system

Abstract

A system is provided.SOLUTION: Collecting a new message from an SNS account of an idol or a celebrity, inputting the collected message into a AI parsing model for context and emotion parsing, inputting the parsed message into a multilingual translating model for automatic translating, storing a translated result in a database, providing a multilingual translated message to a user terminal, and collecting post content and an action history of the user; This system includes a means for grasping anxiety and worries, a means for proposing recommended contents on the basis of the grasped anxiety and worries, a means for detecting that the activity of a user is stopped when a disaster occurs, a means for providing live video and music to the terminal of the user free of charge when the disaster occurs, and a means for collecting necessary disaster information from various disaster information sources and distributing it to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When a disaster occurs, many people are prone to feeling isolated and anxious, which can lead to mental health problems. Even in such situations, people need access to entertainment and music to provide emotional support. Furthermore, providing information in multiple languages ​​is difficult, and there is a need for a means to provide disaster victims with prompt and appropriate disaster information. There is a need to provide a system that can solve these issues and provide emotional support and necessary information even during a disaster. [Means for solving the problem]

[0005] The present invention is a system including: means for collecting new messages from the social media accounts of idols and celebrities; means for inputting the collected messages into an AI analysis model to analyze the context and emotions; means for inputting the analyzed messages into a multilingual translation model to automatically translate them; means for saving the translation results in a database; means for providing the multilingual translated messages to the user's device; means for collecting the user's posted content and behavioral history to understand their anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing live video and music to the user's device free of charge when a disaster occurs; and means for collecting necessary disaster information from various disaster information sources and delivering it to the user.

[0006] Specifically, it includes a means for analyzing collected post content and behavioral history using an AI analysis model to identify anxieties and worries, a means for selecting optimal content based on the identified anxieties and worries and providing it to users, a means for accessing the API of disaster information sources to collect the latest disaster information, and a means for organizing the collected disaster information and distributing it to users in the affected areas, making it possible to provide entertainment and music that will provide psychological support even when a disaster occurs, as well as the distribution of appropriate disaster information.

[0007] "SNS messages" refer to posts and comments made public by idols and celebrities on social networking services.

[0008] "Multilingual translation" is the process of converting text written in one language into multiple other languages.

[0009] An "AI analysis model" is an algorithm that uses artificial intelligence to analyze data and extract specific patterns, emotions, and context.

[0010] "Disaster information" refers to information about natural disasters such as earthquakes, typhoons, and volcanic eruptions, as well as related evacuation instructions and damage status.

[0011] "Content" refers to information resources such as video, music, articles, etc. that are provided as entertainment or media.

[0012] "Anxiety and worries" refer to concerns that users have in their minds or problems that cause them mental stress.

[0013] A "terminal" is a device used by a user, such as a smartphone, tablet, or PC.

[0014] A "database" is a system that stores collected data in an organized manner so that it can be searched and referenced later.

[0015] "Live footage" refers to video that is distributed in real time, especially live broadcasts of concerts and events featuring idols and celebrities.

[0016] "API" stands for Application Programming Interface, an interface that allows different software programs to communicate with each other and utilize their functions.

[0017] "Disaster area" refers to a place or area that has been affected by a disaster. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[0040] This system is implemented in particular by the following means:

[0041] 1. Collecting social media messages

[0042] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[0043] 2. AI-powered message analysis and translation

[0044] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, for example, assigning emotional labels such as positive, negative, or neutral.

[0045] The server inputs the analyzed message into a multilingual translation model and performs automatic translation, translating Japanese messages into English, Chinese, Korean, etc.

[0046] 3. Providing multilingual translated messages

[0047] The user requests a social media message from their favorite person using an application on their device.

[0048] The server retrieves the corresponding translated message from the database in response to the user's request and transmits it to the terminal.

[0049] The terminal displays the transmitted translated message on the screen and provides it to the user.

[0050] 4. Understanding fans' concerns and worries and proposing content

[0051] A user posts on social media or within an application, for example, "I've been feeling a lot of anxiety lately."

[0052] The terminal collects the content posted by the user and transmits it to the server.

[0053] The server inputs the user's submitted post into an AI analysis model to understand their anxieties and worries.

[0054] The server selects the most suitable song or video based on the user's anxieties and worries and sends it to the device.

