1230] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[1231] Specific examples of hardware and software used

[1232] Hardware:

[1233] Smartphone: Uses camera and microphone to capture user facial expressions and tone of voice.

[1234] Server: Analyzes and processes data.

[1235] software:

[1236] GPT-3 (generative AI model): Generates summaries of news articles.

[1237] Sentiment analysis engine: Analyzes user emotions.

[1238] Text-to-Video Software: Convert text to video.

[1239] Specific examples

[1240] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[1241] Collection phase:

[1242] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[1243] For example, if User A views an article on "Latest Smartphone Technology" and the camera and microphone detect a smile and an interesting tone of voice, that information will be stored in the database.

[1244] Analysis phase:

[1245] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1246] Selection phase:

[1247] The server selects the latest technology-related news articles from a news database.

[1248] For example, it narrows down articles to match users' positive emotions, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[1249] Summary phase:

[1250] The server summarizes the selected articles using a generative AI model.

[1251] For example, "Announcement of new AI technology" can be summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1252] Image Generation Phase:

[1253] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1254] Delivery phase:

[1255] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1256] Prompt Sentence Examples

[1257] Summarize news articles and adjust tone depending on emotional information.

[1258] User Sentiment: Positive

[1259] Article content: The latest AI technology has been announced. It is expected to revolutionize various industries, with applications in the medical and financial fields attracting particular attention.

[1260] Abstract: The latest AI technology has been unveiled, promising revolutionary changes in the fields of medicine and finance.

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The server uses user information collection means to collect user behavioral data and emotional information. It obtains the URL of the article the user is viewing, the viewing time, click events, and scrolling information, while also recording the user's facial expressions and tone of voice in real time via a camera and microphone. This data is sent to the server and stored in a database.

[1264] Input: User browsing history, click events, browsing time, facial expressions, tone of voice

[1265] Output: Behavioral data and emotional information stored in a database

[1266] Step 2:

[1267] The server analyzes the collected behavioral data and emotional information using analytical means, using machine learning models and an emotional analysis engine to identify which topics users are interested in and their emotional state at the time.

[1268] Input: Behavioral data and emotional information

[1269] Output: User's interest topics and emotional state

[1270] Step 3:

[1271] The server selects relevant latest news articles from the news database based on the analysis results, filters news articles corresponding to the topics and emotional information that the user has expressed interest in, and selects the most relevant articles.

[1272] Input: User's interest topics and emotional state

[1273] Output: A list of selected news articles

[1274] Step 4:

[1275] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The user inputs a prompt into the generative AI model, which generates a summary that conveys the article's content in a short amount of time. The generated summary reflects the user's emotional information and adjusts the article's tone and key points.

[1276] Input: Selected news article, prompt

[1277] Output: A summarized news article

[1278] Step 5:

[1279] The server then uses the video generation means to visualize the news articles summarized by the summarization means. The server integrates the text, related images, and audio narration to create a video using a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[1280] Input: A summarized news article, associated images, and audio files

[1281] Output: Finished video

[1282] Step 6:

[1283] The server distributes the generated video to the user using a distribution method. It also sends a notification to the user at a specific time period (e.g., morning, afternoon, or evening) designated by the user, informing them that the video is available for viewing. The user receives the notification and can begin viewing the video.

[1284] Input: Finished video, user-specified duration

[1285] Output: Notification to the user and video distribution

[1286] 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.

[1287] 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.

[1288] 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.

[1289] [Fourth embodiment]

[1290] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1291] 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.

[1292] 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).

[1293] 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.

[1294] 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.

[1295] 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).

[1296] 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. 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.

[1297] 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.

[1298] 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.

[1299] 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.

[1300] 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.

[1301] 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.

[1302] 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."

[1303] Overall overview

[1304] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain the news information they need efficiently and quickly.

[1305] Program Structure

[1306] User information collection method

[1307] The server uses a user information collection means to collect user behavior data, which collects information such as the URL of the article the user viewed, the viewing time, the link clicked, and the scrolling speed, and stores the collected information in a database.

[1308] Analysis means

[1309] The server analyzes the collected behavioral data using analytics, such as text analytics and machine learning models, to identify which topics users are interested in.

[1310] Selection method

[1311] The server selects news articles based on the user's interest information identified by the analysis means, and filters the most recent articles in the news database to select the articles most relevant to the user.

[1312] Summary tools

[1313] The server then summarises the news articles selected by the selection means using a generative AI model (e.g., GPT-3) to create a short summary of each article that can be conveyed in under one minute.

[1314] Image generation means

[1315] The server visualizes the summarized news articles using a video generation means that integrates the text, associated images, and audio narration to create a short video.

[1316] Delivery Method

[1317] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[1318] Specific examples

[1319] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[1320] Collection Phase

[1321] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data is recorded.

[1322] Analysis Phase

[1323] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[1324] Selection Phase

[1325] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[1326] Summary Phase

[1327] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[1328] Image generation phase

[1329] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[1330] Delivery Phase

[1331] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1332] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[1333] The processing flow will be explained below.

[1334] Step 1:

[1335] The server collects user behavior data in real time. When a user browses a news site, it records the article's URL, viewing time, click events, scrolling information, etc. For example, if a user browses an article on "latest smartphone technology" and stays there for a long time, that information is stored in a database.

