System

Abstract

A system is provided.SOLUTION: A system comprising: means for automatically obtaining news data; means for summarizing the obtained news data; means for generating relevant illustrations and captions based on the summarized news data; and means for delivering the generated summarized news data, illustrations and captions to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, many working adults and elementary and junior high school students find it difficult to find the time to understand the news. Furthermore, news content can be difficult to understand, especially news that requires specialized knowledge. Furthermore, it can be difficult for parents to explain the news to their children in an easy-to-understand manner. To solve these problems, there is a need for a system that summarizes the news and provides it in a format that anyone can easily understand. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for automatically acquiring news data, a means for summarizing the acquired news data, a means for generating related illustrations and explanatory text based on the summarized news data, and a means for delivering the generated summarized news data, illustrations, and explanatory text to users. Furthermore, the present invention also provides a configuration that includes a means for generating videos from the summarized news data and a means for delivering the generated videos to users, and a configuration that includes a means for summarizing the news data using a generative AI model, generating explanatory text, and a means for converting the generated explanatory text into audio. As a result, even busy working adults and elementary and junior high school students can easily understand the news.

[0006] "News data" is a collection of information obtained from news broadcasters and media organizations.

[0007] "Summarizing" means reducing the content of the original news data into a concise and easy-to-understand form.

[0008] "Illustrations" are images or pictures that make the news content easier to understand visually.

[0009] An "explanatory text" is a simple, short piece of text written to supplement the news content.

[0010] "Distributing" refers to the act of delivering the generated news content to users.

[0011] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to summarize news data and generate explanatory text.

[0012] "Converting to audio" means converting textual news descriptions into audio data.

[0013] "Acquiring" means obtaining news data from external news broadcasters or media organizations. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

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

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention automatically acquires news data, summarizes it, generates related illustrations and brief descriptions, and delivers them to users.

[0036] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0037] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0038] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0039] The server then prepares the integrated news content for delivery to the user. First, it converts the summary news, illustrations, and explanatory text into a format suitable for the application and prepares it for delivery to the device. If video is also to be generated, it converts the summary text into audio and creates a video combined with the illustrations.

[0040] The server then sends a push notification to the device to notify it of new news. The user's device receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news in a visually easy-to-understand format for the user. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0041] For example, if a news item about "important policy decisions on environmental protection made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the Earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." The user can check this on their device and quickly understand the news content.

[0042] The present invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this mode, users can easily grasp important news in their daily lives.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0046] Step 2:

[0047] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0048] Step 3:

[0049] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0050] Step 4:

[0051] The server inputs the preprocessed news data into a generative AI model, which is instructed to concisely summarize the original news data.

[0052] Step 5:

[0053] The generative AI model generates a summary, and the server stores the generated summary in a database for further processing.

[0054] Step 6:

[0055] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0056] Step 7:

[0057] The server uses the generative AI model to generate a brief description of the selected illustration, such as "This shows efforts to prevent global warming."

[0058] Step 8:

[0059] The server integrates the summary news, illustrations, and explanatory text into a single piece of content and stores this integrated data in a database.

[0060] Step 9:

[0061] The server converts the aggregated news content into a format suitable for the application. If video is also generated, the server converts the summary text into audio and creates a video that combines the audio with illustrations.

[0062] Step 10:

[0063] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0064] Step 11:

[0065] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves the news data from the server and displays it to the user.

[0066] Step 12:

[0067] When a user watches a generated video, they play it on their device and understand the news visually and aurally. The video includes a narrator's voice and related illustrations.

[0068] As described above, by having the server, terminals, and users work together to automatically acquire, summarize, and distribute news, a system is realized that allows even busy working people and elementary and junior high school students to easily understand the news.

[0069] Example 1

[0070] 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."

[0071] In modern society, it is difficult to efficiently obtain and understand the necessary news from the vast amount of information available. Busy working people and those who need to obtain information quickly are particularly required to accurately grasp the news in a short amount of time. There is also a demand for news content to be provided in a visually easy-to-understand format. However, existing news distribution systems do not adequately provide systems that can automatically generate summarized news text and distribute it together with related explanatory text and illustrations.

[0072] 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.

[0073] In this invention, the server includes means for automatically acquiring news data, means for using a generative AI model to summarize the acquired news data, means for generating related images and descriptions based on the summarized news data, means for delivering the generated summarized news data, images, and descriptions to users, means for saving the acquired news data in a database, means for saving the summarized news data again in the database, means for analyzing keywords in the summary data and selecting images based thereon, means for generating descriptions using a generative AI model, means for saving illustration information and the generated descriptions in the database, means for converting the summarized news, images, and descriptions into a format suitable for an application, means for preparing the summarized news, images, and descriptions for delivery to a terminal, and means for sending push notifications. This enables efficient and rapid execution of a series of processes, from acquiring news data to summarizing it, generating related images and descriptions, and automatically delivering them to users.

[0074] "News Data" means current news reports and related information obtained from news stations and other information providers.

[0075] "Means of acquisition" refers to the function by which the server automatically requests and receives news data using an API.

[0076] A "generative AI model" is an artificial intelligence technology that can generate summaries and explanations from given data, and is, for example, a type of natural language processing model.

[0077] "Means of summarization" refers to the function of summarizing acquired news data in a concise form using a generative AI model.

[0078] "Related images" are visual materials such as illustrations and photographs selected based on summarized news data to complement the news content.

[0079] A "description" is a concise piece of text generated based on relevant image and news data using a generative AI model.

[0080] "Means of distribution" refers to the function of sending the generated news data, images, and explanatory text to the user's terminal and displaying them.

[0081] A "database" is an information system for storing and managing news data, generated summaries, images, descriptions, etc.

[0082] "Keyword analysis means" refers to the function of extracting key terms from summaries and news data and selecting related images based on them.

[0083] The "format conversion means" refers to a function that converts the generated news data, images, and descriptions into a format that can be displayed on a user terminal.

[0084] The "means for sending push notifications" refers to a communication means by which the server notifies the user's terminal of the distribution of new news.

[0085] The system of the present invention automatically acquires news data, summarizes it, generates related images and descriptions, and delivers them to users. To implement this system, the following elements work together: a server, a terminal, and a user.

[0086] The system of the present invention uses the following hardware and software: The server is a computer with high processing power and storage capacity, and performs processes for data acquisition, analysis, generation, storage, and distribution. Specifically, the server has the function of automatically acquiring news data periodically through an API. This is done using an HTTP request, which is achieved by sending a request including authentication information and necessary parameters. This request receives the latest news data from affiliated news broadcasters and stores it in a database.

[0087] The server is responsible for summarizing the acquired news data using a generative AI model. The text of the news data is input into the generative AI model (e.g., OpenAI's GPT-4) to generate a concise summary. An example of the prompt used in this case is: "Please provide a brief summary of the following news article: [news article text]." The generated summary is stored in a database.

[0088] The server then generates relevant images and descriptions based on the summarized news data. Keywords in the summary data are analyzed and images are selected based on them. For example, if a news summary includes "important policy decisions on environmental protection at an international conference," images such as "a picture of a factory emitting smoke" or "an image showing the Earth" are selected. A generative AI model is used to create a brief description of the image. An example prompt is: "This illustration shows [keyword]. Please generate a brief description: [description of the illustration]." The generated images and descriptions are also stored in a database.

[0089] The server then converts the generated summary news, images, and descriptions into a format suitable for the application and prepares them for delivery to the device. In this case, the generative AI model also supports text-to-speech conversion of the summary. The generated data is then converted into a format suitable for the user's device.

[0090] The server then sends a push notification to the device, informing the user that there is new news. The user's device receives the notification and opens the application to view the latest news summary. The device retrieves the data from the server and displays it to the user in a visually appealing format. If a video has been generated, the user can watch it within the application.

[0091] This system allows users to instantly grasp important news even in the midst of their busy daily lives, streamlining the process of acquiring and understanding information and realizing highly optimized news delivery to users.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] Every morning, the server sends an HTTP request to the API of a partner news station to automatically retrieve the latest news data.

[0095] Specific behavior:

[0096] Set the API key in the HTTP request header.

[0097] Include date information in the request body.

[0098] Send a request to a URL like "https: / / newsapi.example.com / latest?date=YYYY-MM-DD".

[0099] Input: API key, date information

[0100] Output: News data in JSON format

[0101] Step 2:

[0102] The server stores the acquired news data in a database.

[0103] Specific behavior:

[0104] Parse (analyze) the received JSON format data.

[0105] The parsed data is separated into each field and inserted into the corresponding table in the database.

[0106] Input: News data in JSON format

[0107] Output: News data stored in the news table of the database

[0108] Step 3:

[0109] The server uses a generative AI model to convert news data into concise summaries.

[0110] Specific behavior:

[0111] The news text is fed into a generative AI model.

[0112] Use the prompt: "Please briefly summarize the following news article: [news article text]."

[0113] Get the summary generated from the model response.

[0114] Input: News data text, prompt

[0115] Output: A summary generated by the generative AI model

[0116] Step 4:

[0117] The server stores the summarized news data in a database.

[0118] Specific behavior:

[0119] The generated summary sentence is inserted into the "Summary News" table of the database.

[0120] Input: Summarized news data

[0121] Output: Summary data stored in the Summary News table in the database

[0122] Step 5:

[0123] The server analyzes the keywords in the summary data and selects relevant images based on them.

[0124] Specific behavior:

[0125] Extract key nouns and verbs from the summary.

[0126] Based on the extracted keywords, appropriate illustrations are searched for in the image database.

[0127] Input: Abstract data, Keyword extraction algorithm

[0128] Output: The path to the selected image.

[0129] Step 6:

[0130] The server uses a generative AI model to generate a description of the image.

[0131] Specific behavior:

[0132] Keywords and image information are input into the generative AI model.

[0133] Use the prompt: "This illustration shows [keyword]. Please generate a brief description: [illustration description]."

[0134] Get the explanation generated in the response from the model.

[0135] Input: Keywords, image information, prompt text

[0136] Output: Generated description

[0137] Step 7:

[0138] The server stores the generated images and descriptions in a database.

[0139] Specific behavior:

[0140] The path of the illustration and the generated description are inserted into the "Illustration Description" table of the database.

[0141] Input: Image path, generated description

[0142] Output: Data stored in the illustration description table in the database

[0143] Step 8:

[0144] The server formats the news summaries, images and descriptions for the application.

[0145] Specific behavior:

[0146] Each data is compiled in JSON format.

[0147] If necessary, convert the summary text into audio (Text-to-Speech) and create a video combining it with illustrations (using a video generation API).

[0148] Input: News summary, image, description

[0149] Output: Data converted into a format that can be delivered to the user's device

[0150] Step 9:

[0151] The server sends a push notification to the device to notify it that there is new news.

[0152] Specific behavior:

[0153] Send notifications using a push notification service (e.g., Firebase Cloud Messaging).

[0154] The notification will include information about new news.

[0155] Input: Latest news

[0156] Output: Push notification sent to device

[0157] Step 10:

[0158] The device receives a notification and opens the application to view the latest news summary.

[0159] Specific behavior:

[0160] Tapping the notification will launch the application.

[0161] Within the app, a request is sent to the server to retrieve the latest news summary, images, and descriptions.

[0162] Input: Push notification, server request

[0163] Output: Retrieved latest news data, images, and descriptions

[0164] Step 11:

[0165] The terminal displays news to the user in a visually easy-to-understand format.

[0166] Specific behavior:

[0167] Set the retrieved data in the appropriate UI component.

[0168] The news text, summary, images and description are displayed on the screen.

[0169] If a video has been generated, it is played using a video player.

[0170] Input: Latest news data, images, descriptions

[0171] Output: News displayed to the user, and video playback

[0172] (Application example 1)

[0173] 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."

[0174] In today's busy society, it is important to be able to quickly and easily grasp the latest news. However, traditional news delivery methods often contain too much information and take too much time, making it difficult for users to easily understand the latest news. Another problem is that there is little visual content, making it difficult to intuitively understand the news content. Furthermore, the push notification function, which is used to quickly deliver new news, is not optimized, which can lead to users missing important information.

[0175] 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.

[0176] In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for notifying users of the existence of new news using push notifications, and means for visually displaying the summarized news, illustrations, and explanatory text on the user terminal, thereby enabling a news distribution system that allows users to grasp the main points in a short amount of time.

[0177] "News Data" is the latest information obtained from affiliated news stations.

[0178] "Automatic acquisition means" refers to a mechanism by which the server automatically acquires news data using the news broadcaster's API.

[0179] "Means for summarizing" is a function that extracts important information from acquired news data and summarizes it concisely.

[0180] The "means for generation" is a function that automatically generates related visual content and text from summarized news data.

[0181] "Illustrations" are visual images or pictures associated with the news summary.

[0182] "Description" is text that briefly explains the content of the illustration or the main points of the news.

[0183] The "distribution means" is a mechanism for transmitting the generated summary news data, illustrations, and explanatory text to the user.

[0184] "Means of notifying using push notifications" is a system that instantly notifies users of new news on their devices.

[0185] "Means for displaying on a user's device" refers to a function for visually displaying news content on a user's device such as a smartphone or tablet.

[0186] "Means for generating video" refers to a mechanism for creating video content from summary news data.

[0187] The "means for converting into audio" is a mechanism for converting the generated explanatory text into audio data.

[0188] A "generative AI model" is a model that uses artificial intelligence to summarize news data and generate related content.

[0189] In this embodiment, we will build a system that automatically acquires news data, summarizes it, generates related illustrations and explanatory text, and delivers it to users. This system mainly requires the combination of various components, such as a server, user terminal, generation AI model, news acquisition API, database, and push notification service.

[0190] First, the server automatically collects the latest news data from affiliated news stations every morning using a news retrieval API. To do this, the server sends an HTTP request containing authentication information and necessary parameters. The retrieved news data is then stored in a database (e.g., MySQL).

[0191] Next, the server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate a concise summary from the stored news data. The text of the news data is provided as input to the generative AI model, which generates a concise summary. The generated summary data is then stored back in the database.

[0192] The server then generates related illustrations and descriptions based on the summarized news data. It analyzes keywords contained in the summary data and selects pre-prepared illustrations based on them. It then uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are also stored in a database.

[0193] The server then prepares this consolidated news content for delivery to users, notifying them of new news using a push notification service (e.g., Firebase Cloud Messaging). The server then converts the news summary, illustrations, and descriptions into a format suitable for the application, converting the descriptions into audio data using FFMpeg for playback, and, if necessary, generating a video that combines the audio data with the illustrations.

[0194] When a user receives a notification on their device (e.g., an iOS or Android smartphone), they open the app to view the latest news summary. The app retrieves data from the server and displays the news summary, illustrations, and explanatory text in a visually appealing format. If a video has been generated, the user can watch it within the app.

[0195] Examples and prompts

[0196] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the following prompt sentence is given to the generative AI model:

[0197] News: An international conference has decided on a plan for countries to work together to reduce greenhouse gas emissions.

[0198] Generate a summary in five sentences or less.

[0199] Next, the prompt for generating a description after selecting an illustration is given as follows:

[0200] Illustration: Earth and factory smoke

[0201] Description:

[0202] The expected output is, for example, "This shows efforts to prevent global warming."

[0203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0204] Step 1:

[0205] The server automatically retrieves news data every morning using the news station's API. At this time, the server sends an HTTP request including authentication information and parameters. The retrieved news data is returned to the server in JSON format or other formats. The server stores the returned news data in a MySQL database.

[0206] Input: News data (JSON format) returned from the news broadcaster's API.

[0207] Output: News data stored in a MySQL database.

[0208] Step 2:

[0209] The server uses a generative AI model (GPT-4) to generate summaries of news data retrieved from a MySQL database. The server inputs the text of the news data into the generative AI model and instructs it to generate a concise summary. The generated summary is then stored back in the MySQL database.

[0210] Input: Text news data stored in a MySQL database.

[0211] Output: Summary text stored in a MySQL database.

[0212] Step 3:

[0213] The server generates relevant illustrations and descriptions from the summary data. First, the server analyzes keywords contained in the summary data and selects appropriate illustrations from pre-prepared illustrations based on the analysis. Next, it generates descriptions for the selected illustrations using a generative AI model. The generated illustrations and descriptions are stored in a database.

[0214] Input: Summarized news data.

[0215] Output: Illustrations and descriptions stored in a MySQL database.

[0216] Step 4:

[0217] The server then converts the generated news summary, illustrations, and descriptions into a new format and reconstructs them for the application. It uses FFMpeg to convert the descriptions into audio data and then combines them with the illustrations to generate a video. All content, including the video, is then stored in a database.

[0218] Input: News summary, selected illustrations, generated description.

[0219] Output: Data, audio data, and video data formatted for the application.