[0055] The device will display the selected song or video for playback.

[0056] 5. Providing free content during disasters

[0057] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[0058] The server provides live video and music to its users free of charge.

[0059] The terminal displays the received free live video and music on the screen, making them available to the user.

[0060] 6. Rapid collection and distribution of disaster information

[0061] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[0062] The server will then properly organize the collected disaster information and deliver relevant information to users.

[0063] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0064] Specific examples

[0065] Example 1: Collecting and translating social media messages

[0066] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[0067] Example 2: Understanding Anxiety and Proposing Content

[0068] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an AI analysis model and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[0069] As described above, the system of the present invention allows fans to receive emotional support even in the event of a disaster, and can quickly provide necessary information.

[0070] The processing flow will be explained below.

[0071] SNS message collection

[0072] Step 1:

[0073] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[0074] Step 2:

[0075] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[0076] Step 3:

[0077] The server stores the retrieved new messages in a database.

[0078] AI-powered message analysis and translation

[0079] Step 1:

[0080] The server reads the collected SNS messages from the database.

[0081] Step 2:

[0082] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[0083] Step 3:

[0084] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[0085] Step 4:

[0086] The server stores the translated messages in a database.

[0087] Providing multilingual translated messages

[0088] Step 1:

[0089] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[0090] Step 2:

[0091] The terminal sends a request to the server.

[0092] Step 3:

[0093] The server retrieves the corresponding translated message from the database.

[0094] Step 4:

[0095] The server transmits the obtained translated message to the terminal.

[0096] Step 5:

[0097] The terminal displays the received translated message on the screen.

[0098] Understanding fans' concerns and worries and proposing content

[0099] Step 1:

[0100] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[0101] Step 2:

[0102] The terminal collects the content posted by the user and transmits it to the server.

[0103] Step 3:

[0104] The server inputs the posted content into an AI analysis model to analyze and identify the user's anxieties and worries.

[0105] Step 4:

[0106] The server selects from a database the most appropriate content to address the user's anxieties and worries (for example, fun videos or uplifting music).

[0107] Step 5:

[0108] The server transmits the selected content to the terminal.

[0109] Step 6:

[0110] The terminal displays and plays the selected content on the screen.

[0111] Providing free content during disasters

[0112] Step 1:

[0113] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[0114] Step 2:

[0115] The server creates a list of users who have lost contact when a disaster occurs.

[0116] Step 3:

[0117] The server selects live videos and music to offer to the user free of charge.

[0118] Step 4:

[0119] The server transmits the selected free content to the terminal.

[0120] Step 5:

[0121] The terminal is enabled to display and play the received free content on the screen.

[0122] Rapid collection and distribution of disaster information

[0123] Step 1:

[0124] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[0125] Step 2:

[0126] The server will then properly organize and classify the collected disaster information.

[0127] Step 3:

[0128] The server distributes organized disaster information to users in the affected areas.

[0129] Step 4:

[0130] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0131] Example 1

[0132] 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."

[0133] In the event of a disaster, there are currently insufficient methods to provide a safe environment where fans can feel free to avoid feeling isolated or anxious. Furthermore, since it is difficult to understand the content of social media messages in foreign languages ​​and there are limited ways to quickly obtain necessary disaster information, it is necessary to create an environment where fans can act with peace of mind.

[0134] 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.

[0135] In this invention, the server includes a means for collecting new messages from idols' and celebrities' posting accounts, a means for inputting the collected messages into a generative AI model and analyzing the context and sentiment, and a means for inputting the analyzed messages into a multilingual translation model for automatic translation. This allows fans to understand idols' and celebrities' SNS messages in real time even in the event of a disaster, providing an environment where they can feel safe and secure without feeling lonely or anxious. Furthermore, by providing a means for collecting necessary disaster information from various disaster information sources and distributing it to users, it is possible to quickly provide necessary disaster information and create an environment where users can act with peace of mind.

[0136] "Idols and celebrities" are people who are widely recognized by the public and famous for their entertainment or social activities.

[0137] "Posting Account" means an account on a social networking site or other social media platform used by an individual or entity to post messages or content.

[0138] A "generative AI model" is an algorithm or program that uses artificial intelligence techniques to analyze data and understand context and emotions.