[1336] Step 2:

[1337] The server analyzes the collected behavioral data, using machine learning models and text analysis engines to identify which topics the user is interested in. For example, based on the user's past browsing history, it may determine that the user is interested in technology articles.

[1338] Step 3:

[1339] Based on the analysis results, the server selects relevant latest news articles from the news database, narrowing down the articles to those that match the user's interests, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[1340] Step 4:

[1341] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the original article content is summarized in a short, approximately one-minute sentence. For example, "Announcement of new AI technology" is summarized as "The latest AI technology has been announced. As a result..."

[1342] Step 5:

[1343] The server then creates a video from the summarized news article. It uses a video generation framework to integrate the summary text, related images, and audio narration to create a video of less than one minute. For example, it adds images and visual effects related to the summary, "The latest AI technology has been announced."

[1344] Step 6:

[1345] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[1346] Step 7:

[1347] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[1348] Step 8:

[1349] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[1350] These processing steps allow the user to efficiently obtain the latest news that interests him or her in a short amount of time.

[1351] Example 1

[1352] 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."

[1353] In modern society, users are exposed to a huge amount of information, making it difficult to efficiently obtain the news information they need. Furthermore, because users do not have the time to browse news articles individually, there is a growing need to quickly grasp important information. However, there is a lack of systems that can select relevant news based on users' interests and behavioral history, and then present it in a format that is easily understandable.

[1354] 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.

[1355] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This allows the server to select news of interest based on the user's behavior data, summarize it, and visualize it, thereby enabling the user to efficiently obtain highly relevant news information in a short amount of time.

[1356] The "user information collection means" is a means for collecting behavioral data such as a user's browsing history, click events, browsing time, and scrolling speed.

[1357] The "analysis means" is a means for analyzing collected user behavior data using a machine learning model to identify user interests.

[1358] The "selection means" is a means for selecting news articles based on the user's interest information identified by the analysis means.

[1359] The "summarization method" is a method for summarizing selected news articles into short sentences using a generative AI model.

[1360] The "video generation means" is a means for creating a video of a summarized news article by integrating text, related images, and audio narration.

[1361] The "distribution means" is a means for distributing the generated video in accordance with a specific time period (morning, afternoon, or evening) selected by the user.

[1362] Overall overview

[1363] The present invention is a system that summarizes news based on a user's interests and behavioral history, visualizes the news, and delivers it to the user. This system allows users to obtain the news information they need efficiently and quickly.

[1364] Program Structure

[1365] User information collection method

[1366] The server uses a user information collection means to collect user behavior data. This means collects information such as the URL of the article the user viewed, the viewing time, the links clicked, and the scrolling speed, and stores it in a database. Specific software used may include a web tracking tool or a data collection library.

[1367] Analysis means

[1368] The server analyzes the collected behavioral data using analytics, which can include text analysis and machine learning models to identify topics that users are interested in. Machine learning algorithms can be implemented using programming languages ​​such as Python and R.

[1369] Selection method

[1370] The server selects news articles based on the user's interests identified by the analysis means, filtering the most relevant articles for the user from the latest articles in a news database (e.g., MongoDB).

[1371] Summary tools

[1372] The server summarizes the news articles selected by the selection means using the summarization means. Using a generative AI model (e.g., GPT-3), each article is converted into a short summary that can be explained in less than one minute. An example of a prompt sentence is, "Please summarize an article about the latest AI technology announcement. Please write a short sentence that can be explained in less than one minute."

[1373] Image generation means

[1374] The server then visualizes the summarized news articles using a video generation tool that integrates the text, related images, and audio narration to create short videos. FFmpeg is used as the video generation software.

[1375] Delivery Method

[1376] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). For example, if the user selects "morning news," the server notifies the user that the generated video will be available for viewing at 7:00 a.m. After receiving the notification, the user can view the video.

[1377] Specific examples

[1378] For example, if user A frequently reads technology-related articles on a news site, the system operates as follows:

[1379] Collection Phase

[1380] The server collects user A's behavioral data (article browsing history, click events, browsing time, scrolling speed, etc.). For example, if user A browses an article on "latest smartphone technology" and stays on the page for a long time, that data will be recorded.

[1381] Analysis Phase

[1382] The server analyzes the collected data and determines that User A has a strong interest in technology. This analysis is performed using a machine learning model.

[1383] Selection Phase

[1384] The server selects the latest technology-related news articles from a news database, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[1385] Summary Phase

[1386] The server then uses a generative AI model to summarize the selected articles, turning, for example, a "New AI Technology Announcement" into an explainable summary in under one minute.

[1387] Image generation phase

[1388] The server then converts the summarized news article into a video, combining the text, relevant images, and audio narration to produce a one-minute video that reads, "The latest AI technology has been unveiled..."

[1389] Delivery Phase

[1390] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1391] This system allows user A to efficiently obtain the latest news of interest in the shortest possible time.

[1392] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1393] Step 1:

[1394] The server uses the user information collection means to collect user behavior data.

[1395] How it works: When a user visits a news site, the server collects data in real time, such as the article URL, viewing time, click events, and scrolling speed.

[1396] Input: User behavior data (article URL, viewing time, click events, scroll speed)

[1397] Output: Collected user behavior data is saved in a database

[1398] Step 2:

[1399] The server analyzes the collected behavioral data using an analysis means.