[0220] Step 5:

[0221] The server uses a push notification service (Firebase Cloud Messaging) to notify the user that there is new news. When the user receives the notification, they open the application and check the latest news summary.

[0222] Enter: a notification that there's new news.

[0223] Output: Push notification to user device.

[0224] Step 6:

[0225] The user device retrieves the data from the server and displays it in a visually easy-to-understand format. Within the application, the user can view news summaries, illustrations, and explanatory text, and can also watch videos if they are generated.

[0226] Input: News summary, illustrations, descriptions, and video data from the server.

[0227] Output: News content and videos displayed on the user's device.

[0228] This detailed processing flow allows users to quickly and concisely grasp important news and receive information in a visually easy-to-understand format.

[0229] 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.

[0230] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0231] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0232] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0233] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0234] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from cameras, microphones, etc. For example, if the user is stressed, the server adjusts the tone of the news content to be calmer.

[0235] The server then formats the consolidated news content for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The emotion engine also adjusts the content and tone of the video. For example, if the user needs inspiring news, the audio tone of the video will be set to an uplifting one.

[0236] The server then sends a push notification to the device to notify it of new news. The user's device then receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news to the user in a visually easy-to-understand format. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0237] For example, if a news item about "an important policy decision on environmental protection was made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy decision was made at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." If the emotion engine recognizes that the user is in a relaxed state, the server presents this content in a simple, calm tone. The user can check this on their device and quickly understand the news content.

[0238] This invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this form, users can easily grasp important news in their daily lives and enjoy a personalized experience that responds to their emotions.

[0239] The processing flow will be explained below.

[0240] Step 1:

[0241] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0242] Step 2:

[0243] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0244] Step 3:

[0245] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0246] Step 4:

[0247] The server inputs the preprocessed news data into a generative AI model, which is given instructions to concisely summarize the original news data. The generative AI model then generates the summarized news data.

[0248] Step 5:

[0249] The server stores the summarized news data in a database, which is used for further processing.

[0250] Step 6:

[0251] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0252] Step 7:

[0253] The server uses the generative AI model to generate a simple description of the selected illustration. For example, it generates a description such as, "This shows efforts to prevent global warming." The generated illustration and description are stored in a database.

[0254] Step 8:

[0255] The user's device recognizes the user's emotional state using sensor data from cameras, microphones, etc. The device then uses an emotion engine to analyze the user's emotions.

[0256] Step 9:

[0257] The device then sends the user's emotional data to the server, which then adjusts the content and tone of the news content based on the emotional data.

[0258] Step 10:

[0259] The server integrates the summary news, illustrations, and descriptions into a single piece of content, and stores this integrated data in a database. The news content is then adjusted based on the emotion engine.

[0260] Step 11:

[0261] The server converts the aggregated news content into a format suitable for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The content and tone of the video are also adjusted by the emotion engine.

[0262] Step 12:

[0263] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0264] Step 13:

[0265] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves news data from the server and displays the news in a visually easy-to-understand format for the user.

[0266] Step 14:

[0267] When users watch the generated video, they play it on their device and use their eyes and ears to understand the news. The video includes a narrator voice and relevant illustrations, which are tailored based on emotion.

[0268] As described above, by linking the server, terminals, and users to automatically acquire, summarize, and distribute news, a system has been realized that allows even busy working people and elementary and junior high school students to easily understand the news. In addition, by incorporating an emotion engine, a personalized news experience is provided according to the user's emotional state.

[0269] Example 2

[0270] 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."

[0271] Conventional news delivery systems make it difficult for users to efficiently obtain vast amounts of information, and they lack the ability to provide personalized information tailored to their emotional state. Furthermore, people who do not have time to read long news articles need a way to quickly grasp important information. Furthermore, technologies for generating content that combines visual and emotional elements are insufficient.

[0272] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data using a generative AI model, means for analyzing keywords in the summarized news data and selecting related illustrations, means for generating explanatory text for the selected illustrations using the generative AI model, a user terminal including an emotion engine that analyzes the user's emotional state, means for adjusting the tone of the news content depending on the emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This allows the user to quickly grasp the news information they need and enjoy a personalized news experience tailored to their emotional state.

[0273] "News data" is a collection of current news information obtained from news broadcasters and other information providers.

[0274] A "generative AI model" is a type of artificial intelligence algorithm that analyzes news data and automatically generates summaries and descriptions.

[0275] A "summary" is a concise text that extracts key information from news data.

[0276] "Keywords" are important words or phrases extracted from news data or summary data.

[0277] "Illustrations" are images or diagrams used to visually represent news content.

[0278] "Description" is text that provides additional information about the selected illustration and explains its meaning or content.

[0279] An "emotion engine" is a combination of software and hardware used to analyze a user's emotional state.

[0280] "User terminal" means a device used by a user to access the emotion engine in real time and view news content.

[0281] "Tone" refers to the style and mood of the news content, which is adjusted according to the user's emotional state.

[0282] "Format conversion" is a process of converting news data and generated content into a format that can be displayed appropriately on the user's terminal.

[0283] "Speech synthesis" is the technology of converting generated text into speech.

[0284] "Push notification" is a function that notifies the user's device of new news in real time from the server.

[0285] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0286] Every morning, the server automatically retrieves the latest news data from partner news stations. This process involves sending an HTTP request using an API, including authentication information and required parameters. For example, a scheduled task can be set up to issue an HTTP request at a specific time, access the news station's API endpoint, and store the retrieved data in JSON format in the server's database.

[0287] Next, the server uses a generative AI model to summarize the acquired news data. It extracts the text field of the news data and sends the generative AI model a prompt: "Summarize the latest news in 100 characters or less." The generative AI model generates a summary based on this prompt and stores the result back in the database. As a specific example, news data such as "An important announcement regarding greenhouse gas reduction was made at an international conference" is converted into a summary such as "An important greenhouse gas reduction policy was announced at an international conference."

[0288] The server then generates relevant illustrations and descriptions from the summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. For example, if the summary contains the keyword "greenhouse gas," it selects an image depicting factory smoke. Next, it uses a generative AI model to send a prompt asking, "Please generate a description for this illustration," and the description is automatically generated. The generated description takes the form, for example, "This shows efforts to prevent global warming."

[0289] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from the camera, microphone, etc. For example, if the server detects that the user is relaxed, it adjusts the tone of the news to be presented calmly. When a speech synthesis API is used to convert the summary text into speech and generate a video that combines the speech with illustrations, the background music and speech tempo are also adjusted based on the user's emotional state.

[0290] The server finally converts the aggregated news content into a format suitable for the app and sends a push notification to the user's device to notify them of new news. When the user receives the notification, they can open the app to view the latest news summary. The generated video can also be viewed within the app.

[0291] As a concrete example, when news about "important policy decisions on environmental protection at an international conference" is acquired, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects a related illustration and adds a caption that reads, "This shows efforts to prevent global warming." If the emotion engine determines that the user is relaxed, the server presents the news in a calm tone. The user can view this on their device and quickly understand the news content.

[0292] The following prompts are used as examples of input to the generative AI model:

[0293] Generate a summary of the news data:

[0294] "Summarize the latest news in 100 characters or less"

[0295] And an example prompt to generate the description:

[0296] Please generate a description for this illustration:

[0297] "Efforts to prevent global warming"

[0298] This system allows users to efficiently grasp the main points of news and enjoy a personalized news experience that responds to their emotions.

[0299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0300] Step 1: Obtaining news data

[0301] Every morning, the server retrieves the latest news data from a partner news station. The server sends an HTTP request using the API at a fixed time, including authentication information and required parameters. Specifically, the server sets up a scheduled task (e.g., a cron job) to access the news station's API endpoint. The input is the news station's API address and authentication information, and the output is the retrieved news data (in JSON format). Once the news data is returned, the server stores it in a database.

[0302] Step 2: Summarizing the news data

[0303] The news data acquired by the server is input into the generative AI model, which generates a summary. Specifically, the text fields of the news data are extracted and sent to the generative AI model as a prompt. The input is the text portion of the news data, and a prompt is generated: "Please summarize the latest news in 100 characters or less." Based on this prompt, the generative AI model generates a summary. The output is the generated summary, which is saved in a database. For example, the news data "An important announcement regarding greenhouse gas reduction was made at an international conference" becomes a summary: "An important greenhouse gas reduction policy was announced at an international conference."

[0304] Step 3: Generate illustrations and descriptions

[0305] The server generates relevant illustrations and descriptions based on summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. The input is the analyzed keywords. For example, if the summary contains the keyword "greenhouse gas," it obtains an image depicting factory smoke. Next, it uses a generative AI model to send a prompt saying, "Please generate a description for this illustration." The generative AI model generates and outputs the description. The description generated as output will be in the format, "This shows efforts to prevent global warming." The generated illustrations and descriptions are stored in a database.

[0306] Step 4: Analyze emotional state

[0307] An emotion engine built into the user's device analyzes the user's emotional state using sensor data from cameras, microphones, etc. The input is real-time sensor data obtained from cameras and microphones. The emotion engine analyzes this sensor data and determines the user's emotional state, such as whether they are stressed or relaxed. The output is information about the user's emotional state, such as stress or relaxation. This information is sent to a server and used to adjust the tone of the news content.

[0308] Step 5: Adjust the tone of your content

[0309] The server adjusts the tone of the news content based on the user's emotional state received from the emotion engine. The input is the user's emotional state information. For example, if the server detects that the user is feeling stressed, it adjusts the tone of the news to be gentler. It also uses a speech synthesis API to convert the generated summary text into an audio file. The output is the tone-adjusted text and audio file.

[0310] Step 6: Content Reformatting and Delivery

[0311] The server converts the consolidated news content into a format suitable for the application and generates a video if necessary. The generated summary text is converted into audio and combined with selected illustrations to create a video. Specifically, the server uses a video editing tool (e.g., FFmpeg) to combine the audio and illustrations. The output is a completed video file. The content and tone of this video are also adjusted by the emotion engine. Finally, the server sends a push notification to the user's device to notify them of new news. When the device receives the notification, they can open the application to check the latest news summary, and if a video has been generated, they can watch it within the app. The input is consolidated news content, and the output is data converted into a viewable format.

[0312] This allows users to quickly and efficiently grasp the main points of the news and enjoy a news experience that is tailored to their emotions.

[0313] (Application example 2)

[0314] 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."

[0315] Conventional news delivery systems have difficulty adjusting content based on the user's emotional state and providing a personalized news experience. Furthermore, automatic generation of news summaries and related illustrations is insufficient. Therefore, there is a need for news delivery that is concise and visually easy for users to understand.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for analyzing the user's emotional state, means for adjusting the content and tone of the news content based on the analyzed user's emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This provides a personalized news experience according to the user's emotional state, enabling news to be provided in a concise and visually easy-to-understand manner.

[0317] "News Data" refers to the latest news information obtained from affiliated news broadcasters and news services.

[0318] A "summary" is information that briefly summarizes the contents of the acquired news data.

[0319] "Illustrations" are visual images or pictures associated with the summarized news data.

[0320] "Description" refers to text that briefly explains the summarized news data and related illustrations.

[0321] "Users" are people who use the system to receive news content.

[0322] "Emotional state" refers to the user's current mental and emotional state.

[0323] A "generative AI model" is an artificial intelligence model used to summarize news data and generate relevant descriptions and summaries.

[0324] "Analysis" is the process of understanding the content and structure of data and extracting information.

[0325] "Distribution" refers to the act of sending the generated content to the user.

[0326] "Video" refers to video content generated from news data.

[0327] "Tone" refers to the tone, atmosphere, or presentation of news content or video.

[0328] To implement this invention, a system is required that automatically acquires news data, summarizes it, generates relevant illustrations and descriptions, and provides them to users.Furthermore, it can analyze the user's emotional state and adjust the content and tone of the news content to provide a more personalized experience.

[0329] Hardware and software used

[0330] This system mainly uses the following hardware and software:

[0331] Hardware: Smartphone

[0332] software:

[0333] News API: Acquire external news data

[0334] Generative AI model: News summary generation and related content generation

[0335] Emotion Engine: Analyzing the user's emotional state

[0336] Illustration Generation Engine: Illustration Generation

[0337] System operation overview

[0338] 1. News Data Acquisition: Every morning, the server retrieves the latest news data from the partner news provider. This process is achieved by sending an HTTP request using the news API. The request includes authentication information and necessary parameters, and the retrieved news data is stored in a database on the server.

[0339] 2. Summarizing news data and generating content: The server uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. After the summary is generated, related illustrations and descriptions are generated based on this data. Illustrations are selected by analyzing keywords contained in the summary data. The descriptions are then automatically created using the generative AI model.

[0340] 3. Emotional state analysis and content adjustment: Using an emotion engine running on the user's device, the system analyzes sensor data from cameras and microphones to detect the user's emotional state. For example, if the user is feeling stressed, the system adjusts the tone of the news content to a calmer tone.

[0341] 4. News content delivery: The server converts the consolidated news content into a format suitable for the application and sends a push notification to the user's smartphone to notify them of new news. The user receives the notification and opens the application to view the summarized news. If a video has been generated, the user can also watch the video within the application.

[0342] Examples of specific examples and prompts

[0343] Examples:

[0344] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the generative AI model will generate the following summary:

[0345] "An important policy was decided at an international conference. It is about environmental protection, and countries are working together to reduce greenhouse gas emissions."

[0346] Based on this, the server selects a relevant illustration (e.g., an image of the Earth and a smoking factory) and adds a description like this:

[0347] "This shows our efforts to prevent global warming."

[0348] Example prompt sentence:

[0349] For example, to ask a generative AI model to generate a summary, the prompt might look like this:

[0350] Summarize the following news article:

[0351] "New policies regarding environmental protection were decided at yesterday's international conference. Representatives from various countries gathered together to discuss reducing greenhouse gas emissions."

[0352] An example prompt for content adjustment based on emotional state is:

[0353] Rewrite the following news summary in a tone appropriate for when the user is relaxed:

[0354] "New policies for environmental protection have been decided at an international conference. Countries will work together to reduce greenhouse gas emissions."

[0355] These examples and prompts demonstrate part of a system for providing understandable and personalized news content to users.

[0356] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0357] Step 1: Acquire news data

[0358] Every morning, the server retrieves the latest news data from a partner news service. As input, it sends an HTTP request to the news API and stores the retrieved news data in a database. The output is the stored news data.

[0359] Step 2: News data summary

[0360] The server uses a generative AI model to summarize the acquired news data. As input, the text of the news data is fed into the generative AI model, which generates a concise summary. The output is the summarized news data.

[0361] Step 3: Generate illustrations and descriptions

[0362] The server generates related illustrations and descriptions based on summarized news data. It analyzes keywords from the summarized data as input and selects illustrations based on them. It then automatically generates descriptions for the illustrations using a generative AI model. The output is the summarized news data, illustrations, and descriptions.

[0363] Step 4: Analyze emotional state

[0364] The device uses an emotion engine to analyze the user's emotional state. Sensor data from cameras, microphones, etc. is taken as input into the emotion engine, which then analyzes the user's emotions. The output is the analyzed user's emotional state.

[0365] Step 5: Tailor your news content

[0366] The server adjusts the content and tone of news content based on the analyzed user's emotional state. As input, it applies an algorithm that adjusts the tone of news content based on the emotional state. The output is the adjusted news content.

[0367] Step 6: Video Generation (Optional)

[0368] The server generates videos from summarized news data as needed. As input, it provides the summary text and illustrations to the video generation module, and adds audio narration. The output is the generated news video.

[0369] Step 7: Distributing news content

[0370] The server formats the aggregated news content for the application and delivers it to the user's smartphone via push notifications. The input includes the adjusted news content and optional video, which is then converted for visual display to the user. The output is the news content displayed on the user's device.

[0371] Step 8: Review news content

[0372] The user checks the received push notification on the device and opens the application to check the news summary. The input contains news content delivered from the server and the news is displayed through the user interface. The output is the visually displayed news content and its understanding.

[0373] 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.

[0374] 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.

[0375] 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.

[0376] [Second embodiment]

[0377] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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).

[0383] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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."

[0389] The system of the present invention automatically acquires news data, summarizes it, generates related illustrations and brief descriptions, and delivers them to users.

[0390] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0391] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0392] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0393] The server then prepares the integrated news content for delivery to the user. First, it converts the summary news, illustrations, and explanatory text into a format suitable for the application and prepares it for delivery to the device. If video is also to be generated, it converts the summary text into audio and creates a video combined with the illustrations.

[0394] The server then sends a push notification to the device to notify it of new news. The user's device receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news in a visually easy-to-understand format for the user. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0395] For example, if a news item about "important policy decisions on environmental protection made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the Earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." The user can check this on their device and quickly understand the news content.

[0396] The present invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this mode, users can easily grasp important news in their daily lives.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0400] Step 2:

[0401] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0402] Step 3:

[0403] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0404] Step 4:

[0405] The server inputs the preprocessed news data into a generative AI model, which is instructed to concisely summarize the original news data.