[0139] A "multilingual translation model" is an artificial intelligence model that performs automatic translation between different languages ​​and is used to convert content from one language to another.

[0140] A "recording medium" refers to a device or system for storing data or information, and includes databases, hard disks, flash memory, etc.

[0141] A "communication terminal" is a device that a user uses to send and receive information, and includes smartphones, tablets, computers, etc.

[0142] "Operation history" is a record of the operations performed by a user when using the system, and includes login history, click history, posting history, etc.

[0143] "Recommended content" refers to appropriate and optimal content such as music, video, and text selected by the system based on the user's behavior and status.

[0144] "Disaster information sources" are channels for disseminating disaster-related information provided by government agencies, public organizations, and reliable private organizations.

[0145] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0146] This invention relates to a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[0147] Hardware and software used

[0148] 1. Server

[0149] Use the API key to get SNS messages.

[0150] It uses generative AI models such as the Hugging Face sentiment analysis model and the Google Translate API.

[0151] A database (e.g., MySQL, PostgreSQL) is used as the recording medium for storing data.

[0152] 2. Terminal

[0153] Use a smartphone, tablet or computer to run the application.

[0154] 3. Users

[0155] A user of a system who requests and receives information through an application.

[0156] Data processing and calculation

[0157] 1. Collecting social media messages

[0158] The server authenticates to the SNS API using the API key and secret, periodically collects new messages, and stores them in a database.

[0159] 2. Message Analysis and Translation

[0160] The server inputs the collected messages into a generative AI model to analyze the context and sentiment. Based on the analysis results, the messages are input into a multilingual translation model to translate them from Japanese to English, Chinese, Korean, etc.

[0161] 3. Provision to Users

[0162] When a user requests a translation message through an application, the server retrieves the corresponding message from the database and sends it to the terminal, which then displays the translation message.

[0163] 4. Understanding anxieties and worries and proposing content

[0164] When a user enters their post content into the application, the device sends it to the server, which then uses a generative AI model to analyze their anxieties and worries. Based on the analysis results, the server selects the most appropriate content (e.g., uplifting songs or videos) and sends it to the device.

[0165] 5. Disaster response

[0166] The server detects when a user's regular activity has stopped and provides free live video and music.

[0167] The terminal receives and displays this information, allowing the user to use it with peace of mind.

[0168] 6. Collection and distribution of disaster information

[0169] The server accesses APIs for government and various disaster information sources, collects new disaster information, organizes it, and distributes it to users.

[0170] The terminal displays the received information on the screen, allowing the user to respond promptly.

[0171] Specific examples

[0172] Example 1: Collecting and translating social media messages

[0173] The server collects new messages from idols' social media accounts via API. The collected messages are analyzed for emotion using the Hugging Face model and then translated into multiple languages ​​using the Google Translate API. The translated results are stored in a database, and when a user makes a request through the app, the server retrieves the corresponding message, sends it to the device, and displays it.

[0174] Example 2: Understanding Anxiety and Proposing Content

[0175] When a user posts in the app, "I've been feeling more and more anxious lately," the device sends the post to the server, where it is analyzed by the generative AI model. The server then selects upbeat songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content, which the user can view or play.

[0176] Prompt Sentence Examples

[0177] "Get the latest tweets from Twitter, perform sentiment analysis, and translate them from Japanese to English."

[0178] "Please use AI to analyze user posts and recommend appropriate songs and videos."

[0179] Through the specific processing described above, the present invention is a system that can provide an environment in which fans can feel safe even in the event of a disaster and quickly deliver necessary information.

[0180] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0181] Step 1:

[0182] The server uses the social networking service's API to collect new messages from idol and celebrity posting accounts.

[0183] Input: SNS API key and secret, and the ID of the specific account.

[0184] Specific operation: The server authenticates with SNS using the API key and secret and sends a request to the "statuses / user_timeline" endpoint.

[0185] Data processing: Message data obtained from SNS is received in JSON format.

[0186] Output: The retrieved SNS message.

[0187] Step 2:

[0188] The server inputs the collected social media messages into a generative AI model and analyzes the context and emotions.

[0189] Input: Collected SNS messages.

[0190] What it does: The server analyzes the message using Hugging Face's sentiment analysis model.

[0191] Data computation: AI models analyze the context and sentiment of messages and assign sentiment labels as positive, negative, or neutral.