[1400] How it works: The server uses machine learning models (e.g., Python's Scikit-learn library) to analyze text and behavioral patterns to identify topics that interest the user.

[1401] Input: User behavior data collected in step 1

[1402] Output: Identification of user topics of interest

[1403] Step 3:

[1404] The server selects news articles based on the user's interest information identified by the analysis means.

[1405] What it does: The server filters the latest articles from a news database (e.g., MongoDB) that are most relevant to the user.

[1406] Input: User interest topics identified in step 2, news database

[1407] Output: A list of relevant news articles

[1408] Step 4:

[1409] The server summarizes the selected news articles using a summarizing means.

[1410] How it works: The server inputs selected news articles into a generative AI model (e.g., GPT-3) and generates summaries using prompts such as, "Please summarize an article about the latest AI technology announcement in a short sentence that can be explained in under one minute."

[1411] Input: News articles selected in step 3

[1412] Output: Summarized news article text

[1413] Step 5:

[1414] The server visualizes the summarized news article using an image generating means.

[1415] Specific operation: The server generates a video by integrating the text summary, related images, and audio narration. Specifically, it uses video generation software such as FFmpeg to create video content of less than one minute.

[1416] Input: Text of the news article summarized in step 4, associated images, and audio narration

[1417] Output: Generated video file

[1418] Step 6:

[1419] The server distributes the generated video to the user through a distribution means.

[1420] Specific operation: The server sends a notification according to the time period specified by the user (e.g., morning, afternoon, or evening) and distributes the generated video. For example, if the user selects "Morning News," the server sends a notification at 7:00 a.m. and starts distributing the video.

[1421] Input: Video file generated in step 5, user distribution settings

[1422] Output: Notifications sent to users and videos delivered

[1423] By implementing the above steps, this system is able to summarize news based on the user's interests, visualize it, and deliver it efficiently.

[1424] (Application example 1)

[1425] 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."

[1426] Conventional news acquisition methods require users to manually search for news relevant to their interests from a vast amount of information, which requires time and effort. In addition, there is a lack of a means to quickly and efficiently understand the content of interest, making it difficult to provide useful news information to users.

[1427] 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.

[1428] In this invention, the server includes user information collection means, analysis means for analyzing user behavior information collected by the user information collection means, selection means for selecting news articles based on the user interest information analyzed by the analysis means, summarization means for summarizing the news articles selected by the selection means, image generation means for visualizing the news articles summarized by the summarization means, distribution means for distributing the images generated by the image generation means at a specified time, and display means for displaying the images distributed by the distribution means on the user's terminal. This enables users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[1429] The "user information collection means" is a means for collecting user behavior data (for example, browsing history, click events, browsing time, etc.).

[1430] "Analysis means" refers to means that uses machine learning models to analyze user behavior information and identify user interests.

[1431] The "selection means" is a means for selecting an appropriate news article based on the user interest information analyzed by the analysis means.

[1432] The "summarization means" is a means for briefly summarizing the news articles selected by the selection means using a generative AI model (e.g., a generative AI model).

[1433] The "video generating means" is a means for generating a short video by integrating text, related images, and audio narration to visualize the news article summarized by the summarizing means.

[1434] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[1435] The "display means" is a means for displaying the video distributed by the distribution means on a user's terminal (for example, a smartphone, smart glasses, a head-mounted display, etc.).

[1436] This invention is a system that summarizes news based on a user's behavioral history, visualizes it, and distributes it. The purpose is to enable users to obtain necessary news information efficiently in a short time.

[1437] System configuration

[1438] The system includes the following means:

[1439] 1. How we collect user information

[1440] 2. Analysis method

[1441] 3. Selection method

[1442] 4. Summary tools

[1443] 5. Image Generation Method

[1444] 6. Distribution Method

[1445] 7. Display means

[1446] Hardware and software used

[1447] Hardware: Servers, user devices such as smartphones, smart glasses, and head-mounted displays.

[1448] software:

[1449] requests: An HTTP request library for retrieving news article data.

[1450] openai: A generative AI model (e.g. GPT-3) library for summarizing text.

[1451] datetime: A standard library for getting the current time and sending notifications to the user.

[1452] Process Overview

[1453] 1. User information collection means: The server collects user behavior data (e.g., browsing history, click events, browsing time, etc.), which allows us to understand which topics the user is interested in.

[1454] 2. Analysis: The server analyzes the collected behavioral data to identify user interests. This analysis is performed using machine learning models.

[1455] 3. Selection method: Based on the analysis results, the server selects news articles related to the user's interests from the news database.

[1456] 4. Summarization: The server summarizes the selected news articles using a generative AI model, such as GPT-3.

[1457] Example prompt: "Write a one-minute summary of the following article:\n\nA presentation on the latest AI technology..."

[1458] 5. Video generation tool: Converts summarized news articles into short videos by integrating text, related images, and audio narration.

[1459] 6. Distribution method: The server distributes the generated video at the time specified by the user (e.g., morning, afternoon, or evening).

[1460] 7. Display method: The distributed video is displayed on the user's device such as a smartphone, smart glasses, or head-mounted display.