[0406] Step 5:

[0407] The generative AI model generates a summary, and the server stores the generated summary in a database for further processing.

[0408] Step 6:

[0409] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0410] Step 7:

[0411] The server uses the generative AI model to generate a brief description of the selected illustration, such as "This shows efforts to prevent global warming."

[0412] Step 8:

[0413] The server integrates the summary news, illustrations, and explanatory text into a single piece of content and stores this integrated data in a database.

[0414] Step 9:

[0415] The server converts the aggregated news content into a format suitable for the application. If video is also generated, the server converts the summary text into audio and creates a video that combines the audio with illustrations.

[0416] Step 10:

[0417] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0418] Step 11:

[0419] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves the news data from the server and displays it to the user.

[0420] Step 12:

[0421] When a user watches a generated video, they play it on their device and understand the news visually and aurally. The video includes a narrator's voice and related illustrations.

[0422] As described above, by having the server, terminals, and users work together to automatically acquire, summarize, and distribute news, a system is realized that allows even busy working people and elementary and junior high school students to easily understand the news.

[0423] Example 1

[0424] 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."

[0425] In modern society, it is difficult to efficiently obtain and understand the necessary news from the vast amount of information available. Busy working people and those who need to obtain information quickly are particularly required to accurately grasp the news in a short amount of time. There is also a demand for news content to be provided in a visually easy-to-understand format. However, existing news distribution systems do not adequately provide systems that can automatically generate summarized news text and distribute it together with related explanatory text and illustrations.

[0426] 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.

[0427] In this invention, the server includes means for automatically acquiring news data, means for using a generative AI model to summarize the acquired news data, means for generating related images and descriptions based on the summarized news data, means for delivering the generated summarized news data, images, and descriptions to users, means for saving the acquired news data in a database, means for saving the summarized news data again in the database, means for analyzing keywords in the summary data and selecting images based thereon, means for generating descriptions using a generative AI model, means for saving illustration information and the generated descriptions in the database, means for converting the summarized news, images, and descriptions into a format suitable for an application, means for preparing the summarized news, images, and descriptions for delivery to a terminal, and means for sending push notifications. This enables efficient and rapid execution of a series of processes, from acquiring news data to summarizing it, generating related images and descriptions, and automatically delivering them to users.

[0428] "News Data" means current news reports and related information obtained from news stations and other information providers.

[0429] "Means of acquisition" refers to the function by which the server automatically requests and receives news data using an API.

[0430] A "generative AI model" is an artificial intelligence technology that can generate summaries and explanations from given data, and is, for example, a type of natural language processing model.

[0431] "Means of summarization" refers to the function of summarizing acquired news data in a concise form using a generative AI model.

[0432] "Related images" are visual materials such as illustrations and photographs selected based on summarized news data to complement the news content.

[0433] A "description" is a concise piece of text generated based on relevant image and news data using a generative AI model.

[0434] "Means of distribution" refers to the function of sending the generated news data, images, and explanatory text to the user's terminal and displaying them.

[0435] A "database" is an information system for storing and managing news data, generated summaries, images, descriptions, etc.

[0436] "Keyword analysis means" refers to the function of extracting key terms from summaries and news data and selecting related images based on them.

[0437] The "format conversion means" refers to a function that converts the generated news data, images, and descriptions into a format that can be displayed on a user terminal.

[0438] The "means for sending push notifications" refers to a communication means by which the server notifies the user's terminal of the distribution of new news.

[0439] The system of the present invention automatically acquires news data, summarizes it, generates related images and descriptions, and delivers them to users. To implement this system, the following elements work together: a server, a terminal, and a user.

[0440] The system of the present invention uses the following hardware and software: The server is a computer with high processing power and storage capacity, and performs processes for data acquisition, analysis, generation, storage, and distribution. Specifically, the server has the function of automatically acquiring news data periodically through an API. This is done using an HTTP request, which is achieved by sending a request including authentication information and necessary parameters. This request receives the latest news data from affiliated news broadcasters and stores it in a database.

[0441] The server is responsible for summarizing the acquired news data using a generative AI model. The text of the news data is input into the generative AI model (e.g., OpenAI's GPT-4) to generate a concise summary. An example of the prompt used in this case is: "Please provide a brief summary of the following news article: [news article text]." The generated summary is stored in a database.

[0442] The server then generates relevant images and descriptions based on the summarized news data. Keywords in the summary data are analyzed and images are selected based on them. For example, if a news summary includes "important policy decisions on environmental protection at an international conference," images such as "a picture of a factory emitting smoke" or "an image showing the Earth" are selected. A generative AI model is used to create a brief description of the image. An example prompt is: "This illustration shows [keyword]. Please generate a brief description: [description of the illustration]." The generated images and descriptions are also stored in a database.

[0443] The server then converts the generated summary news, images, and descriptions into a format suitable for the application and prepares them for delivery to the device. In this case, the generative AI model also supports text-to-speech conversion of the summary. The generated data is then converted into a format suitable for the user's device.

[0444] The server then sends a push notification to the device, informing the user that there is new news. The user's device receives the notification and opens the application to view the latest news summary. The device retrieves the data from the server and displays it to the user in a visually appealing format. If a video has been generated, the user can watch it within the application.

[0445] This system allows users to instantly grasp important news even in the midst of their busy daily lives, streamlining the process of acquiring and understanding information and realizing highly optimized news delivery to users.

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1:

[0448] Every morning, the server sends an HTTP request to the API of a partner news station to automatically retrieve the latest news data.

[0449] Specific behavior:

[0450] Set the API key in the HTTP request header.

[0451] Include date information in the request body.

[0452] Send a request to a URL like "https: / / newsapi.example.com / latest?date=YYYY-MM-DD".

[0453] Input: API key, date information

[0454] Output: News data in JSON format

[0455] Step 2:

[0456] The server stores the acquired news data in a database.

[0457] Specific behavior:

[0458] Parse (analyze) the received JSON format data.

[0459] The parsed data is separated into each field and inserted into the corresponding table in the database.

[0460] Input: News data in JSON format

[0461] Output: News data stored in the news table of the database

[0462] Step 3:

[0463] The server uses a generative AI model to convert news data into concise summaries.

[0464] Specific behavior:

[0465] The news text is fed into a generative AI model.

[0466] Use the prompt: "Please briefly summarize the following news article: [news article text]."

[0467] Get the summary generated from the model response.

[0468] Input: News data text, prompt

[0469] Output: A summary generated by the generative AI model

[0470] Step 4:

[0471] The server stores the summarized news data in a database.

[0472] Specific behavior:

[0473] The generated summary sentence is inserted into the "Summary News" table of the database.

[0474] Input: Summarized news data

[0475] Output: Summary data stored in the Summary News table in the database

[0476] Step 5:

[0477] The server analyzes the keywords in the summary data and selects relevant images based on them.

[0478] Specific behavior:

[0479] Extract key nouns and verbs from the summary.

[0480] Based on the extracted keywords, appropriate illustrations are searched for in the image database.

[0481] Input: Abstract data, Keyword extraction algorithm

[0482] Output: The path to the selected image.

[0483] Step 6:

[0484] The server uses a generative AI model to generate a description of the image.

[0485] Specific behavior:

[0486] Keywords and image information are input into the generative AI model.

[0487] Use the prompt: "This illustration shows [keyword]. Please generate a brief description: [illustration description]."

[0488] Get the explanation generated in the response from the model.

[0489] Input: Keywords, image information, prompt text

[0490] Output: Generated description

[0491] Step 7:

[0492] The server stores the generated images and descriptions in a database.

[0493] Specific behavior:

[0494] The path of the illustration and the generated description are inserted into the "Illustration Description" table of the database.

[0495] Input: Image path, generated description

[0496] Output: Data stored in the illustration description table in the database

[0497] Step 8:

[0498] The server formats the news summaries, images and descriptions for the application.

[0499] Specific behavior:

[0500] Each data is compiled in JSON format.

[0501] If necessary, convert the summary text into audio (Text-to-Speech) and create a video combining it with illustrations (using a video generation API).

[0502] Input: News summary, image, description

[0503] Output: Data converted into a format that can be delivered to the user's device

[0504] Step 9:

[0505] The server sends a push notification to the device to notify it that there is new news.

[0506] Specific behavior:

[0507] Send notifications using a push notification service (e.g., Firebase Cloud Messaging).

[0508] The notification will include information about new news.

[0509] Input: Latest news

[0510] Output: Push notification sent to device

[0511] Step 10:

[0512] The device receives a notification and opens the application to view the latest news summary.

[0513] Specific behavior:

[0514] Tapping the notification will launch the application.

[0515] Within the app, a request is sent to the server to retrieve the latest news summary, images, and descriptions.

[0516] Input: Push notification, server request

[0517] Output: Retrieved latest news data, images, and descriptions

[0518] Step 11:

[0519] The terminal displays news to the user in a visually easy-to-understand format.

[0520] Specific behavior:

[0521] Set the retrieved data in the appropriate UI component.

[0522] The news text, summary, images and description are displayed on the screen.

[0523] If a video has been generated, it is played using a video player.

[0524] Input: Latest news data, images, descriptions

[0525] Output: News displayed to the user, and video playback

[0526] (Application example 1)

[0527] 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."

[0528] In today's busy society, it is important to be able to quickly and easily grasp the latest news. However, traditional news delivery methods often contain too much information and take too much time, making it difficult for users to easily understand the latest news. Another problem is that there is little visual content, making it difficult to intuitively understand the news content. Furthermore, the push notification function, which is used to quickly deliver new news, is not optimized, which can lead to users missing important information.

[0529] 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.

[0530] In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for notifying users of the existence of new news using push notifications, and means for visually displaying the summarized news, illustrations, and explanatory text on the user terminal, thereby enabling a news distribution system that allows users to grasp the main points in a short amount of time.

[0531] "News Data" is the latest information obtained from affiliated news stations.

[0532] "Automatic acquisition means" refers to a mechanism by which the server automatically acquires news data using the news broadcaster's API.

[0533] "Means for summarizing" is a function that extracts important information from acquired news data and summarizes it concisely.

[0534] The "means for generation" is a function that automatically generates related visual content and text from summarized news data.

[0535] "Illustrations" are visual images or pictures associated with the news summary.

[0536] "Description" is text that briefly explains the content of the illustration or the main points of the news.

[0537] The "distribution means" is a mechanism for transmitting the generated summary news data, illustrations, and explanatory text to the user.

[0538] "Means of notifying using push notifications" is a system that instantly notifies users of new news on their devices.

[0539] "Means for displaying on a user's device" refers to a function for visually displaying news content on a user's device such as a smartphone or tablet.

[0540] "Means for generating video" refers to a mechanism for creating video content from summary news data.

[0541] The "means for converting into audio" is a mechanism for converting the generated explanatory text into audio data.

[0542] A "generative AI model" is a model that uses artificial intelligence to summarize news data and generate related content.

[0543] In this embodiment, we will build a system that automatically acquires news data, summarizes it, generates related illustrations and explanatory text, and delivers it to users. This system mainly requires the combination of various components, such as a server, user terminal, generation AI model, news acquisition API, database, and push notification service.

[0544] First, the server automatically collects the latest news data from affiliated news stations every morning using a news retrieval API. To do this, the server sends an HTTP request containing authentication information and necessary parameters. The retrieved news data is then stored in a database (e.g., MySQL).

[0545] Next, the server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate a concise summary from the stored news data. The text of the news data is provided as input to the generative AI model, which generates a concise summary. The generated summary data is then stored back in the database.

[0546] The server then generates related illustrations and descriptions based on the summarized news data. It analyzes keywords contained in the summary data and selects pre-prepared illustrations based on them. It then uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are also stored in a database.

[0547] The server then prepares this consolidated news content for delivery to users, notifying them of new news using a push notification service (e.g., Firebase Cloud Messaging). The server then converts the news summary, illustrations, and descriptions into a format suitable for the application, converting the descriptions into audio data using FFMpeg for playback, and, if necessary, generating a video that combines the audio data with the illustrations.

[0548] When a user receives a notification on their device (e.g., an iOS or Android smartphone), they open the app to view the latest news summary. The app retrieves data from the server and displays the news summary, illustrations, and explanatory text in a visually appealing format. If a video has been generated, the user can watch it within the app.

[0549] Examples and prompts

[0550] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the following prompt sentence is given to the generative AI model:

[0551] News: An international conference has decided on a plan for countries to work together to reduce greenhouse gas emissions.

[0552] Generate a summary in five sentences or less.

[0553] Next, the prompt for generating a description after selecting an illustration is given as follows:

[0554] Illustration: Earth and factory smoke

[0555] Description:

[0556] The expected output is, for example, "This shows efforts to prevent global warming."

[0557] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0558] Step 1:

[0559] The server automatically retrieves news data every morning using the news station's API. At this time, the server sends an HTTP request including authentication information and parameters. The retrieved news data is returned to the server in JSON format or other formats. The server stores the returned news data in a MySQL database.

[0560] Input: News data (JSON format) returned from the news broadcaster's API.

[0561] Output: News data stored in a MySQL database.

[0562] Step 2:

[0563] The server uses a generative AI model (GPT-4) to generate summaries of news data retrieved from a MySQL database. The server inputs the text of the news data into the generative AI model and instructs it to generate a concise summary. The generated summary is then stored back in the MySQL database.

[0564] Input: Text news data stored in a MySQL database.

[0565] Output: Summary text stored in a MySQL database.

[0566] Step 3:

[0567] The server generates relevant illustrations and descriptions from the summary data. First, the server analyzes keywords contained in the summary data and selects appropriate illustrations from pre-prepared illustrations based on the analysis. Next, it generates descriptions for the selected illustrations using a generative AI model. The generated illustrations and descriptions are stored in a database.

[0568] Input: Summarized news data.

[0569] Output: Illustrations and descriptions stored in a MySQL database.

[0570] Step 4:

[0571] The server then converts the generated news summary, illustrations, and descriptions into a new format and reconstructs them for the application. It uses FFMpeg to convert the descriptions into audio data and then combines them with the illustrations to generate a video. All content, including the video, is then stored in a database.

[0572] Input: News summary, selected illustrations, generated description.

[0573] Output: Data, audio data, and video data formatted for the application.

[0574] Step 5:

[0575] The server uses a push notification service (Firebase Cloud Messaging) to notify the user that there is new news. When the user receives the notification, they open the application and check the latest news summary.

[0576] Enter: a notification that there's new news.

[0577] Output: Push notification to user device.

[0578] Step 6:

[0579] The user device retrieves the data from the server and displays it in a visually easy-to-understand format. Within the application, the user can view news summaries, illustrations, and explanatory text, and can also watch videos if they are generated.

[0580] Input: News summary, illustrations, descriptions, and video data from the server.

[0581] Output: News content and videos displayed on the user's device.

[0582] This detailed processing flow allows users to quickly and concisely grasp important news and receive information in a visually easy-to-understand format.

[0583] 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.

[0584] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0585] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0586] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0587] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0588] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from cameras, microphones, etc. For example, if the user is stressed, the server adjusts the tone of the news content to be calmer.

[0589] The server then formats the consolidated news content for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The emotion engine also adjusts the content and tone of the video. For example, if the user needs inspiring news, the audio tone of the video will be set to an uplifting one.

[0590] The server then sends a push notification to the device to notify it of new news. The user's device then receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news to the user in a visually easy-to-understand format. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0591] For example, if a news item about "an important policy decision on environmental protection was made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy decision was made at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." If the emotion engine recognizes that the user is in a relaxed state, the server presents this content in a simple, calm tone. The user can check this on their device and quickly understand the news content.

[0592] This invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this form, users can easily grasp important news in their daily lives and enjoy a personalized experience that responds to their emotions.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0596] Step 2:

[0597] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0598] Step 3:

[0599] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0600] Step 4:

[0601] The server inputs the preprocessed news data into a generative AI model, which is given instructions to concisely summarize the original news data. The generative AI model then generates the summarized news data.

[0602] Step 5:

[0603] The server stores the summarized news data in a database, which is used for further processing.

[0604] Step 6:

[0605] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0606] Step 7:

[0607] The server uses the generative AI model to generate a simple description of the selected illustration. For example, it generates a description such as, "This shows efforts to prevent global warming." The generated illustration and description are stored in a database.

[0608] Step 8:

[0609] The user's device recognizes the user's emotional state using sensor data from cameras, microphones, etc. The device then uses an emotion engine to analyze the user's emotions.

[0610] Step 9:

[0611] The device then sends the user's emotional data to the server, which then adjusts the content and tone of the news content based on the emotional data.

[0612] Step 10:

[0613] The server integrates the summary news, illustrations, and descriptions into a single piece of content, and stores this integrated data in a database. The news content is then adjusted based on the emotion engine.