[0192] Output: Social media messages with sentiment labels.

[0193] Step 3:

[0194] The server inputs the emotion-analyzed message into a multilingual translation model for automatic translation.

[0195] Input: SNS messages with sentiment labels.

[0196] Specific operation: The server uses the Google Translate API to translate the message from Japanese to other languages ​​(English, Chinese, Korean, etc.).

[0197] Data processing: The model translates the text and generates messages in multiple languages.

[0198] Output: Multilingual translated message.

[0199] Step 4:

[0200] The server stores the translation results in a recording medium.

[0201] Input: A message translated into multiple languages.

[0202] Specific operation: The server stores the translation results in a database (e.g., MySQL, PostgreSQL).

[0203] Data processing: Store the message ID and the corresponding translation text in the appropriate table.

[0204] Output: The translated message stored in the database.

[0205] Step 5:

[0206] The user requests a social media message from their favorite person using an application on their device.

[0207] Input: User request (e.g. Idol A's recent posts).

[0208] What happens: A user clicks a button in the app, and a request is sent from the device to the server.

[0209] Data Calculation: The server retrieves the corresponding translation message from the database upon request.

[0210] Output: The translated message is sent from the server to the device.

[0211] Step 6:

[0212] The terminal displays the received translated message on the screen and provides it to the user.

[0213] Input: The translated message sent from the server.

[0214] Specific operation: The terminal displays the received message on the application screen.

[0215] Output: The translated message that is visible to the user.

[0216] Step 7:

[0217] Users post their concerns and worries within the application.

[0218] Input: A text post typed by the user (e.g., "I've been worrying a lot lately").

[0219] Specific operation: The user clicks the post button, and the post content is sent from the device to the server.

[0220] Output: The post sent to the server.

[0221] Step 8:

[0222] The server inputs the submitted post content into a generative AI model to understand the person's anxieties and worries.

[0223] Input: User submissions.

[0224] What happens: The server parses the post using a natural language processing model (e.g., the BERT model).

[0225] Data calculation: The model extracts keywords related to anxiety and worries from the content of the post.

[0226] Output: Analysis results identifying anxieties and worries.

[0227] Step 9:

[0228] The server selects the most appropriate content based on the user's anxieties and worries and sends it to the device.

[0229] Input: Analysis results of anxiety and worries.

[0230] Specific operation: The server searches the database for upbeat songs and videos and selects the most suitable content.

[0231] Data processing: Obtain information about the selected content and send it to the device.

[0232] Output: The selected content is sent to the device.

[0233] Step 10:

[0234] The terminal displays the selected content on the screen for the user to view or play.

[0235] Input: Content information sent from the server.

[0236] Specific operation: The terminal displays the received content on the application screen and provides a play button.

[0237] Output: The content that users can view and play.

[0238] Step 11:

[0239] The server accesses the API of the disaster information source to collect the latest disaster information.

[0240] Input: API key and endpoint of disaster information source.

[0241] Specific operation: The server sends a request to the API to obtain new disaster information.

[0242] Data processing: Receive disaster information in JSON format and organize it appropriately.

[0243] Output: Retrieved and organized disaster information.

[0244] Step 12:

[0245] The server distributes the organized disaster information to users.

[0246] Input: Organized disaster information.

[0247] Specific operation: The server identifies users in the affected area and registers them in a distribution list for disaster information.

[0248] Data Processing: Generate a unique notification message and send it to the corresponding user.

[0249] Output: Disaster information notification sent to the user's device.

[0250] Step 13:

[0251] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0252] Input: Disaster information notification sent from the server.

[0253] Specific operation: The terminal displays the received information on the application screen and notifies the user.

[0254] Output: A screen displaying disaster information and user response preparation.

[0255] (Application example 1)

[0256] 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."

[0257] Many fans tend to feel lonely and anxious during disasters, and there is a need for a system to alleviate this psychological burden. However, many current systems are unable to respond quickly and appropriately due to language barriers and a lack of information. Furthermore, they do not adequately provide content tailored to the psychological state of fans, and are unable to provide sufficient emotional support. This invention aims to utilize AI technology to translate multiple languages, grasp psychological states, and quickly distribute information during disasters, thereby providing an environment where fans can feel safe.