[1461] Specific examples

[1462] For example, if user A frequently browses articles related to "technology" on a news site, the system operates as follows:

[1463] The server collects user A's behavioral data (article browsing history, click events, viewing time, etc.).

[1464] Analyze the collected data and determine that User A has a strong interest in technology.

[1465] Latest technology-related news articles are selected and summarized using a generative AI model.

[1466] A summary of the news article is visualized to create a one-minute video that integrates text, related images, and audio narration.

[1467] If user A selects "Morning News," a video link will be sent at the set time.

[1468] This allows User A to efficiently obtain the latest technology-related news in the shortest possible time.

[1469] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1470] Step 1: Collect user information

[1471] The server collects behavioral data such as user browsing history, click events, and browsing time, which allows it to understand which topics users are interested in. Specifically, the server collects the URLs of pages visited by users, the length of time they stayed on the site, and the number of links they clicked, and stores this data in a database.

[1472] Input: User behavior data

[1473] Data processing / data calculation: Collecting behavioral data and storing it in a database

[1474] Output: User behavior information stored in a database

[1475] Step 2: Analyzing user behavior data

[1476] The server analyzes the collected behavioral data to identify the user's interests. The analysis uses machine learning models to identify which categories and topics the user is interested in. For example, if a user views many articles related to "technology," it is determined that the user has a strong interest in that category.

[1477] Input: User behavior information stored in a database

[1478] Data processing / data calculation: Data analysis using machine learning models

[1479] Output: User interest information

[1480] Step 3: Selecting news articles

[1481] The server selects relevant news articles from the news database based on the analyzed user interest information by filtering the latest news articles that match the user's interests.

[1482] Input: User interest information, news article database

[1483] Data processing / data calculation: filtering news articles

[1484] Output: Selected news articles

[1485] Step 4: Summarize the news article

[1486] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The generative AI model receives the article's content as a prompt, and generates a summary based on that. For example, the prompt might be, "Please summarize the following article in a way that can be explained in less than one minute."

[1487] Input: Selected news articles

[1488] Data processing / data calculation: Summary generation using generative AI models

[1489] Output: A summarized news article

[1490] Step 5: Visualize the news story

[1491] The server then creates a video of the summarized news article, which involves integrating the text, associated images, and audio narration. For example, the server reads the generated summary text as an audio narration and combines it with associated images to create a video.

[1492] Input: Summarized news article, associated images

[1493] Data processing / data calculation: text, image, and voice integration

[1494] Output: Visualized news article

[1495] Step 6: Stream and display

[1496] The server distributes the generated video at the time specified by the user. A video link is sent to the user's device, such as a smartphone, smart glasses, or head-mounted display, and the user can view the video by receiving the notification. Specifically, a notification is sent to the user's device at the specified time, and the user can open the video from the notification and view it.

[1497] Input: Animated news article, user-specified time

[1498] Data processing / data calculation: generating and sending notifications

[1499] Output: Video link notification sent to the user's device

[1500] This allows users to efficiently obtain news related to their interests in a short amount of time and to visually understand it.

[1501] 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.

[1502] Overall overview

[1503] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1504] Program Structure

[1505] User information collection method

[1506] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[1507] Analysis means

[1508] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1509] Selection method

[1510] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1511] Summary tools

[1512] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[1513] Image generation means

[1514] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[1515] Delivery Method

[1516] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[1517] Specific examples

[1518] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[1519] Collection Phase

[1520] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, if user A browses an article on "latest smartphone technology" and the camera and microphone detect a smile and an interested tone of voice, that information is stored in the database.

[1521] Analysis Phase

[1522] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1523] Selection Phase

[1524] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[1525] Summary Phase

[1526] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1527] Image generation phase

[1528] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1529] Delivery Phase

[1530] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1531] This system allows user A to efficiently obtain the latest news of interest with positive emotions in the shortest possible time.

[1532] The processing flow will be explained below.

[1533] Step 1:

[1534] The server collects user behavioral data and emotional information in real time. When a user browses a news site, the server records the article's URL, viewing time, click events, and scrolling information. It also uses a camera and microphone to recognize the user's facial expressions and tone of voice, and an emotion engine analyzes and stores the emotional information in a database. For example, if a user browses an article about the latest smartphone technology, stays there for a long time, and the camera detects a smile, the server records that information.

[1535] Step 2:

[1536] The server analyzes the collected behavioral and emotional data. It uses machine learning models and text analysis engines to identify which topics a user is interested in and what emotions they have. For example, based on a user's past browsing history, it may determine that they have a strong interest in technology-related topics and show positive emotions while viewing those articles.

[1537] Step 3:

[1538] The server selects relevant latest news articles from the news database based on the analysis results. It then filters appropriate articles based on the user's topics of interest and emotional information identified from the analysis results. For example, it selects technology-related articles that evoke positive emotions in users, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1539] Step 4:

[1540] The server summarizes the selected news articles. Using a generative AI model (e.g., GPT-3), the article content is summarized within one minute. Furthermore, the server reflects the user's emotional information obtained through an emotion engine, creating summaries that emphasize a positive tone and key points. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone, with the following sentence: "The latest AI technology has been announced. As a result..."

[1541] Step 5:

[1542] The server then creates a video from the summarized news article. Using a video generation framework, it integrates the summary text, related images, and audio narration to create a video with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and a cheerful narration.