[0614] Step 11:

[0615] The server converts the aggregated news content into a format suitable for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The content and tone of the video are also adjusted by the emotion engine.

[0616] Step 12:

[0617] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0618] Step 13:

[0619] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves news data from the server and displays the news in a visually easy-to-understand format for the user.

[0620] Step 14:

[0621] When users watch the generated video, they play it on their device and use their eyes and ears to understand the news. The video includes a narrator voice and relevant illustrations, which are tailored based on emotion.

[0622] As described above, by linking the server, terminals, and users to automatically acquire, summarize, and distribute news, a system has been realized that allows even busy working people and elementary and junior high school students to easily understand the news. In addition, by incorporating an emotion engine, a personalized news experience is provided according to the user's emotional state.

[0623] Example 2

[0624] 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."

[0625] Conventional news delivery systems make it difficult for users to efficiently obtain vast amounts of information, and they lack the ability to provide personalized information tailored to their emotional state. Furthermore, people who do not have time to read long news articles need a way to quickly grasp important information. Furthermore, technologies for generating content that combines visual and emotional elements are insufficient.

[0626] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data using a generative AI model, means for analyzing keywords in the summarized news data and selecting related illustrations, means for generating explanatory text for the selected illustrations using the generative AI model, a user terminal including an emotion engine that analyzes the user's emotional state, means for adjusting the tone of the news content depending on the emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This allows the user to quickly grasp the news information they need and enjoy a personalized news experience tailored to their emotional state.

[0627] "News data" is a collection of current news information obtained from news broadcasters and other information providers.

[0628] A "generative AI model" is a type of artificial intelligence algorithm that analyzes news data and automatically generates summaries and descriptions.

[0629] A "summary" is a concise text that extracts key information from news data.

[0630] "Keywords" are important words or phrases extracted from news data or summary data.

[0631] "Illustrations" are images or diagrams used to visually represent news content.

[0632] "Description" is text that provides additional information about the selected illustration and explains its meaning or content.

[0633] An "emotion engine" is a combination of software and hardware used to analyze a user's emotional state.

[0634] "User terminal" means a device used by a user to access the emotion engine in real time and view news content.

[0635] "Tone" refers to the style and mood of the news content, which is adjusted according to the user's emotional state.

[0636] "Format conversion" is a process of converting news data and generated content into a format that can be displayed appropriately on the user's terminal.

[0637] "Speech synthesis" is the technology of converting generated text into speech.

[0638] "Push notification" is a function that notifies the user's device of new news in real time from the server.

[0639] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0640] Every morning, the server automatically retrieves the latest news data from partner news stations. This process involves sending an HTTP request using an API, including authentication information and required parameters. For example, a scheduled task can be set up to issue an HTTP request at a specific time, access the news station's API endpoint, and store the retrieved data in JSON format in the server's database.

[0641] Next, the server uses a generative AI model to summarize the acquired news data. It extracts the text field of the news data and sends the generative AI model a prompt: "Summarize the latest news in 100 characters or less." The generative AI model generates a summary based on this prompt and stores the result back in the database. As a specific example, news data such as "An important announcement regarding greenhouse gas reduction was made at an international conference" is converted into a summary such as "An important greenhouse gas reduction policy was announced at an international conference."

[0642] The server then generates relevant illustrations and descriptions from the summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. For example, if the summary contains the keyword "greenhouse gas," it selects an image depicting factory smoke. Next, it uses a generative AI model to send a prompt asking, "Please generate a description for this illustration," and the description is automatically generated. The generated description takes the form, for example, "This shows efforts to prevent global warming."

[0643] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from the camera, microphone, etc. For example, if the server detects that the user is relaxed, it adjusts the tone of the news to be presented calmly. When a speech synthesis API is used to convert the summary text into speech and generate a video that combines the speech with illustrations, the background music and speech tempo are also adjusted based on the user's emotional state.

[0644] The server finally converts the aggregated news content into a format suitable for the app and sends a push notification to the user's device to notify them of new news. When the user receives the notification, they can open the app to view the latest news summary. The generated video can also be viewed within the app.

[0645] As a concrete example, when news about "important policy decisions on environmental protection at an international conference" is acquired, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects a related illustration and adds a caption that reads, "This shows efforts to prevent global warming." If the emotion engine determines that the user is relaxed, the server presents the news in a calm tone. The user can view this on their device and quickly understand the news content.

[0646] The following prompts are used as examples of input to the generative AI model:

[0647] Generate a summary of the news data:

[0648] "Summarize the latest news in 100 characters or less"

[0649] And an example prompt to generate the description:

[0650] Please generate a description for this illustration:

[0651] "Efforts to prevent global warming"

[0652] This system allows users to efficiently grasp the main points of news and enjoy a personalized news experience that responds to their emotions.

[0653] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0654] Step 1: Obtaining news data

[0655] Every morning, the server retrieves the latest news data from a partner news station. The server sends an HTTP request using the API at a fixed time, including authentication information and required parameters. Specifically, the server sets up a scheduled task (e.g., a cron job) to access the news station's API endpoint. The input is the news station's API address and authentication information, and the output is the retrieved news data (in JSON format). Once the news data is returned, the server stores it in a database.

[0656] Step 2: Summarizing the news data

[0657] The news data acquired by the server is input into the generative AI model, which generates a summary. Specifically, the text fields of the news data are extracted and sent to the generative AI model as a prompt. The input is the text portion of the news data, and a prompt is generated: "Please summarize the latest news in 100 characters or less." Based on this prompt, the generative AI model generates a summary. The output is the generated summary, which is saved in a database. For example, the news data "An important announcement regarding greenhouse gas reduction was made at an international conference" becomes a summary: "An important greenhouse gas reduction policy was announced at an international conference."

[0658] Step 3: Generate illustrations and descriptions

[0659] The server generates relevant illustrations and descriptions based on summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. The input is the analyzed keywords. For example, if the summary contains the keyword "greenhouse gas," it obtains an image depicting factory smoke. Next, it uses a generative AI model to send a prompt saying, "Please generate a description for this illustration." The generative AI model generates and outputs the description. The description generated as output will be in the format, "This shows efforts to prevent global warming." The generated illustrations and descriptions are stored in a database.

[0660] Step 4: Analyze emotional state

[0661] An emotion engine built into the user's device analyzes the user's emotional state using sensor data from cameras, microphones, etc. The input is real-time sensor data obtained from cameras and microphones. The emotion engine analyzes this sensor data and determines the user's emotional state, such as whether they are stressed or relaxed. The output is information about the user's emotional state, such as stress or relaxation. This information is sent to a server and used to adjust the tone of the news content.

[0662] Step 5: Adjust the tone of your content

[0663] The server adjusts the tone of the news content based on the user's emotional state received from the emotion engine. The input is the user's emotional state information. For example, if the server detects that the user is feeling stressed, it adjusts the tone of the news to be gentler. It also uses a speech synthesis API to convert the generated summary text into an audio file. The output is the tone-adjusted text and audio file.

[0664] Step 6: Content Reformatting and Delivery

[0665] The server converts the consolidated news content into a format suitable for the application and generates a video if necessary. The generated summary text is converted into audio and combined with selected illustrations to create a video. Specifically, the server uses a video editing tool (e.g., FFmpeg) to combine the audio and illustrations. The output is a completed video file. The content and tone of this video are also adjusted by the emotion engine. Finally, the server sends a push notification to the user's device to notify them of new news. When the device receives the notification, they can open the application to check the latest news summary, and if a video has been generated, they can watch it within the app. The input is consolidated news content, and the output is data converted into a viewable format.

[0666] This allows users to quickly and efficiently grasp the main points of the news and enjoy a news experience that is tailored to their emotions.

[0667] (Application example 2)

[0668] 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."

[0669] Conventional news delivery systems have difficulty adjusting content based on the user's emotional state and providing a personalized news experience. Furthermore, automatic generation of news summaries and related illustrations is insufficient. Therefore, there is a need for news delivery that is concise and visually easy for users to understand.

[0670] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for analyzing the user's emotional state, means for adjusting the content and tone of the news content based on the analyzed user's emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This provides a personalized news experience according to the user's emotional state, enabling news to be provided in a concise and visually easy-to-understand manner.

[0671] "News Data" refers to the latest news information obtained from affiliated news broadcasters and news services.

[0672] A "summary" is information that briefly summarizes the contents of the acquired news data.

[0673] "Illustrations" are visual images or pictures associated with the summarized news data.

[0674] "Description" refers to text that briefly explains the summarized news data and related illustrations.

[0675] "Users" are people who use the system to receive news content.

[0676] "Emotional state" refers to the user's current mental and emotional state.

[0677] A "generative AI model" is an artificial intelligence model used to summarize news data and generate relevant descriptions and summaries.

[0678] "Analysis" is the process of understanding the content and structure of data and extracting information.

[0679] "Distribution" refers to the act of sending the generated content to the user.

[0680] "Video" refers to video content generated from news data.

[0681] "Tone" refers to the tone, atmosphere, or presentation of news content or video.

[0682] To implement this invention, a system is required that automatically acquires news data, summarizes it, generates relevant illustrations and descriptions, and provides them to users.Furthermore, it can analyze the user's emotional state and adjust the content and tone of the news content to provide a more personalized experience.

[0683] Hardware and software used

[0684] This system mainly uses the following hardware and software:

[0685] Hardware: Smartphone

[0686] software:

[0687] News API: Acquire external news data

[0688] Generative AI model: News summary generation and related content generation

[0689] Emotion Engine: Analyzing the user's emotional state

[0690] Illustration Generation Engine: Illustration Generation

[0691] System operation overview

[0692] 1. News Data Acquisition: Every morning, the server retrieves the latest news data from the partner news provider. This process is achieved by sending an HTTP request using the news API. The request includes authentication information and necessary parameters, and the retrieved news data is stored in a database on the server.

[0693] 2. Summarizing news data and generating content: The server uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. After the summary is generated, related illustrations and descriptions are generated based on this data. Illustrations are selected by analyzing keywords contained in the summary data. The descriptions are then automatically created using the generative AI model.

[0694] 3. Emotional state analysis and content adjustment: Using an emotion engine running on the user's device, the system analyzes sensor data from cameras and microphones to detect the user's emotional state. For example, if the user is feeling stressed, the system adjusts the tone of the news content to a calmer tone.

[0695] 4. News content delivery: The server converts the consolidated news content into a format suitable for the application and sends a push notification to the user's smartphone to notify them of new news. The user receives the notification and opens the application to view the summarized news. If a video has been generated, the user can also watch the video within the application.

[0696] Examples of specific examples and prompts

[0697] Examples:

[0698] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the generative AI model will generate the following summary:

[0699] "An important policy was decided at an international conference. It is about environmental protection, and countries are working together to reduce greenhouse gas emissions."

[0700] Based on this, the server selects a relevant illustration (e.g., an image of the Earth and a smoking factory) and adds a description like this:

[0701] "This shows our efforts to prevent global warming."

[0702] Example prompt sentence:

[0703] For example, to ask a generative AI model to generate a summary, the prompt might look like this:

[0704] Summarize the following news article:

[0705] "New policies regarding environmental protection were decided at yesterday's international conference. Representatives from various countries gathered together to discuss reducing greenhouse gas emissions."

[0706] An example prompt for content adjustment based on emotional state is:

[0707] Rewrite the following news summary in a tone appropriate for when the user is relaxed:

[0708] "New policies for environmental protection have been decided at an international conference. Countries will work together to reduce greenhouse gas emissions."

[0709] These examples and prompts demonstrate part of a system for providing understandable and personalized news content to users.

[0710] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0711] Step 1: Acquire news data

[0712] Every morning, the server retrieves the latest news data from a partner news service. As input, it sends an HTTP request to the news API and stores the retrieved news data in a database. The output is the stored news data.

[0713] Step 2: News data summary

[0714] The server uses a generative AI model to summarize the acquired news data. As input, the text of the news data is fed into the generative AI model, which generates a concise summary. The output is the summarized news data.

[0715] Step 3: Generate illustrations and descriptions

[0716] The server generates related illustrations and descriptions based on summarized news data. It analyzes keywords from the summarized data as input and selects illustrations based on them. It then automatically generates descriptions for the illustrations using a generative AI model. The output is the summarized news data, illustrations, and descriptions.

[0717] Step 4: Analyze emotional state

[0718] The device uses an emotion engine to analyze the user's emotional state. Sensor data from cameras, microphones, etc. is taken as input into the emotion engine, which then analyzes the user's emotions. The output is the analyzed user's emotional state.

[0719] Step 5: Tailor your news content

[0720] The server adjusts the content and tone of news content based on the analyzed user's emotional state. As input, it applies an algorithm that adjusts the tone of news content based on the emotional state. The output is the adjusted news content.

[0721] Step 6: Video Generation (Optional)

[0722] The server generates videos from summarized news data as needed. As input, it provides the summary text and illustrations to the video generation module, and adds audio narration. The output is the generated news video.

[0723] Step 7: Distributing news content

[0724] The server formats the aggregated news content for the application and delivers it to the user's smartphone via push notifications. The input includes the adjusted news content and optional video, which is then converted for visual display to the user. The output is the news content displayed on the user's device.

[0725] Step 8: Review news content

[0726] The user checks the received push notification on the device and opens the application to check the news summary. The input contains news content delivered from the server and the news is displayed through the user interface. The output is the visually displayed news content and its understanding.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] [Third embodiment]

[0731] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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).

[0737] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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."

[0743] The system of the present invention automatically acquires news data, summarizes it, generates related illustrations and brief descriptions, and delivers them to users.

[0744] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0745] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0746] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0747] The server then prepares the integrated news content for delivery to the user. First, it converts the summary news, illustrations, and explanatory text into a format suitable for the application and prepares it for delivery to the device. If video is also to be generated, it converts the summary text into audio and creates a video combined with the illustrations.

[0748] The server then sends a push notification to the device to notify it of new news. The user's device receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news in a visually easy-to-understand format for the user. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0749] For example, if a news item about "important policy decisions on environmental protection made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the Earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." The user can check this on their device and quickly understand the news content.

[0750] The present invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this mode, users can easily grasp important news in their daily lives.

[0751] The processing flow will be explained below.

[0752] Step 1:

[0753] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0754] Step 2:

[0755] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0756] Step 3:

[0757] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0758] Step 4:

[0759] The server inputs the preprocessed news data into a generative AI model, which is instructed to concisely summarize the original news data.

[0760] Step 5:

[0761] The generative AI model generates a summary, and the server stores the generated summary in a database for further processing.

[0762] Step 6:

[0763] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0764] Step 7:

[0765] The server uses the generative AI model to generate a brief description of the selected illustration, such as "This shows efforts to prevent global warming."

[0766] Step 8:

[0767] The server integrates the summary news, illustrations, and explanatory text into a single piece of content and stores this integrated data in a database.

[0768] Step 9:

[0769] The server converts the aggregated news content into a format suitable for the application. If video is also generated, the server converts the summary text into audio and creates a video that combines the audio with illustrations.

[0770] Step 10:

[0771] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0772] Step 11:

[0773] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves the news data from the server and displays it to the user.

[0774] Step 12:

[0775] When a user watches a generated video, they play it on their device and understand the news visually and aurally. The video includes a narrator's voice and related illustrations.

[0776] As described above, by having the server, terminals, and users work together to automatically acquire, summarize, and distribute news, a system is realized that allows even busy working people and elementary and junior high school students to easily understand the news.

[0777] Example 1

[0778] 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."

[0779] In modern society, it is difficult to efficiently obtain and understand the necessary news from the vast amount of information available. Busy working people and those who need to obtain information quickly are particularly required to accurately grasp the news in a short amount of time. There is also a demand for news content to be provided in a visually easy-to-understand format. However, existing news distribution systems do not adequately provide systems that can automatically generate summarized news text and distribute it together with related explanatory text and illustrations.

[0780] 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.

[0781] In this invention, the server includes means for automatically acquiring news data, means for using a generative AI model to summarize the acquired news data, means for generating related images and descriptions based on the summarized news data, means for delivering the generated summarized news data, images, and descriptions to users, means for saving the acquired news data in a database, means for saving the summarized news data again in the database, means for analyzing keywords in the summary data and selecting images based thereon, means for generating descriptions using a generative AI model, means for saving illustration information and the generated descriptions in the database, means for converting the summarized news, images, and descriptions into a format suitable for an application, means for preparing the summarized news, images, and descriptions for delivery to a terminal, and means for sending push notifications. This enables efficient and rapid execution of a series of processes, from acquiring news data to summarizing it, generating related images and descriptions, and automatically delivering them to users.

[0782] "News Data" means current news reports and related information obtained from news stations and other information providers.

[0783] "Means of acquisition" refers to the function by which the server automatically requests and receives news data using an API.