[0258] 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.

[0259] In this invention, the server includes: means for collecting new messages from the social media accounts of idols and celebrities; means for inputting the collected messages into an automatic analysis model and analyzing the context and sentiment; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing the multilingual translated messages to a terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing free viewing content to a terminal when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and delivering it to users; means for using a generative AI model to provide recommended content; and means for creating prompts for the generative AI model. This allows fans to feel safe and secure even during disasters without worrying about information.

[0260] "Idols and celebrities" generally refer to individuals who have a large number of fans and social influence.

[0261] "Social media account" refers to an account held by an individual or company within a social networking service for disseminating information, whether public or private.

[0262] "New messages" refers to the latest posts, comments, tweets, and other information from social media accounts.

[0263] An "automatic analysis model" refers to an algorithm that uses machine learning and natural language processing technology to analyze input text data and parse its content and emotions.

[0264] "Context and sentiment" refers to the meaning of the information contained within a text message and its emotional tenor, such as positive, negative, or neutral.

[0265] "Multilingual translation model" refers to an algorithm that uses artificial intelligence technology to automatically translate from a specific language to another language.

[0266] "Database" refers to a system for storing and managing data in a structured format.

[0267] A "terminal" refers to a device with communication capabilities that can be operated by a user, such as a smartphone, tablet, or personal computer.

[0268] "User posts and behavioral history" refers to data such as text, images, and activity logs posted by users on social media or within applications.

[0269] "Understanding anxieties and worries" refers to analyzing the content posted by users and their behavioral history to identify their mental state.

[0270] "Recommended content" refers to information such as songs, videos, articles, etc. that are deemed most suitable based on the user's emotions and interests.

[0271] "Detecting a cessation of user activity when a disaster occurs" means that the system detects a sudden decrease in the frequency of normal logins and posts.

[0272] "Providing viewing content free of charge" refers to providing entertainment content such as live footage and music without charging fees in the event of a disaster.

[0273] "Disaster information sources" refers to up-to-date information about disasters provided by government agencies and disaster information services.

[0274] A "generative AI model" refers to an artificial intelligence system that uses machine learning and deep learning to generate information and content in response to user requests.

[0275] A "prompt sentence" refers to an input sentence that causes a generative AI model to perform a specific task.

[0276] The present invention provides a system that reduces the loneliness and anxiety felt by users when a disaster occurs, and provides a safe environment. Specific embodiments of the system are described below.

[0277] System Configuration

[0278] This system consists of the following elements:

[0279] server

[0280] Terminal

[0281] User

[0282] Hardware and software used

[0283] API: Twitter API, YouTube API

[0284] AI analysis model: Google Cloud Natural Language API

[0285] Multilingual Translation: Google Translation API

[0286] Database: Firebase Realtime Database

[0287] Frontend: React Native for mobile app

[0288] System processing overview

[0289] 1. Collecting social media messages

[0290] The server uses the Twitter API to periodically collect new messages from the social media accounts of idols and celebrities.

[0291] 2. Message Analysis

[0292] The server analyzes the collected messages using the Google Cloud Natural Language API to analyze context and sentiment.

[0293] 3. Multilingual Translation

[0294] The server automatically translates the parsed messages into multiple languages ​​using the Google Translation API, and the translated messages are stored in the Firebase Realtime Database.

[0295] 4. Provision to Users

[0296] The server provides the multilingual translated message to the terminal when requested by the user through the terminal, and the terminal displays the message to the user.

[0297] 5. Understanding anxieties and worries and proposing content

[0298] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries and selects recommended content based on that information. The selected content is then provided to the user via the device.

[0299] 6. Providing free content during disasters

[0300] The server detects when a user's regular activities (logins and posts) are interrupted during a disaster. If this is detected, the server provides free viewing content (live video and music) to the device, allowing the user to use it.

[0301] 7. Collection and distribution of disaster information

[0302] The server accesses the APIs of various disaster information sources to collect the latest disaster information, which is then organized and quickly distributed to users in the affected areas.

[0303] Specific examples

[0304] Collecting and translating social media messages

[0305] The server collects new tweets from idols' and celebrities' social media accounts and assigns them sentiment labels. The server then translates the messages into multiple languages ​​(English, Chinese, Korean, etc.) and stores them in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message and sends it to the device. The device then displays the translated message.