[1543] Step 6:

[1544] The server prepares to distribute the generated video at the time specified by the user. For example, if the user selects "Morning News," the distribution schedule is set to 7:00 AM.

[1545] Step 7:

[1546] The server will send a notification to the user at the specified time, for example, a message saying "Your personalized news summary is ready."

[1547] Step 8:

[1548] The user receives a notification and can view the video in response to the notification. The user can click on the video to play it on their device and view the latest news summary.

[1549] These processing steps allow users to efficiently obtain the latest news that interests them, and also provide content based on emotion information.

[1550] Example 2

[1551] 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."

[1552] Conventional news delivery systems make it difficult for users to efficiently obtain content tailored to their interests and emotions. With so much news available, it is difficult to quickly grasp only the news that meets a specific user need. Furthermore, there are no systems that utilize emotional information to select news that will interest users and deliver it at the optimal time. As a result, users are often overwhelmed by the sheer volume of information, and may miss important news.

[1553] 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.

[1554] In this invention, the server includes a user information collection means, an analysis means, a selection means, a summarization means, an image generation means, and a distribution means. This makes it possible to summarize optimal news articles based on the user's behavioral data and emotional information, and to distribute the summarized news in the form of images. This allows users to quickly and efficiently obtain news that matches their interests and receive it with positive emotions.

[1555] "User information collection means" refers to means for collecting user behavior data (browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[1556] The "analysis means" is a means for analyzing collected user behavioral data and emotional information to identify the user's topics of interest and emotional state.

[1557] The "selection means" is a means for selecting related news articles from the news database based on the user's interest information and emotional state identified by the analysis means.

[1558] The "summarization means" is a means for converting the news articles selected by the selection means into short summaries using a generative AI model.

[1559] The "image generating means" is a means for visualizing the news article summarized by the summarizing means by integrating the text, related images, audio narration, etc.

[1560] The "distribution means" is a means for distributing and notifying the user of the video generated by the video generation means at a specified time.

[1561] A "generative AI model" is an artificial intelligence technology used to generate news article summaries tailored to the user's preferences, such as a language generation model.

[1562] MODE FOR CARRYING OUT THE INVENTION

[1563] The system of the present invention summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1564] User information collection method

[1565] The server uses user information collection means to collect user behavioral data and emotional information. This includes the URL of the article the user viewed, the viewing time, the links clicked, scrolling information, and sensors such as the camera and microphone. This allows the server to recognize the user's facial expressions and tone of voice, analyze the emotional information using an emotion engine, and store it in a database. For example, the server can detect the smiling face and cheerful voice of a user viewing an article on "latest smartphone technology."

[1566] Analysis means

[1567] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and text analysis engines are used to identify which topics the user is interested in and their emotional state. For example, if the user views many technology-related articles and shows positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1568] Selection method

[1569] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1570] Summary tools

[1571] The server summarizes the news articles selected by the selection method. Using a generative AI model (e.g., GPT-3), it creates a short summary that conveys the original article content in less than one minute. Furthermore, it adjusts the tone and key points of the article based on the user's emotional information obtained through an emotion engine. For example, for a user with a positive emotion, the server creates a summary that emphasizes success stories and positive news.

[1572] Image generation means

[1573] The server then uses a video generation tool to visualize the summarized news articles. This tool integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[1574] Delivery Method

[1575] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). After receiving the notification, the user can watch the video.

[1576] Specific examples

[1577] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional response to them, the system would operate as follows:

[1578] 1. Collection Phase:

[1579] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.). For example, when user A browses an article on "latest smartphone technology," the camera and microphone detect a smile and an interesting tone of voice, and the information is stored in a database.

[1580] 2. Analysis phase:

[1581] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1582] 3. Selection Phase:

[1583] The server selects the latest technology-related news articles from a news database, narrowing down the articles to those that match the user's positive emotions, such as "announcements of new AI technology" or "reviews of the latest smart devices."

[1584] 4. Summary Phase:

[1585] The server then uses a generative AI model to summarize the selected articles. For example, an article titled "Announcement of new AI technology" is summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1586] 5. Image generation phase:

[1587] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1588] 6. Delivery Phase:

[1589] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1590] Prompt Sentence Examples

[1591] "Summarize the latest technology news in a positive tone in under one minute."

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1:

[1594] The server uses the user information collection means to collect user behavior data and emotion information.

[1595] Input: URLs of articles viewed by users, viewing time, links clicked, scrolling information, facial expressions and tone of voice from camera and microphone.

[1596] How it works: An application installed on the device records the user's behavioral data and simultaneously uses the camera and microphone to analyze facial expressions and tone of voice in real time.

[1597] Data processing: In the process of saving the above data in the database, it is saved in an organized format for each user.

[1598] Output: Behavioral data and emotional information stored in a database.

[1599] Step 2:

[1600] The server analyzes the collected behavioral data and emotional information using an analysis means.

[1601] Input: Behavioral data and emotional information collected in step 1.

[1602] How it works: The server uses machine learning models to analyze the content of articles viewed by the user, their browsing patterns, and even their emotional information to identify the user's interests and emotional state.

[1603] Data calculation: Machine learning algorithms are used to classify and identify topics of interest and emotional states, and output these as results.