[0784] A "generative AI model" is an artificial intelligence technology that can generate summaries and explanations from given data, and is, for example, a type of natural language processing model.

[0785] "Means of summarization" refers to the function of summarizing acquired news data in a concise form using a generative AI model.

[0786] "Related images" are visual materials such as illustrations and photographs selected based on summarized news data to complement the news content.

[0787] A "description" is a concise piece of text generated based on relevant image and news data using a generative AI model.

[0788] "Means of distribution" refers to the function of sending the generated news data, images, and explanatory text to the user's terminal and displaying them.

[0789] A "database" is an information system for storing and managing news data, generated summaries, images, descriptions, etc.

[0790] "Keyword analysis means" refers to the function of extracting key terms from summaries and news data and selecting related images based on them.

[0791] The "format conversion means" refers to a function that converts the generated news data, images, and descriptions into a format that can be displayed on a user terminal.

[0792] The "means for sending push notifications" refers to a communication means by which the server notifies the user's terminal of the distribution of new news.

[0793] The system of the present invention automatically acquires news data, summarizes it, generates related images and descriptions, and delivers them to users. To implement this system, the following elements work together: a server, a terminal, and a user.

[0794] The system of the present invention uses the following hardware and software: The server is a computer with high processing power and storage capacity, and performs processes for data acquisition, analysis, generation, storage, and distribution. Specifically, the server has the function of automatically acquiring news data periodically through an API. This is done using an HTTP request, which is achieved by sending a request including authentication information and necessary parameters. This request receives the latest news data from affiliated news broadcasters and stores it in a database.

[0795] The server is responsible for summarizing the acquired news data using a generative AI model. The text of the news data is input into the generative AI model (e.g., OpenAI's GPT-4) to generate a concise summary. An example of the prompt used in this case is: "Please provide a brief summary of the following news article: [news article text]." The generated summary is stored in a database.

[0796] The server then generates relevant images and descriptions based on the summarized news data. Keywords in the summary data are analyzed and images are selected based on them. For example, if a news summary includes "important policy decisions on environmental protection at an international conference," images such as "a picture of a factory emitting smoke" or "an image showing the Earth" are selected. A generative AI model is used to create a brief description of the image. An example prompt is: "This illustration shows [keyword]. Please generate a brief description: [description of the illustration]." The generated images and descriptions are also stored in a database.

[0797] The server then converts the generated summary news, images, and descriptions into a format suitable for the application and prepares them for delivery to the device. In this case, the generative AI model also supports text-to-speech conversion of the summary. The generated data is then converted into a format suitable for the user's device.

[0798] The server then sends a push notification to the device, informing the user that there is new news. The user's device receives the notification and opens the application to view the latest news summary. The device retrieves the data from the server and displays it to the user in a visually appealing format. If a video has been generated, the user can watch it within the application.

[0799] This system allows users to instantly grasp important news even in the midst of their busy daily lives, streamlining the process of acquiring and understanding information and realizing highly optimized news delivery to users.

[0800] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0801] Step 1:

[0802] Every morning, the server sends an HTTP request to the API of a partner news station to automatically retrieve the latest news data.

[0803] Specific behavior:

[0804] Set the API key in the HTTP request header.

[0805] Include date information in the request body.

[0806] Send a request to a URL like "https: / / newsapi.example.com / latest?date=YYYY-MM-DD".

[0807] Input: API key, date information

[0808] Output: News data in JSON format

[0809] Step 2:

[0810] The server stores the acquired news data in a database.

[0811] Specific behavior:

[0812] Parse (analyze) the received JSON format data.

[0813] The parsed data is separated into each field and inserted into the corresponding table in the database.

[0814] Input: News data in JSON format

[0815] Output: News data stored in the news table of the database

[0816] Step 3:

[0817] The server uses a generative AI model to convert news data into concise summaries.

[0818] Specific behavior:

[0819] The news text is fed into a generative AI model.

[0820] Use the prompt: "Please briefly summarize the following news article: [news article text]."

[0821] Get the summary generated from the model response.

[0822] Input: News data text, prompt

[0823] Output: A summary generated by the generative AI model

[0824] Step 4:

[0825] The server stores the summarized news data in a database.

[0826] Specific behavior:

[0827] The generated summary sentence is inserted into the "Summary News" table of the database.

[0828] Input: Summarized news data

[0829] Output: Summary data stored in the Summary News table in the database

[0830] Step 5:

[0831] The server analyzes the keywords in the summary data and selects relevant images based on them.

[0832] Specific behavior:

[0833] Extract key nouns and verbs from the summary.

[0834] Based on the extracted keywords, appropriate illustrations are searched for in the image database.

[0835] Input: Abstract data, Keyword extraction algorithm

[0836] Output: The path to the selected image.

[0837] Step 6:

[0838] The server uses a generative AI model to generate a description of the image.

[0839] Specific behavior:

[0840] Keywords and image information are input into the generative AI model.

[0841] Use the prompt: "This illustration shows [keyword]. Please generate a brief description: [illustration description]."

[0842] Get the explanation generated in the response from the model.

[0843] Input: Keywords, image information, prompt text

[0844] Output: Generated description

[0845] Step 7:

[0846] The server stores the generated images and descriptions in a database.

[0847] Specific behavior:

[0848] The path of the illustration and the generated description are inserted into the "Illustration Description" table of the database.

[0849] Input: Image path, generated description

[0850] Output: Data stored in the illustration description table in the database

[0851] Step 8:

[0852] The server formats the news summaries, images and descriptions for the application.

[0853] Specific behavior:

[0854] Each data is compiled in JSON format.

[0855] If necessary, convert the summary text into audio (Text-to-Speech) and create a video combining it with illustrations (using a video generation API).

[0856] Input: News summary, image, description

[0857] Output: Data converted into a format that can be delivered to the user's device

[0858] Step 9:

[0859] The server sends a push notification to the device to notify it that there is new news.

[0860] Specific behavior:

[0861] Send notifications using a push notification service (e.g., Firebase Cloud Messaging).

[0862] The notification will include information about new news.

[0863] Input: Latest news

[0864] Output: Push notification sent to device

[0865] Step 10:

[0866] The device receives a notification and opens the application to view the latest news summary.

[0867] Specific behavior:

[0868] Tapping the notification will launch the application.

[0869] Within the app, a request is sent to the server to retrieve the latest news summary, images, and descriptions.

[0870] Input: Push notification, server request

[0871] Output: Retrieved latest news data, images, and descriptions

[0872] Step 11:

[0873] The terminal displays news to the user in a visually easy-to-understand format.

[0874] Specific behavior:

[0875] Set the retrieved data in the appropriate UI component.

[0876] The news text, summary, images and description are displayed on the screen.

[0877] If a video has been generated, it is played using a video player.

[0878] Input: Latest news data, images, descriptions

[0879] Output: News displayed to the user, and video playback

[0880] (Application example 1)

[0881] 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."

[0882] In today's busy society, it is important to be able to quickly and easily grasp the latest news. However, traditional news delivery methods often contain too much information and take too much time, making it difficult for users to easily understand the latest news. Another problem is that there is little visual content, making it difficult to intuitively understand the news content. Furthermore, the push notification function, which is used to quickly deliver new news, is not optimized, which can lead to users missing important information.

[0883] 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.

[0884] In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for notifying users of the existence of new news using push notifications, and means for visually displaying the summarized news, illustrations, and explanatory text on the user terminal, thereby enabling a news distribution system that allows users to grasp the main points in a short amount of time.

[0885] "News Data" is the latest information obtained from affiliated news stations.

[0886] "Automatic acquisition means" refers to a mechanism by which the server automatically acquires news data using the news broadcaster's API.

[0887] "Means for summarizing" is a function that extracts important information from acquired news data and summarizes it concisely.

[0888] The "means for generation" is a function that automatically generates related visual content and text from summarized news data.

[0889] "Illustrations" are visual images or pictures associated with the news summary.

[0890] "Description" is text that briefly explains the content of the illustration or the main points of the news.

[0891] The "distribution means" is a mechanism for transmitting the generated summary news data, illustrations, and explanatory text to the user.

[0892] "Means of notifying using push notifications" is a system that instantly notifies users of new news on their devices.

[0893] "Means for displaying on a user's device" refers to a function for visually displaying news content on a user's device such as a smartphone or tablet.

[0894] "Means for generating video" refers to a mechanism for creating video content from summary news data.

[0895] The "means for converting into audio" is a mechanism for converting the generated explanatory text into audio data.

[0896] A "generative AI model" is a model that uses artificial intelligence to summarize news data and generate related content.

[0897] In this embodiment, we will build a system that automatically acquires news data, summarizes it, generates related illustrations and explanatory text, and delivers it to users. This system mainly requires the combination of various components, such as a server, user terminal, generation AI model, news acquisition API, database, and push notification service.

[0898] First, the server automatically collects the latest news data from affiliated news stations every morning using a news retrieval API. To do this, the server sends an HTTP request containing authentication information and necessary parameters. The retrieved news data is then stored in a database (e.g., MySQL).

[0899] Next, the server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate a concise summary from the stored news data. The text of the news data is provided as input to the generative AI model, which generates a concise summary. The generated summary data is then stored back in the database.

[0900] The server then generates related illustrations and descriptions based on the summarized news data. It analyzes keywords contained in the summary data and selects pre-prepared illustrations based on them. It then uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are also stored in a database.

[0901] The server then prepares this consolidated news content for delivery to users, notifying them of new news using a push notification service (e.g., Firebase Cloud Messaging). The server then converts the news summary, illustrations, and descriptions into a format suitable for the application, converting the descriptions into audio data using FFMpeg for playback, and, if necessary, generating a video that combines the audio data with the illustrations.

[0902] When a user receives a notification on their device (e.g., an iOS or Android smartphone), they open the app to view the latest news summary. The app retrieves data from the server and displays the news summary, illustrations, and explanatory text in a visually appealing format. If a video has been generated, the user can watch it within the app.

[0903] Examples and prompts

[0904] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the following prompt sentence is given to the generative AI model:

[0905] News: An international conference has decided on a plan for countries to work together to reduce greenhouse gas emissions.

[0906] Generate a summary in five sentences or less.

[0907] Next, the prompt for generating a description after selecting an illustration is given as follows:

[0908] Illustration: Earth and factory smoke

[0909] Description:

[0910] The expected output is, for example, "This shows efforts to prevent global warming."

[0911] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0912] Step 1:

[0913] The server automatically retrieves news data every morning using the news station's API. At this time, the server sends an HTTP request including authentication information and parameters. The retrieved news data is returned to the server in JSON format or other formats. The server stores the returned news data in a MySQL database.

[0914] Input: News data (JSON format) returned from the news broadcaster's API.

[0915] Output: News data stored in a MySQL database.

[0916] Step 2:

[0917] The server uses a generative AI model (GPT-4) to generate summaries of news data retrieved from a MySQL database. The server inputs the text of the news data into the generative AI model and instructs it to generate a concise summary. The generated summary is then stored back in the MySQL database.

[0918] Input: Text news data stored in a MySQL database.

[0919] Output: Summary text stored in a MySQL database.

[0920] Step 3:

[0921] The server generates relevant illustrations and descriptions from the summary data. First, the server analyzes keywords contained in the summary data and selects appropriate illustrations from pre-prepared illustrations based on the analysis. Next, it generates descriptions for the selected illustrations using a generative AI model. The generated illustrations and descriptions are stored in a database.

[0922] Input: Summarized news data.

[0923] Output: Illustrations and descriptions stored in a MySQL database.

[0924] Step 4:

[0925] The server then converts the generated news summary, illustrations, and descriptions into a new format and reconstructs them for the application. It uses FFMpeg to convert the descriptions into audio data and then combines them with the illustrations to generate a video. All content, including the video, is then stored in a database.

[0926] Input: News summary, selected illustrations, generated description.

[0927] Output: Data, audio data, and video data formatted for the application.

[0928] Step 5:

[0929] The server uses a push notification service (Firebase Cloud Messaging) to notify the user that there is new news. When the user receives the notification, they open the application and check the latest news summary.

[0930] Enter: a notification that there's new news.

[0931] Output: Push notification to user device.

[0932] Step 6:

[0933] The user device retrieves the data from the server and displays it in a visually easy-to-understand format. Within the application, the user can view news summaries, illustrations, and explanatory text, and can also watch videos if they are generated.

[0934] Input: News summary, illustrations, descriptions, and video data from the server.

[0935] Output: News content and videos displayed on the user's device.

[0936] This detailed processing flow allows users to quickly and concisely grasp important news and receive information in a visually easy-to-understand format.

[0937] 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.

[0938] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0939] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[0940] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[0941] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[0942] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from cameras, microphones, etc. For example, if the user is stressed, the server adjusts the tone of the news content to be calmer.

[0943] The server then formats the consolidated news content for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The emotion engine also adjusts the content and tone of the video. For example, if the user needs inspiring news, the audio tone of the video will be set to an uplifting one.

[0944] The server then sends a push notification to the device to notify it of new news. The user's device then receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news to the user in a visually easy-to-understand format. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[0945] For example, if a news item about "an important policy decision on environmental protection was made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy decision was made at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." If the emotion engine recognizes that the user is in a relaxed state, the server presents this content in a simple, calm tone. The user can check this on their device and quickly understand the news content.

[0946] This invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this form, users can easily grasp important news in their daily lives and enjoy a personalized experience that responds to their emotions.

[0947] The processing flow will be explained below.

[0948] Step 1:

[0949] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[0950] Step 2:

[0951] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[0952] Step 3:

[0953] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[0954] Step 4:

[0955] The server inputs the preprocessed news data into a generative AI model, which is given instructions to concisely summarize the original news data. The generative AI model then generates the summarized news data.

[0956] Step 5:

[0957] The server stores the summarized news data in a database, which is used for further processing.

[0958] Step 6:

[0959] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[0960] Step 7:

[0961] The server uses the generative AI model to generate a simple description of the selected illustration. For example, it generates a description such as, "This shows efforts to prevent global warming." The generated illustration and description are stored in a database.

[0962] Step 8:

[0963] The user's device recognizes the user's emotional state using sensor data from cameras, microphones, etc. The device then uses an emotion engine to analyze the user's emotions.

[0964] Step 9:

[0965] The device then sends the user's emotional data to the server, which then adjusts the content and tone of the news content based on the emotional data.

[0966] Step 10:

[0967] The server integrates the summary news, illustrations, and descriptions into a single piece of content, and stores this integrated data in a database. The news content is then adjusted based on the emotion engine.

[0968] Step 11:

[0969] The server converts the aggregated news content into a format suitable for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The content and tone of the video are also adjusted by the emotion engine.

[0970] Step 12:

[0971] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[0972] Step 13:

[0973] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves news data from the server and displays the news in a visually easy-to-understand format for the user.

[0974] Step 14:

[0975] When users watch the generated video, they play it on their device and use their eyes and ears to understand the news. The video includes a narrator voice and relevant illustrations, which are tailored based on emotion.

[0976] As described above, by linking the server, terminals, and users to automatically acquire, summarize, and distribute news, a system has been realized that allows even busy working people and elementary and junior high school students to easily understand the news. In addition, by incorporating an emotion engine, a personalized news experience is provided according to the user's emotional state.

[0977] Example 2

[0978] 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."

[0979] Conventional news delivery systems make it difficult for users to efficiently obtain vast amounts of information, and they lack the ability to provide personalized information tailored to their emotional state. Furthermore, people who do not have time to read long news articles need a way to quickly grasp important information. Furthermore, technologies for generating content that combines visual and emotional elements are insufficient.

[0980] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data using a generative AI model, means for analyzing keywords in the summarized news data and selecting related illustrations, means for generating explanatory text for the selected illustrations using the generative AI model, a user terminal including an emotion engine that analyzes the user's emotional state, means for adjusting the tone of the news content depending on the emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This allows the user to quickly grasp the news information they need and enjoy a personalized news experience tailored to their emotional state.

[0981] "News data" is a collection of current news information obtained from news broadcasters and other information providers.

[0982] A "generative AI model" is a type of artificial intelligence algorithm that analyzes news data and automatically generates summaries and descriptions.

[0983] A "summary" is a concise text that extracts key information from news data.

[0984] "Keywords" are important words or phrases extracted from news data or summary data.

[0985] "Illustrations" are images or diagrams used to visually represent news content.

[0986] "Description" is text that provides additional information about the selected illustration and explains its meaning or content.

[0987] An "emotion engine" is a combination of software and hardware used to analyze a user's emotional state.

[0988] "User terminal" means a device used by a user to access the emotion engine in real time and view news content.

[0989] "Tone" refers to the style and mood of the news content, which is adjusted according to the user's emotional state.

[0990] "Format conversion" is a process of converting news data and generated content into a format that can be displayed appropriately on the user's terminal.

[0991] "Speech synthesis" is the technology of converting generated text into speech.

[0992] "Push notification" is a function that notifies the user's device of new news in real time from the server.

[0993] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[0994] Every morning, the server automatically retrieves the latest news data from partner news stations. This process involves sending an HTTP request using an API, including authentication information and required parameters. For example, a scheduled task can be set up to issue an HTTP request at a specific time, access the news station's API endpoint, and store the retrieved data in JSON format in the server's database.

[0995] Next, the server uses a generative AI model to summarize the acquired news data. It extracts the text field of the news data and sends the generative AI model a prompt: "Summarize the latest news in 100 characters or less." The generative AI model generates a summary based on this prompt and stores the result back in the database. As a specific example, news data such as "An important announcement regarding greenhouse gas reduction was made at an international conference" is converted into a summary such as "An important greenhouse gas reduction policy was announced at an international conference."

[0996] The server then generates relevant illustrations and descriptions from the summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. For example, if the summary contains the keyword "greenhouse gas," it selects an image depicting factory smoke. Next, it uses a generative AI model to send a prompt asking, "Please generate a description for this illustration," and the description is automatically generated. The generated description takes the form, for example, "This shows efforts to prevent global warming."

[0997] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from the camera, microphone, etc. For example, if the server detects that the user is relaxed, it adjusts the tone of the news to be presented calmly. When a speech synthesis API is used to convert the summary text into speech and generate a video that combines the speech with illustrations, the background music and speech tempo are also adjusted based on the user's emotional state.

[0998] The server finally converts the aggregated news content into a format suitable for the app and sends a push notification to the user's device to notify them of new news. When the user receives the notification, they can open the app to view the latest news summary. The generated video can also be viewed within the app.

[0999] As a concrete example, when news about "important policy decisions on environmental protection at an international conference" is acquired, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects a related illustration and adds a caption that reads, "This shows efforts to prevent global warming." If the emotion engine determines that the user is relaxed, the server presents the news in a calm tone. The user can view this on their device and quickly understand the news content.

[1000] The following prompts are used as examples of input to the generative AI model:

[1001] Generate a summary of the news data:

[1002] "Summarize the latest news in 100 characters or less"

[1003] And an example prompt to generate the description:

[1004] Please generate a description for this illustration:

[1005] "Efforts to prevent global warming"

[1006] This system allows users to efficiently grasp the main points of news and enjoy a personalized news experience that responds to their emotions.

[1007] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1008] Step 1: Obtaining news data

[1009] Every morning, the server retrieves the latest news data from a partner news station. The server sends an HTTP request using the API at a fixed time, including authentication information and required parameters. Specifically, the server sets up a scheduled task (e.g., a cron job) to access the news station's API endpoint. The input is the news station's API address and authentication information, and the output is the retrieved news data (in JSON format). Once the news data is returned, the server stores it in a database.

[1010] Step 2: Summarizing the news data

[1011] The news data acquired by the server is input into the generative AI model, which generates a summary. Specifically, the text fields of the news data are extracted and sent to the generative AI model as a prompt. The input is the text portion of the news data, and a prompt is generated: "Please summarize the latest news in 100 characters or less." Based on this prompt, the generative AI model generates a summary. The output is the generated summary, which is saved in a database. For example, the news data "An important announcement regarding greenhouse gas reduction was made at an international conference" becomes a summary: "An important greenhouse gas reduction policy was announced at an international conference."

[1012] Step 3: Generate illustrations and descriptions

[1013] The server generates relevant illustrations and descriptions based on summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. The input is the analyzed keywords. For example, if the summary contains the keyword "greenhouse gas," it obtains an image depicting factory smoke. Next, it uses a generative AI model to send a prompt saying, "Please generate a description for this illustration." The generative AI model generates and outputs the description. The description generated as output will be in the format, "This shows efforts to prevent global warming." The generated illustrations and descriptions are stored in a database.

[1014] Step 4: Analyze emotional state

[1015] An emotion engine built into the user's device analyzes the user's emotional state using sensor data from cameras, microphones, etc. The input is real-time sensor data obtained from cameras and microphones. The emotion engine analyzes this sensor data and determines the user's emotional state, such as whether they are stressed or relaxed. The output is information about the user's emotional state, such as stress or relaxation. This information is sent to a server and used to adjust the tone of the news content.

[1016] Step 5: Adjust the tone of your content

[1017] The server adjusts the tone of the news content based on the user's emotional state received from the emotion engine. The input is the user's emotional state information. For example, if the server detects that the user is feeling stressed, it adjusts the tone of the news to be gentler. It also uses a speech synthesis API to convert the generated summary text into an audio file. The output is the tone-adjusted text and audio file.

[1018] Step 6: Content Reformatting and Delivery

[1019] The server converts the consolidated news content into a format suitable for the application and generates a video if necessary. The generated summary text is converted into audio and combined with selected illustrations to create a video. Specifically, the server uses a video editing tool (e.g., FFmpeg) to combine the audio and illustrations. The output is a completed video file. The content and tone of this video are also adjusted by the emotion engine. Finally, the server sends a push notification to the user's device to notify them of new news. When the device receives the notification, they can open the application to check the latest news summary, and if a video has been generated, they can watch it within the app. The input is consolidated news content, and the output is data converted into a viewable format.

[1020] This allows users to quickly and efficiently grasp the main points of the news and enjoy a news experience that is tailored to their emotions.

[1021] (Application example 2)

[1022] 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."

[1023] Conventional news delivery systems have difficulty adjusting content based on the user's emotional state and providing a personalized news experience. Furthermore, automatic generation of news summaries and related illustrations is insufficient. Therefore, there is a need for news delivery that is concise and visually easy for users to understand.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for analyzing the user's emotional state, means for adjusting the content and tone of the news content based on the analyzed user's emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This provides a personalized news experience according to the user's emotional state, enabling news to be provided in a concise and visually easy-to-understand manner.

[1025] "News Data" refers to the latest news information obtained from affiliated news broadcasters and news services.

[1026] A "summary" is information that briefly summarizes the contents of the acquired news data.

[1027] "Illustrations" are visual images or pictures associated with the summarized news data.

[1028] "Description" refers to text that briefly explains the summarized news data and related illustrations.

[1029] "Users" are people who use the system to receive news content.

[1030] "Emotional state" refers to the user's current mental and emotional state.

[1031] A "generative AI model" is an artificial intelligence model used to summarize news data and generate relevant descriptions and summaries.

[1032] "Analysis" is the process of understanding the content and structure of data and extracting information.

[1033] "Distribution" refers to the act of sending the generated content to the user.

[1034] "Video" refers to video content generated from news data.

[1035] "Tone" refers to the tone, atmosphere, or presentation of news content or video.

[1036] To implement this invention, a system is required that automatically acquires news data, summarizes it, generates relevant illustrations and descriptions, and provides them to users.Furthermore, it can analyze the user's emotional state and adjust the content and tone of the news content to provide a more personalized experience.

[1037] Hardware and software used

[1038] This system mainly uses the following hardware and software:

[1039] Hardware: Smartphone

[1040] software:

[1041] News API: Acquire external news data

[1042] Generative AI model: News summary generation and related content generation

[1043] Emotion Engine: Analyzing the user's emotional state

[1044] Illustration Generation Engine: Illustration Generation

[1045] System operation overview

[1046] 1. News Data Acquisition: Every morning, the server retrieves the latest news data from the partner news provider. This process is achieved by sending an HTTP request using the news API. The request includes authentication information and necessary parameters, and the retrieved news data is stored in a database on the server.

[1047] 2. Summarizing news data and generating content: The server uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. After the summary is generated, related illustrations and descriptions are generated based on this data. Illustrations are selected by analyzing keywords contained in the summary data. The descriptions are then automatically created using the generative AI model.

[1048] 3. Emotional state analysis and content adjustment: Using an emotion engine running on the user's device, the system analyzes sensor data from cameras and microphones to detect the user's emotional state. For example, if the user is feeling stressed, the system adjusts the tone of the news content to a calmer tone.

[1049] 4. News content delivery: The server converts the consolidated news content into a format suitable for the application and sends a push notification to the user's smartphone to notify them of new news. The user receives the notification and opens the application to view the summarized news. If a video has been generated, the user can also watch the video within the application.

[1050] Examples of specific examples and prompts

[1051] Examples:

[1052] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the generative AI model will generate the following summary:

[1053] "An important policy was decided at an international conference. It is about environmental protection, and countries are working together to reduce greenhouse gas emissions."

[1054] Based on this, the server selects a relevant illustration (e.g., an image of the Earth and a smoking factory) and adds a description like this:

[1055] "This shows our efforts to prevent global warming."

[1056] Example prompt sentence:

[1057] For example, to ask a generative AI model to generate a summary, the prompt might look like this:

[1058] Summarize the following news article:

[1059] "New policies regarding environmental protection were decided at yesterday's international conference. Representatives from various countries gathered together to discuss reducing greenhouse gas emissions."

[1060] An example prompt for content adjustment based on emotional state is:

[1061] Rewrite the following news summary in a tone appropriate for when the user is relaxed:

[1062] "New policies for environmental protection have been decided at an international conference. Countries will work together to reduce greenhouse gas emissions."

[1063] These examples and prompts demonstrate part of a system for providing understandable and personalized news content to users.

[1064] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1065] Step 1: Acquire news data

[1066] Every morning, the server retrieves the latest news data from a partner news service. As input, it sends an HTTP request to the news API and stores the retrieved news data in a database. The output is the stored news data.

[1067] Step 2: News data summary

[1068] The server uses a generative AI model to summarize the acquired news data. As input, the text of the news data is fed into the generative AI model, which generates a concise summary. The output is the summarized news data.

[1069] Step 3: Generate illustrations and descriptions

[1070] The server generates related illustrations and descriptions based on summarized news data. It analyzes keywords from the summarized data as input and selects illustrations based on them. It then automatically generates descriptions for the illustrations using a generative AI model. The output is the summarized news data, illustrations, and descriptions.

[1071] Step 4: Analyze emotional state

[1072] The device uses an emotion engine to analyze the user's emotional state. Sensor data from cameras, microphones, etc. is taken as input into the emotion engine, which then analyzes the user's emotions. The output is the analyzed user's emotional state.

[1073] Step 5: Tailor your news content

[1074] The server adjusts the content and tone of news content based on the analyzed user's emotional state. As input, it applies an algorithm that adjusts the tone of news content based on the emotional state. The output is the adjusted news content.

[1075] Step 6: Video Generation (Optional)

[1076] The server generates videos from summarized news data as needed. As input, it provides the summary text and illustrations to the video generation module, and adds audio narration. The output is the generated news video.

[1077] Step 7: Distributing news content

[1078] The server formats the aggregated news content for the application and delivers it to the user's smartphone via push notifications. The input includes the adjusted news content and optional video, which is then converted for visual display to the user. The output is the news content displayed on the user's device.

[1079] Step 8: Review news content

[1080] The user checks the received push notification on the device and opens the application to check the news summary. The input contains news content delivered from the server and the news is displayed through the user interface. The output is the visually displayed news content and its understanding.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] [Fourth embodiment]

[1085] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1086] 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.

[1087] 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).

[1088] 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.

[1089] 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.

[1090] 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).

[1091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] 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.

[1096] 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.

[1097] 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."

[1098] The system of the present invention automatically acquires news data, summarizes it, generates related illustrations and brief descriptions, and delivers them to users.

[1099] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[1100] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[1101] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[1102] The server then prepares the integrated news content for delivery to the user. First, it converts the summary news, illustrations, and explanatory text into a format suitable for the application and prepares it for delivery to the device. If video is also to be generated, it converts the summary text into audio and creates a video combined with the illustrations.

[1103] The server then sends a push notification to the device to notify it of new news. The user's device receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news in a visually easy-to-understand format for the user. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[1104] For example, if a news item about "important policy decisions on environmental protection made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the Earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." The user can check this on their device and quickly understand the news content.

[1105] The present invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this mode, users can easily grasp important news in their daily lives.

[1106] The processing flow will be explained below.

[1107] Step 1:

[1108] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[1109] Step 2:

[1110] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[1111] Step 3:

[1112] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[1113] Step 4:

[1114] The server inputs the preprocessed news data into a generative AI model, which is instructed to concisely summarize the original news data.

[1115] Step 5:

[1116] The generative AI model generates a summary, and the server stores the generated summary in a database for further processing.

[1117] Step 6:

[1118] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[1119] Step 7:

[1120] The server uses the generative AI model to generate a brief description of the selected illustration, such as "This shows efforts to prevent global warming."

[1121] Step 8:

[1122] The server integrates the summary news, illustrations, and explanatory text into a single piece of content and stores this integrated data in a database.

[1123] Step 9:

[1124] The server converts the aggregated news content into a format suitable for the application. If video is also generated, the server converts the summary text into audio and creates a video that combines the audio with illustrations.

[1125] Step 10:

[1126] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[1127] Step 11:

[1128] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves the news data from the server and displays it to the user.

[1129] Step 12:

[1130] When a user watches a generated video, they play it on their device and understand the news visually and aurally. The video includes a narrator's voice and related illustrations.

[1131] As described above, by having the server, terminals, and users work together to automatically acquire, summarize, and distribute news, a system is realized that allows even busy working people and elementary and junior high school students to easily understand the news.

[1132] Example 1

[1133] 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."

[1134] In modern society, it is difficult to efficiently obtain and understand the necessary news from the vast amount of information available. Busy working people and those who need to obtain information quickly are particularly required to accurately grasp the news in a short amount of time. There is also a demand for news content to be provided in a visually easy-to-understand format. However, existing news distribution systems do not adequately provide systems that can automatically generate summarized news text and distribute it together with related explanatory text and illustrations.

[1135] 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.

[1136] In this invention, the server includes means for automatically acquiring news data, means for using a generative AI model to summarize the acquired news data, means for generating related images and descriptions based on the summarized news data, means for delivering the generated summarized news data, images, and descriptions to users, means for saving the acquired news data in a database, means for saving the summarized news data again in the database, means for analyzing keywords in the summary data and selecting images based thereon, means for generating descriptions using a generative AI model, means for saving illustration information and the generated descriptions in the database, means for converting the summarized news, images, and descriptions into a format suitable for an application, means for preparing the summarized news, images, and descriptions for delivery to a terminal, and means for sending push notifications. This enables efficient and rapid execution of a series of processes, from acquiring news data to summarizing it, generating related images and descriptions, and automatically delivering them to users.

[1137] "News Data" means current news reports and related information obtained from news stations and other information providers.

[1138] "Means of acquisition" refers to the function by which the server automatically requests and receives news data using an API.

[1139] A "generative AI model" is an artificial intelligence technology that can generate summaries and explanations from given data, and is, for example, a type of natural language processing model.

[1140] "Means of summarization" refers to the function of summarizing acquired news data in a concise form using a generative AI model.

[1141] "Related images" are visual materials such as illustrations and photographs selected based on summarized news data to complement the news content.

[1142] A "description" is a concise piece of text generated based on relevant image and news data using a generative AI model.

[1143] "Means of distribution" refers to the function of sending the generated news data, images, and explanatory text to the user's terminal and displaying them.

[1144] A "database" is an information system for storing and managing news data, generated summaries, images, descriptions, etc.

[1145] "Keyword analysis means" refers to the function of extracting key terms from summaries and news data and selecting related images based on them.

[1146] The "format conversion means" refers to a function that converts the generated news data, images, and descriptions into a format that can be displayed on a user terminal.

[1147] The "means for sending push notifications" refers to a communication means by which the server notifies the user's terminal of the distribution of new news.

[1148] The system of the present invention automatically acquires news data, summarizes it, generates related images and descriptions, and delivers them to users. To implement this system, the following elements work together: a server, a terminal, and a user.

[1149] The system of the present invention uses the following hardware and software: The server is a computer with high processing power and storage capacity, and performs processes for data acquisition, analysis, generation, storage, and distribution. Specifically, the server has the function of automatically acquiring news data periodically through an API. This is done using an HTTP request, which is achieved by sending a request including authentication information and necessary parameters. This request receives the latest news data from affiliated news broadcasters and stores it in a database.

[1150] The server is responsible for summarizing the acquired news data using a generative AI model. The text of the news data is input into the generative AI model (e.g., OpenAI's GPT-4) to generate a concise summary. An example of the prompt used in this case is: "Please provide a brief summary of the following news article: [news article text]." The generated summary is stored in a database.

[1151] The server then generates relevant images and descriptions based on the summarized news data. Keywords in the summary data are analyzed and images are selected based on them. For example, if a news summary includes "important policy decisions on environmental protection at an international conference," images such as "a picture of a factory emitting smoke" or "an image showing the Earth" are selected. A generative AI model is used to create a brief description of the image. An example prompt is: "This illustration shows [keyword]. Please generate a brief description: [description of the illustration]." The generated images and descriptions are also stored in a database.