[1604] Output: Identified user interest topics and emotional state.

[1605] Step 3:

[1606] The server selects relevant news articles from a news database based on the analysis results.

[1607] Input: User's interest topics and emotional state obtained in step 2, and a news database.

[1608] What it does: The server queries the news database to find the latest news articles that match the user's topic of interest and select them based on their emotional state.

[1609] Data processing: filtering and selection of news articles.

[1610] Output: Selected news articles.

[1611] Step 4:

[1612] The server summarizes the selected news articles.

[1613] Input: News articles selected in step 3.

[1614] How it works: The server uses a generative AI model (e.g., GPT-3) to set prompts and convert news articles into summaries that can be conveyed in under one minute, adjusting tone and key points based on the user's emotional information.

[1615] Data calculation: Generating summaries using generative AI models.

[1616] Output: A summarized news article.

[1617] Step 5:

[1618] The server visualizes the summarized news articles.

[1619] Input: The news article summarized in step 4.

[1620] What it does: Integrates text, related images, and audio narration to generate a video with a tone and expression that matches the user's emotions.

[1621] Data processing: converting text to video, integrating images and narration.

[1622] Output: A visualized news article.

[1623] Step 6:

[1624] The server distributes the generated video to the user.

[1625] Input: The video generated in step 5 and the user-specified broadcast time.

[1626] Specific operation: The server sends notifications and distributes video according to the specified time period (e.g., morning, afternoon, evening).

[1627] Data Calculation: Notification generation based on delivery schedule.

[1628] Output: The video notification and video content delivered to the user.

[1629] (Application example 2)

[1630] 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."

[1631] In today's information society, users have access to a large amount of news information, but it is difficult to efficiently understand all of the information and extract only the content that interests them. Furthermore, because the amount of news content is so vast, users often waste time on information that is not important to them. Furthermore, because how users perceive news is greatly influenced by their emotional state, there is a need to provide content that is tailored to each individual. The present invention aims to provide a system that enables efficient and appropriate summarization and delivery of news information based on user behavioral data and emotional information.

[1632] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user information collection means, an analysis means for analyzing user behavior information and emotion information collected by the user information collection means, a selection means for selecting news articles based on the user interest information and emotion information analyzed by the analysis means, a summarization means for summarizing the news articles selected by the selection means using a generative AI model, an image generation means for visualizing the news articles summarized by the summarization means, and a distribution means for distributing the images generated by the image generation means at a time specified by the user. This enables users to efficiently obtain news that is relevant to their areas of interest and leaves them with positive emotions in a minimum amount of time.

[1633] The "user information collection means" is a means for collecting a user's browsing history, click events, browsing time, and emotion information.

[1634] The "analysis means" is a means for analyzing the user behavior information and emotion information collected by the user information collection means, and identifying the user's interests and emotions.

[1635] The "selection means" is a means for selecting a news article based on the user interest information and emotion information analyzed by the analysis means.

[1636] The "summarization means" is a means for summarizing the news articles selected by the selection means using a generative AI model.

[1637] The "image generating means" is a means for visualizing the news article summarized by the summarizing means.

[1638] The "distribution means" is a means for distributing the video generated by the video generation means at a time designated by the user.

[1639] A "generative AI model" is an artificial intelligence model used to generate summaries of news articles.

[1640] "Emotion information" is information that indicates the emotional state of the user, analyzed from their facial expressions and tone of voice.

[1641] This invention is a system that summarizes news based on user behavioral data and emotional information, visualizes it, and distributes it. This system allows users to quickly and efficiently obtain the news information they need, and also provides content tailored to the user's emotions.

[1642] Overall system configuration

[1643] User information collection method

[1644] The server uses a user information collection means to collect user behavior data and emotional information. This means uses the URLs of articles viewed by the user, the viewing time, the links clicked, and scrolling information, as well as sensors such as a camera and microphone to recognize the user's facial expressions and tone of voice, and then analyzes the emotional information using an emotion engine and stores it in a database.

[1645] Analysis means

[1646] The server analyzes the collected behavioral data and emotional information using analytical means. Here, machine learning models and an emotional analysis engine are used to identify which topics the user is interested in and their emotional state. For example, if the user frequently views technology-related articles and expresses positive emotions while viewing them, it is determined that the user has a strong interest in technology and has positive emotions about it.

[1647] Selection method

[1648] The server selects relevant latest news articles from the news database based on the analysis results. Based on the user's topics of interest and emotional information identified from the analysis results, the server filters out the most relevant articles to the user. For example, it selects technology-related articles that users express positive emotions about, such as "announcements of new AI technologies" and "reviews of the latest smart devices."

[1649] Summary tools

[1650] The server uses a generative AI model to summarize the news articles selected by the selection method. By inputting prompt sentences into the generative AI model, it generates a short summary that can convey the original article content in less than one minute. Furthermore, it reflects the user's emotional information obtained through an emotion engine and adjusts the tone and key points of the article. For example, for a user with a positive emotion, the summary will emphasize success stories and positive news.

[1651] Image generation means

[1652] The server then uses a video generator to visualize the summarized news articles. This generator integrates text, related images, and audio narration to create videos with a tone and expression that matches the user's emotions. For example, positive news stories are accompanied by upbeat music and lively narration.