[1152] The server then converts the generated summary news, images, and descriptions into a format suitable for the application and prepares them for delivery to the device. In this case, the generative AI model also supports text-to-speech conversion of the summary. The generated data is then converted into a format suitable for the user's device.

[1153] The server then sends a push notification to the device, informing the user that there is new news. The user's device receives the notification and opens the application to view the latest news summary. The device retrieves the data from the server and displays it to the user in a visually appealing format. If a video has been generated, the user can watch it within the application.

[1154] This system allows users to instantly grasp important news even in the midst of their busy daily lives, streamlining the process of acquiring and understanding information and realizing highly optimized news delivery to users.

[1155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1156] Step 1:

[1157] Every morning, the server sends an HTTP request to the API of a partner news station to automatically retrieve the latest news data.

[1158] Specific behavior:

[1159] Set the API key in the HTTP request header.

[1160] Include date information in the request body.

[1161] Send a request to a URL like "https: / / newsapi.example.com / latest?date=YYYY-MM-DD".

[1162] Input: API key, date information

[1163] Output: News data in JSON format

[1164] Step 2:

[1165] The server stores the acquired news data in a database.

[1166] Specific behavior:

[1167] Parse (analyze) the received JSON format data.

[1168] The parsed data is separated into each field and inserted into the corresponding table in the database.

[1169] Input: News data in JSON format

[1170] Output: News data stored in the news table of the database

[1171] Step 3:

[1172] The server uses a generative AI model to convert news data into concise summaries.

[1173] Specific behavior:

[1174] The news text is fed into a generative AI model.

[1175] Use the prompt: "Please briefly summarize the following news article: [news article text]."

[1176] Get the summary generated from the model response.

[1177] Input: News data text, prompt

[1178] Output: A summary generated by the generative AI model

[1179] Step 4:

[1180] The server stores the summarized news data in a database.

[1181] Specific behavior:

[1182] The generated summary sentence is inserted into the "Summary News" table of the database.

[1183] Input: Summarized news data

[1184] Output: Summary data stored in the Summary News table in the database

[1185] Step 5:

[1186] The server analyzes the keywords in the summary data and selects relevant images based on them.

[1187] Specific behavior:

[1188] Extract key nouns and verbs from the summary.

[1189] Based on the extracted keywords, appropriate illustrations are searched for in the image database.

[1190] Input: Abstract data, Keyword extraction algorithm

[1191] Output: The path to the selected image.

[1192] Step 6:

[1193] The server uses a generative AI model to generate a description of the image.

[1194] Specific behavior:

[1195] Keywords and image information are input into the generative AI model.

[1196] Use the prompt: "This illustration shows [keyword]. Please generate a brief description: [illustration description]."

[1197] Get the explanation generated in the response from the model.

[1198] Input: Keywords, image information, prompt text

[1199] Output: Generated description

[1200] Step 7:

[1201] The server stores the generated images and descriptions in a database.

[1202] Specific behavior:

[1203] The path of the illustration and the generated description are inserted into the "Illustration Description" table of the database.

[1204] Input: Image path, generated description

[1205] Output: Data stored in the illustration description table in the database

[1206] Step 8:

[1207] The server formats the news summaries, images and descriptions for the application.

[1208] Specific behavior:

[1209] Each data is compiled in JSON format.

[1210] If necessary, convert the summary text into audio (Text-to-Speech) and create a video combining it with illustrations (using a video generation API).

[1211] Input: News summary, image, description

[1212] Output: Data converted into a format that can be delivered to the user's device

[1213] Step 9:

[1214] The server sends a push notification to the device to notify it that there is new news.

[1215] Specific behavior:

[1216] Send notifications using a push notification service (e.g., Firebase Cloud Messaging).

[1217] The notification will include information about new news.

[1218] Input: Latest news

[1219] Output: Push notification sent to device

[1220] Step 10:

[1221] The device receives a notification and opens the application to view the latest news summary.

[1222] Specific behavior:

[1223] Tapping the notification will launch the application.

[1224] Within the app, a request is sent to the server to retrieve the latest news summary, images, and descriptions.

[1225] Input: Push notification, server request

[1226] Output: Retrieved latest news data, images, and descriptions

[1227] Step 11:

[1228] The terminal displays news to the user in a visually easy-to-understand format.

[1229] Specific behavior:

[1230] Set the retrieved data in the appropriate UI component.

[1231] The news text, summary, images and description are displayed on the screen.

[1232] If a video has been generated, it is played using a video player.

[1233] Input: Latest news data, images, descriptions

[1234] Output: News displayed to the user, and video playback

[1235] (Application example 1)

[1236] 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."

[1237] In today's busy society, it is important to be able to quickly and easily grasp the latest news. However, traditional news delivery methods often contain too much information and take too much time, making it difficult for users to easily understand the latest news. Another problem is that there is little visual content, making it difficult to intuitively understand the news content. Furthermore, the push notification function, which is used to quickly deliver new news, is not optimized, which can lead to users missing important information.

[1238] 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.

[1239] In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for notifying users of the existence of new news using push notifications, and means for visually displaying the summarized news, illustrations, and explanatory text on the user terminal, thereby enabling a news distribution system that allows users to grasp the main points in a short amount of time.

[1240] "News Data" is the latest information obtained from affiliated news stations.

[1241] "Automatic acquisition means" refers to a mechanism by which the server automatically acquires news data using the news broadcaster's API.

[1242] "Means for summarizing" is a function that extracts important information from acquired news data and summarizes it concisely.

[1243] The "means for generation" is a function that automatically generates related visual content and text from summarized news data.

[1244] "Illustrations" are visual images or pictures associated with the news summary.

[1245] "Description" is text that briefly explains the content of the illustration or the main points of the news.

[1246] The "distribution means" is a mechanism for transmitting the generated summary news data, illustrations, and explanatory text to the user.

[1247] "Means of notifying using push notifications" is a system that instantly notifies users of new news on their devices.

[1248] "Means for displaying on a user's device" refers to a function for visually displaying news content on a user's device such as a smartphone or tablet.

[1249] "Means for generating video" refers to a mechanism for creating video content from summary news data.

[1250] The "means for converting into audio" is a mechanism for converting the generated explanatory text into audio data.

[1251] A "generative AI model" is a model that uses artificial intelligence to summarize news data and generate related content.

[1252] In this embodiment, we will build a system that automatically acquires news data, summarizes it, generates related illustrations and explanatory text, and delivers it to users. This system mainly requires the combination of various components, such as a server, user terminal, generation AI model, news acquisition API, database, and push notification service.

[1253] First, the server automatically collects the latest news data from affiliated news stations every morning using a news retrieval API. To do this, the server sends an HTTP request containing authentication information and necessary parameters. The retrieved news data is then stored in a database (e.g., MySQL).

[1254] Next, the server uses a generative AI model (such as OpenAI's GPT-4) to automatically generate a concise summary from the stored news data. The text of the news data is provided as input to the generative AI model, which generates a concise summary. The generated summary data is then stored back in the database.

[1255] The server then generates related illustrations and descriptions based on the summarized news data. It analyzes keywords contained in the summary data and selects pre-prepared illustrations based on them. It then uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are also stored in a database.

[1256] The server then prepares this consolidated news content for delivery to users, notifying them of new news using a push notification service (e.g., Firebase Cloud Messaging). The server then converts the news summary, illustrations, and descriptions into a format suitable for the application, converting the descriptions into audio data using FFMpeg for playback, and, if necessary, generating a video that combines the audio data with the illustrations.

[1257] When a user receives a notification on their device (e.g., an iOS or Android smartphone), they open the app to view the latest news summary. The app retrieves data from the server and displays the news summary, illustrations, and explanatory text in a visually appealing format. If a video has been generated, the user can watch it within the app.

[1258] Examples and prompts

[1259] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the following prompt sentence is given to the generative AI model:

[1260] News: An international conference has decided on a plan for countries to work together to reduce greenhouse gas emissions.

[1261] Generate a summary in five sentences or less.

[1262] Next, the prompt for generating a description after selecting an illustration is given as follows:

[1263] Illustration: Earth and factory smoke

[1264] Description:

[1265] The expected output is, for example, "This shows efforts to prevent global warming."

[1266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1267] Step 1:

[1268] The server automatically retrieves news data every morning using the news station's API. At this time, the server sends an HTTP request including authentication information and parameters. The retrieved news data is returned to the server in JSON format or other formats. The server stores the returned news data in a MySQL database.

[1269] Input: News data (JSON format) returned from the news broadcaster's API.

[1270] Output: News data stored in a MySQL database.

[1271] Step 2:

[1272] The server uses a generative AI model (GPT-4) to generate summaries of news data retrieved from a MySQL database. The server inputs the text of the news data into the generative AI model and instructs it to generate a concise summary. The generated summary is then stored back in the MySQL database.

[1273] Input: Text news data stored in a MySQL database.

[1274] Output: Summary text stored in a MySQL database.

[1275] Step 3:

[1276] The server generates relevant illustrations and descriptions from the summary data. First, the server analyzes keywords contained in the summary data and selects appropriate illustrations from pre-prepared illustrations based on the analysis. Next, it generates descriptions for the selected illustrations using a generative AI model. The generated illustrations and descriptions are stored in a database.

[1277] Input: Summarized news data.

[1278] Output: Illustrations and descriptions stored in a MySQL database.

[1279] Step 4:

[1280] The server then converts the generated news summary, illustrations, and descriptions into a new format and reconstructs them for the application. It uses FFMpeg to convert the descriptions into audio data and then combines them with the illustrations to generate a video. All content, including the video, is then stored in a database.

[1281] Input: News summary, selected illustrations, generated description.

[1282] Output: Data, audio data, and video data formatted for the application.

[1283] Step 5:

[1284] The server uses a push notification service (Firebase Cloud Messaging) to notify the user that there is new news. When the user receives the notification, they open the application and check the latest news summary.

[1285] Enter: a notification that there's new news.

[1286] Output: Push notification to user device.

[1287] Step 6:

[1288] The user device retrieves the data from the server and displays it in a visually easy-to-understand format. Within the application, the user can view news summaries, illustrations, and explanatory text, and can also watch videos if they are generated.

[1289] Input: News summary, illustrations, descriptions, and video data from the server.

[1290] Output: News content and videos displayed on the user's device.

[1291] This detailed processing flow allows users to quickly and concisely grasp important news and receive information in a visually easy-to-understand format.

[1292] 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.

[1293] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[1294] First, the server automatically retrieves the latest news data from partner news stations every morning. This is achieved by sending an HTTP request using the API, including authentication information and necessary parameters. The server then stores the retrieved news data in a database.

[1295] The server then uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. The summarized news data is then stored back in the database.

[1296] The server then generates relevant illustrations and descriptions based on the summarized news data. First, it analyzes keywords contained in the summary data and selects illustrations based on them. The selected illustrations could be, for example, a picture of a factory emitting smoke or an image of the Earth. Next, it uses a generative AI model to automatically generate a brief description of the illustration. The generated illustrations and descriptions are stored in a database.

[1297] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from cameras, microphones, etc. For example, if the user is stressed, the server adjusts the tone of the news content to be calmer.

[1298] The server then formats the consolidated news content for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The emotion engine also adjusts the content and tone of the video. For example, if the user needs inspiring news, the audio tone of the video will be set to an uplifting one.

[1299] The server then sends a push notification to the device to notify it of new news. The user's device then receives the notification and opens the application to check the latest news summary. The device retrieves the data from the server and displays the news to the user in a visually easy-to-understand format. If a video has been generated, the user can also watch the video within the application. This system allows users to quickly grasp the main points of the news.

[1300] For example, if a news item about "an important policy decision on environmental protection was made at an international conference" is retrieved, the generative AI model generates a summary that reads, "An important policy decision was made at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects an illustration of the earth and a factory emitting smoke, and adds the caption, "This shows efforts to prevent global warming." If the emotion engine recognizes that the user is in a relaxed state, the server presents this content in a simple, calm tone. The user can check this on their device and quickly understand the news content.

[1301] This invention allows even busy working people and elementary and junior high school students to quickly understand the news and acquire information effectively. By implementing this form, users can easily grasp important news in their daily lives and enjoy a personalized experience that responds to their emotions.

[1302] The processing flow will be explained below.

[1303] Step 1:

[1304] The server sends an HTTP request to the API of a partner broadcasting station every morning at 6:00 AM. The request includes authentication information and parameters for retrieving news data. The broadcasting station returns the latest news data in JSON format to the server.

[1305] Step 2:

[1306] The server receives the acquired news data and stores it in a database, which can be done using a SQL or NoSQL database.

[1307] Step 3:

[1308] The server retrieves the latest news data from the database, preprocesses it, removes unnecessary parts, and cleans the text data.

[1309] Step 4:

[1310] The server inputs the preprocessed news data into a generative AI model, which is given instructions to concisely summarize the original news data. The generative AI model then generates the summarized news data.

[1311] Step 5:

[1312] The server stores the summarized news data in a database, which is used for further processing.

[1313] Step 6:

[1314] The server retrieves summarized news data from the database and analyzes the keywords contained in the summary. Based on the analyzed keywords, the server selects relevant illustrations from the illustration library.

[1315] Step 7:

[1316] The server uses the generative AI model to generate a simple description of the selected illustration. For example, it generates a description such as, "This shows efforts to prevent global warming." The generated illustration and description are stored in a database.

[1317] Step 8:

[1318] The user's device recognizes the user's emotional state using sensor data from cameras, microphones, etc. The device then uses an emotion engine to analyze the user's emotions.

[1319] Step 9:

[1320] The device then sends the user's emotional data to the server, which then adjusts the content and tone of the news content based on the emotional data.

[1321] Step 10:

[1322] The server integrates the summary news, illustrations, and descriptions into a single piece of content, and stores this integrated data in a database. The news content is then adjusted based on the emotion engine.

[1323] Step 11:

[1324] The server converts the aggregated news content into a format suitable for the application. If a video is also generated, the summary text is converted into audio and combined with illustrations to create a video. The content and tone of the video are also adjusted by the emotion engine.

[1325] Step 12:

[1326] The server sends a push notification to the user's device, which contains a message informing them that new news content is available.

[1327] Step 13:

[1328] The user receives a push notification on their device, opens the application, and checks the latest news summary. The device retrieves news data from the server and displays the news in a visually easy-to-understand format for the user.

[1329] Step 14:

[1330] When users watch the generated video, they play it on their device and use their eyes and ears to understand the news. The video includes a narrator voice and relevant illustrations, which are tailored based on emotion.

[1331] As described above, by linking the server, terminals, and users to automatically acquire, summarize, and distribute news, a system has been realized that allows even busy working people and elementary and junior high school students to easily understand the news. In addition, by incorporating an emotion engine, a personalized news experience is provided according to the user's emotional state.

[1332] Example 2

[1333] 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."

[1334] Conventional news delivery systems make it difficult for users to efficiently obtain vast amounts of information, and they lack the ability to provide personalized information tailored to their emotional state. Furthermore, people who do not have time to read long news articles need a way to quickly grasp important information. Furthermore, technologies for generating content that combines visual and emotional elements are insufficient.

[1335] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data using a generative AI model, means for analyzing keywords in the summarized news data and selecting related illustrations, means for generating explanatory text for the selected illustrations using the generative AI model, a user terminal including an emotion engine that analyzes the user's emotional state, means for adjusting the tone of the news content depending on the emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This allows the user to quickly grasp the news information they need and enjoy a personalized news experience tailored to their emotional state.

[1336] "News data" is a collection of current news information obtained from news broadcasters and other information providers.

[1337] A "generative AI model" is a type of artificial intelligence algorithm that analyzes news data and automatically generates summaries and descriptions.

[1338] A "summary" is a concise text that extracts key information from news data.

[1339] "Keywords" are important words or phrases extracted from news data or summary data.

[1340] "Illustrations" are images or diagrams used to visually represent news content.

[1341] "Description" is text that provides additional information about the selected illustration and explains its meaning or content.

[1342] An "emotion engine" is a combination of software and hardware used to analyze a user's emotional state.

[1343] "User terminal" means a device used by a user to access the emotion engine in real time and view news content.

[1344] "Tone" refers to the style and mood of the news content, which is adjusted according to the user's emotional state.

[1345] "Format conversion" is a process of converting news data and generated content into a format that can be displayed appropriately on the user's terminal.

[1346] "Speech synthesis" is the technology of converting generated text into speech.

[1347] "Push notification" is a function that notifies the user's device of new news in real time from the server.

[1348] The system of the present invention automatically retrieves news data, summarizes it, generates relevant illustrations and brief descriptions, and delivers them to users. Furthermore, by combining it with an emotion engine, it can adjust news content according to the user's emotional state, providing a more personalized experience.