[1653] Delivery Method

[1654] The server distributes the generated video to the user via a distribution means. The video is distributed while sending a notification according to the time period specified by the user (e.g., morning, afternoon, or evening). The user can view the video in response to the notification.

[1655] Specific examples of hardware and software used

[1656] Hardware:

[1657] Smartphone: Uses camera and microphone to capture user facial expressions and tone of voice.

[1658] Server: Analyzes and processes data.

[1659] software:

[1660] GPT-3 (generative AI model): Generates summaries of news articles.

[1661] Sentiment analysis engine: Analyzes user emotions.

[1662] Text-to-Video Software: Convert text to video.

[1663] Specific examples

[1664] For example, if user A frequently reads technology-related articles on a news site and has a positive emotional reaction to them, the system will operate as follows:

[1665] Collection phase:

[1666] The server collects user A's behavioral data (article browsing history, click events, browsing time, etc.) and emotional information (facial expressions, tone of voice, etc.).

[1667] For example, if User A views an article on "Latest Smartphone Technology" and the camera and microphone detect a smile and an interesting tone of voice, that information will be stored in the database.

[1668] Analysis phase:

[1669] The server analyzes the collected data and determines that User A has a strong interest in technology and has positive feelings about the news. The analysis is performed using a machine learning model.

[1670] Selection phase:

[1671] The server selects the latest technology-related news articles from a news database.

[1672] For example, it narrows down articles to match users' positive emotions, such as "announcements of new AI technologies" or "reviews of the latest smart devices."

[1673] Summary phase:

[1674] The server summarizes the selected articles using a generative AI model.

[1675] For example, "Announcement of new AI technology" can be summarized in a positive tone as "The latest AI technology has been announced. As a result..."

[1676] Image Generation Phase:

[1677] The server then creates a video of the summary article, integrating the text, related images, and audio narration, using a light tone and expressions that match the user's positive emotions.

[1678] Delivery phase:

[1679] If User A selects "Morning News," the server notifies the user to view the video generated at 7 a.m. After receiving the notification, the user can view the video.

[1680] Prompt Sentence Examples

[1681] Summarize news articles and adjust tone depending on emotional information.

[1682] User Sentiment: Positive

[1683] Article content: The latest AI technology has been announced. It is expected to revolutionize various industries, with applications in the medical and financial fields attracting particular attention.

[1684] Abstract: The latest AI technology has been unveiled, promising revolutionary changes in the fields of medicine and finance.

[1685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1686] Step 1:

[1687] The server uses user information collection means to collect user behavioral data and emotional information. It obtains the URL of the article the user is viewing, the viewing time, click events, and scrolling information, while also recording the user's facial expressions and tone of voice in real time via a camera and microphone. This data is sent to the server and stored in a database.

[1688] Input: User browsing history, click events, browsing time, facial expressions, tone of voice

[1689] Output: Behavioral data and emotional information stored in a database

[1690] Step 2:

[1691] The server analyzes the collected behavioral data and emotional information using analytical means, using machine learning models and an emotional analysis engine to identify which topics users are interested in and their emotional state at the time.

[1692] Input: Behavioral data and emotional information

[1693] Output: User's interest topics and emotional state

[1694] Step 3:

[1695] The server selects relevant latest news articles from the news database based on the analysis results, filters news articles corresponding to the topics and emotional information that the user has expressed interest in, and selects the most relevant articles.

[1696] Input: User's interest topics and emotional state

[1697] Output: A list of selected news articles

[1698] Step 4:

[1699] The server summarizes the selected news articles using a generative AI model (e.g., GPT-3). The user inputs a prompt into the generative AI model, which generates a summary that conveys the article's content in a short amount of time. The generated summary reflects the user's emotional information and adjusts the article's tone and key points.

[1700] Input: Selected news article, prompt

[1701] Output: A summarized news article

[1702] Step 5:

[1703] The server then uses the video generation means to visualize the news articles summarized by the summarization means. The server integrates the text, related images, and audio narration to create a video using a tone and expression that matches the user's emotions. For example, positive news can be accompanied by upbeat music and cheerful narration.

[1704] Input: A summarized news article, associated images, and audio files

[1705] Output: Finished video

[1706] Step 6:

[1707] The server distributes the generated video to the user using a distribution method. It also sends a notification to the user at a specific time period (e.g., morning, afternoon, or evening) designated by the user, informing them that the video is available for viewing. The user receives the notification and can begin viewing the video.

[1708] Input: Finished video, user-specified duration

[1709] Output: Notification to the user and video distribution

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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).

[1717] 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.

[1718] 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."

[1719] 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.

[1720] 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).

[1721] 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.

[1722] 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.

[1723] 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.

[1724] 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.

[1725] 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.

[1726] 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.

[1727] 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.

[1728] 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.

[1729] 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.

[1730] 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.

[1731] The following is further disclosed regarding the above embodiment.

[1732] (Claim 1)

[1733] A user information collection means;

[1734] analysis means for analyzing the user behavior information collected by the user information collection means;

[1735] a selection means for selecting a news article based on the user interest information analyzed by the analysis means;

[1736] a summarizing means for summarizing the news articles selected by the selecting means;

[1737] an image generating means for visualizing the news article summarized by the summarizing means;

[1738] a distribution means for distributing the video generated by the video generation means at a specified time;

[1739] A system including:

[1740] (Claim 2)

[1741] The system of claim 1, wherein the user information collection means is a means for collecting behavioral data such as user browsing history, click events, and browsing time, and the analysis means is a means for identifying user interests using a machine learning model.