[1349] Every morning, the server automatically retrieves the latest news data from partner news stations. This process involves sending an HTTP request using an API, including authentication information and required parameters. For example, a scheduled task can be set up to issue an HTTP request at a specific time, access the news station's API endpoint, and store the retrieved data in JSON format in the server's database.

[1350] Next, the server uses a generative AI model to summarize the acquired news data. It extracts the text field of the news data and sends the generative AI model a prompt: "Summarize the latest news in 100 characters or less." The generative AI model generates a summary based on this prompt and stores the result back in the database. As a specific example, news data such as "An important announcement regarding greenhouse gas reduction was made at an international conference" is converted into a summary such as "An important greenhouse gas reduction policy was announced at an international conference."

[1351] The server then generates relevant illustrations and descriptions from the summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. For example, if the summary contains the keyword "greenhouse gas," it selects an image depicting factory smoke. Next, it uses a generative AI model to send a prompt asking, "Please generate a description for this illustration," and the description is automatically generated. The generated description takes the form, for example, "This shows efforts to prevent global warming."

[1352] Furthermore, the server uses an emotion engine to recognize the user's emotional state and adjust the content and tone of the news content accordingly. The emotion engine runs on the user's device and analyzes the user's emotions using sensor data from the camera, microphone, etc. For example, if the server detects that the user is relaxed, it adjusts the tone of the news to be presented calmly. When a speech synthesis API is used to convert the summary text into speech and generate a video that combines the speech with illustrations, the background music and speech tempo are also adjusted based on the user's emotional state.

[1353] The server finally converts the aggregated news content into a format suitable for the app and sends a push notification to the user's device to notify them of new news. When the user receives the notification, they can open the app to view the latest news summary. The generated video can also be viewed within the app.

[1354] As a concrete example, when news about "important policy decisions on environmental protection at an international conference" is acquired, the generative AI model generates a summary that reads, "An important policy was decided at an international conference. It concerns environmental protection, and countries plan to cooperate to reduce greenhouse gas emissions." Based on this summary, the server selects a related illustration and adds a caption that reads, "This shows efforts to prevent global warming." If the emotion engine determines that the user is relaxed, the server presents the news in a calm tone. The user can view this on their device and quickly understand the news content.

[1355] The following prompts are used as examples of input to the generative AI model:

[1356] Generate a summary of the news data:

[1357] "Summarize the latest news in 100 characters or less"

[1358] And an example prompt to generate the description:

[1359] Please generate a description for this illustration:

[1360] "Efforts to prevent global warming"

[1361] This system allows users to efficiently grasp the main points of news and enjoy a personalized news experience that responds to their emotions.

[1362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1363] Step 1: Obtaining news data

[1364] Every morning, the server retrieves the latest news data from a partner news station. The server sends an HTTP request using the API at a fixed time, including authentication information and required parameters. Specifically, the server sets up a scheduled task (e.g., a cron job) to access the news station's API endpoint. The input is the news station's API address and authentication information, and the output is the retrieved news data (in JSON format). Once the news data is returned, the server stores it in a database.

[1365] Step 2: Summarizing the news data

[1366] The news data acquired by the server is input into the generative AI model, which generates a summary. Specifically, the text fields of the news data are extracted and sent to the generative AI model as a prompt. The input is the text portion of the news data, and a prompt is generated: "Please summarize the latest news in 100 characters or less." Based on this prompt, the generative AI model generates a summary. The output is the generated summary, which is saved in a database. For example, the news data "An important announcement regarding greenhouse gas reduction was made at an international conference" becomes a summary: "An important greenhouse gas reduction policy was announced at an international conference."

[1367] Step 3: Generate illustrations and descriptions

[1368] The server generates relevant illustrations and descriptions based on summarized news data. First, it analyzes the keywords contained in the summary data and selects appropriate illustrations from a database or external API based on that. The input is the analyzed keywords. For example, if the summary contains the keyword "greenhouse gas," it obtains an image depicting factory smoke. Next, it uses a generative AI model to send a prompt saying, "Please generate a description for this illustration." The generative AI model generates and outputs the description. The description generated as output will be in the format, "This shows efforts to prevent global warming." The generated illustrations and descriptions are stored in a database.

[1369] Step 4: Analyze emotional state

[1370] An emotion engine built into the user's device analyzes the user's emotional state using sensor data from cameras, microphones, etc. The input is real-time sensor data obtained from cameras and microphones. The emotion engine analyzes this sensor data and determines the user's emotional state, such as whether they are stressed or relaxed. The output is information about the user's emotional state, such as stress or relaxation. This information is sent to a server and used to adjust the tone of the news content.

[1371] Step 5: Adjust the tone of your content

[1372] The server adjusts the tone of the news content based on the user's emotional state received from the emotion engine. The input is the user's emotional state information. For example, if the server detects that the user is feeling stressed, it adjusts the tone of the news to be gentler. It also uses a speech synthesis API to convert the generated summary text into an audio file. The output is the tone-adjusted text and audio file.

[1373] Step 6: Content Reformatting and Delivery

[1374] The server converts the consolidated news content into a format suitable for the application and generates a video if necessary. The generated summary text is converted into audio and combined with selected illustrations to create a video. Specifically, the server uses a video editing tool (e.g., FFmpeg) to combine the audio and illustrations. The output is a completed video file. The content and tone of this video are also adjusted by the emotion engine. Finally, the server sends a push notification to the user's device to notify them of new news. When the device receives the notification, they can open the application to check the latest news summary, and if a video has been generated, they can watch it within the app. The input is consolidated news content, and the output is data converted into a viewable format.

[1375] This allows users to quickly and efficiently grasp the main points of the news and enjoy a news experience that is tailored to their emotions.

[1376] (Application example 2)

[1377] 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."

[1378] Conventional news delivery systems have difficulty adjusting content based on the user's emotional state and providing a personalized news experience. Furthermore, automatic generation of news summaries and related illustrations is insufficient. Therefore, there is a need for news delivery that is concise and visually easy for users to understand.

[1379] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring news data, means for summarizing the acquired news data, means for generating related illustrations and explanatory text based on the summarized news data, means for analyzing the user's emotional state, means for adjusting the content and tone of the news content based on the analyzed user's emotional state, and means for delivering the generated summarized news data, illustrations, and explanatory text to the user. This provides a personalized news experience according to the user's emotional state, enabling news to be provided in a concise and visually easy-to-understand manner.

[1380] "News Data" refers to the latest news information obtained from affiliated news broadcasters and news services.

[1381] A "summary" is information that briefly summarizes the contents of the acquired news data.

[1382] "Illustrations" are visual images or pictures associated with the summarized news data.

[1383] "Description" refers to text that briefly explains the summarized news data and related illustrations.

[1384] "Users" are people who use the system to receive news content.

[1385] "Emotional state" refers to the user's current mental and emotional state.

[1386] A "generative AI model" is an artificial intelligence model used to summarize news data and generate relevant descriptions and summaries.

[1387] "Analysis" is the process of understanding the content and structure of data and extracting information.

[1388] "Distribution" refers to the act of sending the generated content to the user.

[1389] "Video" refers to video content generated from news data.

[1390] "Tone" refers to the tone, atmosphere, or presentation of news content or video.

[1391] To implement this invention, a system is required that automatically acquires news data, summarizes it, generates relevant illustrations and descriptions, and provides them to users.Furthermore, it can analyze the user's emotional state and adjust the content and tone of the news content to provide a more personalized experience.

[1392] Hardware and software used

[1393] This system mainly uses the following hardware and software:

[1394] Hardware: Smartphone

[1395] software:

[1396] News API: Acquire external news data

[1397] Generative AI model: News summary generation and related content generation

[1398] Emotion Engine: Analyzing the user's emotional state

[1399] Illustration Generation Engine: Illustration Generation

[1400] System operation overview

[1401] 1. News Data Acquisition: Every morning, the server retrieves the latest news data from the partner news provider. This process is achieved by sending an HTTP request using the news API. The request includes authentication information and necessary parameters, and the retrieved news data is stored in a database on the server.

[1402] 2. Summarizing news data and generating content: The server uses a generative AI model to summarize the acquired news data. Using the text of the news data as input, the generative AI model is instructed to generate a concise summary. After the summary is generated, related illustrations and descriptions are generated based on this data. Illustrations are selected by analyzing keywords contained in the summary data. The descriptions are then automatically created using the generative AI model.

[1403] 3. Emotional state analysis and content adjustment: Using an emotion engine running on the user's device, the system analyzes sensor data from cameras and microphones to detect the user's emotional state. For example, if the user is feeling stressed, the system adjusts the tone of the news content to a calmer tone.

[1404] 4. News content delivery: The server converts the consolidated news content into a format suitable for the application and sends a push notification to the user's smartphone to notify them of new news. The user receives the notification and opens the application to view the summarized news. If a video has been generated, the user can also watch the video within the application.

[1405] Examples of specific examples and prompts

[1406] Examples:

[1407] For example, if a news item about "important policy decisions on environmental protection at an international conference" is retrieved, the generative AI model will generate the following summary:

[1408] "An important policy was decided at an international conference. It is about environmental protection, and countries are working together to reduce greenhouse gas emissions."

[1409] Based on this, the server selects a relevant illustration (e.g., an image of the Earth and a smoking factory) and adds a description like this:

[1410] "This shows our efforts to prevent global warming."

[1411] Example prompt sentence:

[1412] For example, to ask a generative AI model to generate a summary, the prompt might look like this:

[1413] Summarize the following news article:

[1414] "New policies regarding environmental protection were decided at yesterday's international conference. Representatives from various countries gathered together to discuss reducing greenhouse gas emissions."

[1415] An example prompt for content adjustment based on emotional state is:

[1416] Rewrite the following news summary in a tone appropriate for when the user is relaxed:

[1417] "New policies for environmental protection have been decided at an international conference. Countries will work together to reduce greenhouse gas emissions."

[1418] These examples and prompts demonstrate part of a system for providing understandable and personalized news content to users.

[1419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1420] Step 1: Acquire news data

[1421] Every morning, the server retrieves the latest news data from a partner news service. As input, it sends an HTTP request to the news API and stores the retrieved news data in a database. The output is the stored news data.

[1422] Step 2: News data summary

[1423] The server uses a generative AI model to summarize the acquired news data. As input, the text of the news data is fed into the generative AI model, which generates a concise summary. The output is the summarized news data.

[1424] Step 3: Generate illustrations and descriptions

[1425] The server generates related illustrations and descriptions based on summarized news data. It analyzes keywords from the summarized data as input and selects illustrations based on them. It then automatically generates descriptions for the illustrations using a generative AI model. The output is the summarized news data, illustrations, and descriptions.

[1426] Step 4: Analyze emotional state

[1427] The device uses an emotion engine to analyze the user's emotional state. Sensor data from cameras, microphones, etc. is taken as input into the emotion engine, which then analyzes the user's emotions. The output is the analyzed user's emotional state.

[1428] Step 5: Tailor your news content

[1429] The server adjusts the content and tone of news content based on the analyzed user's emotional state. As input, it applies an algorithm that adjusts the tone of news content based on the emotional state. The output is the adjusted news content.

[1430] Step 6: Video Generation (Optional)

[1431] The server generates videos from summarized news data as needed. As input, it provides the summary text and illustrations to the video generation module, and adds audio narration. The output is the generated news video.

[1432] Step 7: Distributing news content

[1433] The server formats the aggregated news content for the application and delivers it to the user's smartphone via push notifications. The input includes the adjusted news content and optional video, which is then converted for visual display to the user. The output is the news content displayed on the user's device.

[1434] Step 8: Review news content

[1435] The user checks the received push notification on the device and opens the application to check the news summary. The input contains news content delivered from the server and the news is displayed through the user interface. The output is the visually displayed news content and its understanding.

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] 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).

[1443] 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.

[1444] 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."

[1445] 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.

[1446] 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).

[1447] 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.

[1448] 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.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] 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.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] The following is further disclosed regarding the above embodiment.

[1458] (Claim 1)

[1459] a means for automatically acquiring news data;

[1460] a means for summarizing the acquired news data;

[1461] means for generating relevant illustrations and explanatory text based on the summarized news data;

[1462] means for delivering the generated summarized news data, illustrations and explanatory text to a user;

[1463] A system including:

[1464] (Claim 2)

[1465] a means for generating video from the summarized news data;

[1466] 10. The system of claim 1, further comprising means for delivering the generated video to a user.

[1467] (Claim 3)

[1468] A means for summarizing news data and generating explanatory text using a generative AI model;

[1469] 10. The system of claim 1, further comprising means for converting the generated narrative into speech.

[1470] "Example 1"

[1471] (Claim 1)

[1472] a means for automatically acquiring news data;

[1473] a means for using a generative AI model to summarize the acquired news data;

[1474] means for generating relevant images and captions based on the summarized news data;

[1475] means for delivering the generated summarized news data, images and descriptions to a user;

[1476] A means for storing the acquired news data in a database;

[1477] a means for restoring the summarized news data to the database;

[1478] means for analyzing keywords in the summary data and selecting images based thereon;

[1479] a means for generating a description using a generative AI model;

[1480] A means for storing the illustration information and the generated description in a database;

[1481] means for formatting the news summary, images and descriptions for the application;

[1482] means for preparing the device for delivery;

[1483] a means for sending a push notification;

[1484] A system including:

[1485] (Claim 2)

[1486] a means for generating video from the summarized news data;

[1487] 10. The system of claim 1, further comprising means for delivering the generated video to a user.

[1488] (Claim 3)

[1489] A means for summarizing news data and generating explanatory text using a generative AI model;

[1490] 10. The system of claim 1, further comprising means for converting the generated narrative into speech.

[1491] "Application Example 1"

[1492] (Claim 1)

[1493] a means for automatically acquiring news data;

[1494] a means for summarizing the acquired news data;

[1495] means for generating relevant illustrations and explanatory text based on the summarized news data;

[1496] means for delivering the generated summarized news data, illustrations and explanatory text to a user;

[1497] A means for notifying a user of the existence of new news using push notifications;

[1498] means for visually displaying the news summary, illustrations and explanatory text on a user terminal;

[1499] A system including:

[1500] (Claim 2)

[1501] a means for generating video from the summarized news data;

[1502] 10. The system of claim 1, further comprising means for delivering the generated video to a user.

[1503] (Claim 3)

[1504] A means for summarizing news data and generating explanatory text using a generative AI model;

[1505] 10. The system of claim 1, further comprising means for converting the generated narrative into speech.

[1506] "Example 2: Combining Emotion Engines"

[1507] (Claim 1)

[1508] a means for automatically acquiring news data;

[1509] A means for summarizing the acquired news data using a generative AI model;

[1510] A method for analyzing keywords in summarized news data and selecting related illustrations;

[1511] A means for generating a description for the selected illustration using a generative AI model;

[1512] a user terminal including an emotion engine that analyzes the user's emotional state;

[1513] a means of adjusting the tone of news content depending on emotional state;

[1514] means for delivering the generated summarized news data, illustrations and explanatory text to a user;

[1515] A system including:

[1516] (Claim 2)

[1517] a means for generating video from the summarized news data;

[1518] means for delivering the generated video to a user;

[1519] Includes means for adjusting the tone of the video depending on the user's emotional state.

[1520] 10. The system of claim 1.

[1521] (Claim 3)

[1522] a means for converting the generated description into speech;

[1523] A way to create videos by combining audio and illustrations,

[1524] Includes means for format conversion of the generated video.

[1525] 10. The system of claim 1.

[1526] "Application example 2 when combining emotion engines"

[1527] (Claim 1)

[1528] a means for automatically acquiring news data;

[1529] a means for summarizing the acquired news data;

[1530] means for generating relevant illustrations and explanatory text based on the summarized news data;

[1531] means for analyzing the emotional state of a user;

[1532] means for adjusting the content and tone of news content based on the analyzed emotional state of the user;

[1533] means for delivering the generated summarized news data, illustrations and explanatory text to a user;

[1534] A system including:

[1535] (Claim 2)

[1536] a means for generating video from the summarized news data;

[1537] means for delivering the generated video to a user;

[1538] Further includes means for adjusting the content and tone of the video according to the user's emotional state.

[1539] 10. The system of claim 1.

[1540] (Claim 3)

[1541] A means for summarizing news data and generating explanatory text using a generative AI model;

[1542] 10. The system of claim 1, further comprising means for converting the generated narrative into speech. [Explanation of symbols]

[1543] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for automatically acquiring news data; a means for summarizing the acquired news data; means for generating relevant illustrations and explanatory text based on the summarized news data; means for delivering the generated summarized news data, illustrations and explanatory text to a user; A system including:

2. a means for generating video from the summarized news data; The system of claim 1 further comprising means for delivering the generated video to a user.

3. A means for summarizing news data and generating explanatory text using a generative AI model; 10. The system of claim 1, further comprising means for converting the generated narrative into speech.

Images