[1742] (Claim 3)

[1743] 2. The system according to claim 1, wherein said distribution means distributes the video according to a specific time period (morning, afternoon, or evening) selected by the user.

[1744] "Example 1"

[1745] (Claim 1)

[1746] A user information collection means;

[1747] analysis means for analyzing the user behavior information collected by the user information collection means;

[1748] a selection means for selecting a news article based on the user interest information analyzed by the analysis means;

[1749] a summarizing means for summarizing the news articles selected by the selecting means;

[1750] an image generating means for visualizing the news article summarized by the summarizing means;

[1751] a distribution means for distributing the video generated by the video generation means at a specified time;

[1752] A system including:

[1753] (Claim 2)

[1754] The system of claim 1, wherein the user information collection means is a means for collecting behavioral data such as user browsing history, click events, browsing time, and scrolling speed, and the analysis means is a means for identifying user interests using a machine learning model.

[1755] (Claim 3)

[1756] 2. The system of claim 1, wherein the summarizing means uses a generative AI model to summarize news articles into short sentences.

[1757] (Claim 4)

[1758] 2. The system according to claim 1, wherein said distribution means distributes the video according to a specific time period (morning, afternoon, or evening) selected by the user.

[1759] "Application Example 1"

[1760] (Claim 1)

[1761] A user information collection means;

[1762] analysis means for analyzing the user behavior information collected by the user information collection means;

[1763] a selection means for selecting a news article based on the user interest information analyzed by the analysis means;

[1764] a summarizing means for summarizing the news articles selected by the selecting means;

[1765] an image generating means for visualizing the news article summarized by the summarizing means;

[1766] a distribution means for distributing the video generated by the video generation means at a specified time;

[1767] a display means for displaying the video distributed by the distribution means on a user's terminal;

[1768] A system including:

[1769] (Claim 2)

[1770] 2. The system of claim 1, wherein the user information collection means is a means for collecting behavioral data such as user browsing history, click events, and browsing time, the analysis means is a means for identifying user interests using a machine learning model, and the summarization means is a means for summarizing news articles using a generative AI model (e.g., a generative AI model).

[1771] (Claim 3)

[1772] The system of claim 1, wherein the distribution means distributes the video according to a specific time period (morning, noon, or evening) selected by the user, and the display means displays the video on the user's smartphone, smart glasses, head-mounted display, etc.

[1773] "Example 2: Combining Emotion Engines"

[1774] (Claim 1)

[1775] A user information collection means;

[1776] analysis means for analyzing the user behavior information and emotion information collected by the user information collection means;

[1777] a selection means for selecting news articles based on the user interest information and emotional state analyzed by the analysis means;

[1778] a summarizing means for summarizing the news articles selected by the selecting means using a generative AI model;

[1779] an image generating means for visualizing the news article summarized by the summarizing means;

[1780] a distribution means for distributing the video generated by the video generation means at a specified time;

[1781] A system including:

[1782] (Claim 2)

[1783] The system of claim 1, wherein the user information collecting means is a means for collecting user browsing history, click events, browsing time, and emotional information such as facial expressions and tone of voice, and the analyzing means is a means for identifying the user's interests and emotional state using a machine learning model.

[1784] (Claim 3)

[1785] 2. The system according to claim 1, wherein said distribution means distributes the video according to a specific time period (morning, afternoon, or evening) selected by the user.

[1786] "Application example 2 when combining emotion engines"

[1787] (Claim 1)

[1788] A user information collection means;

[1789] analysis means for analyzing the user behavior information and emotion information collected by the user information collection means;

[1790] a selection means for selecting news articles based on the user interest information and emotion information analyzed by the analysis means;

[1791] A summarizing means for summarizing the news articles selected by the selecting means using a generative AI model;

[1792] an image generating means for visualizing the news article summarized by the summarizing means;

[1793] a distribution means for distributing the video generated by the video generation means at a time designated by a user;

[1794] A system including:

[1795] (Claim 2)

[1796] 2. The system of claim 1, wherein the user information collecting means is means for collecting user browsing history, click events, browsing time, and emotion information, and the analyzing means is means for identifying user interests and emotions using a machine learning model and an emotion analysis engine.

[1797] (Claim 3)

[1798] 2. The system according to claim 1, wherein said distribution means distributes the video according to a specific time period (morning, afternoon, or evening) selected by the user. [Explanation of symbols]

[1799] 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 user information collection means; analysis means for analyzing the user behavior information collected by the user information collection means; a selection means for selecting a news article based on the user interest information analyzed by the analysis means; a summarizing means for summarizing the news articles selected by the selecting means; an image generating means for visualizing the news article summarized by the summarizing means; a distribution means for distributing the video generated by the video generation means at a specified time; A system including:

2. The system of claim 1, wherein the user information collection means is a means for collecting behavioral data such as user browsing history, click events, and browsing time, and the analysis means is a means for identifying user interests using a machine learning model.

3. 2. The system according to claim 1, wherein said distribution means distributes the video according to a specific time period selected by the user.

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