[0306] Providing content based on user emotions

[0307] When a user posts "I've been feeling down lately" from their device, the device sends the post to a server. The server uses an AI analysis model to analyze the post and identify the anxiety. The server then selects uplifting songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content for the user to view and play.

[0308] Prompt Sentence Examples

[0309] "Please suggest appropriate content (uplifting songs or videos) in response to the recent user post, 'I've been feeling down lately.'"

[0310] The above is an embodiment of the present invention. This system allows fans to feel at ease even in the event of a disaster and to quickly obtain the information they need.

[0311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0312] Step 1:

[0313] Collecting SNS messages

[0314] The server periodically collects new messages from idols and celebrities using the APIs of their social media accounts. Specifically, it uses the Twitter API to retrieve the latest tweets from the accounts. The input is the response from the social media API, and the output is the retrieved messages.

[0315] Step 2:

[0316] Message Parsing

[0317] The server inputs the collected messages into the Google Cloud Natural Language API to analyze the context and sentiment. This process determines whether the message contains positive, negative, or neutral sentiment. The input is the collected messages, and the output is messages with sentiment labels attached. Specifically, the server sends messages to the Natural Language API and receives sentiment labels in response.

[0318] Step 3:

[0319] Multilingual Translation

[0320] The server uses the Google Translation API to translate the analyzed message into multiple languages. The target languages ​​for translation include English, Chinese, and Korean. The input is a message with an emotion label attached, and the output is a multilingual translation of the message. Specifically, the server sends the message to the Translation API and receives the translated text in each language.

[0321] Step 4:

[0322] Saving to a database

[0323] The server stores the translated messages in the Firebase Realtime Database. The input is the multilingual translated message, and the output is a data entry stored in the database. Specifically, the translated message is written to the database and a new data entry is created.

[0324] Step 5:

[0325] Provision to users

[0326] When a user requests a translation message through a terminal, the server retrieves the corresponding translation message from the database and sends it to the terminal. The input is the user request, and the output is the translation message. Specifically, the server queries the database based on the request and sends the result to the terminal.

[0327] Step 6:

[0328] Understanding anxieties and worries

[0329] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries. The input is the user's posts and behavioral history, and the output is the identified anxieties and worries. Specifically, the collected data is sent to the server, where it is analyzed.

[0330] Step 7:

[0331] Content Suggestion

[0332] The server selects recommended content, such as uplifting music or videos, based on the analyzed anxieties and worries. The selected content is provided to the user via the device. The input is the identified anxieties and worries, and the output is the selected content. Specifically, the server creates a prompt for the generative AI model, inputs the analysis results, and generates appropriate content.

[0333] Step 8:

[0334] Disaster activity detection

[0335] The server detects when a user's regular activity (logins or posts) has ceased. The input is the login and post activity history, and the output is the result of the detection of the cessation of activity. Specifically, it periodically checks the user's activity log and confirms the cessation of activity if there has been no activity for a certain period of time.

[0336] Step 9:

[0337] Providing free content during disasters

[0338] The server provides free content to inactive users. The input is the result of the inactivity detection, and the output is free content. Specifically, the server provides live video and music data via a generative AI model.

[0339] Step 10:

[0340] Collection and distribution of disaster information

[0341] The server accesses the APIs of various disaster information sources, collects and organizes the latest disaster information, and then distributes the organized information to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information. Specifically, the server obtains information from the APIs and notifies users of the organized information in real time.

[0342] The above are the specific processing steps for carrying out the present invention. The input, data processing, and output performed at each step have been described in detail, and specific operations have been shown.

[0343] 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.

[0344] This invention combines an emotion engine with a system that provides a safe environment for fans in the event of a disaster, preventing them from feeling lonely or anxious. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and in addition to translating them into multiple languages, it also has an emotion engine that recognizes the user's emotions, aiming to provide more appropriate content.

[0345] This system is implemented by the following means:

[0346] 1. Collecting social media messages

[0347] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[0348] 2. AI-powered message analysis and translation

[0349] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, generating emotion labels such as "positive," "negative," and "neutral."

[0350] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[0351] The server stores the translated messages in a database.

[0352] 3. Providing multilingual translated messages

[0353] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[0354] The terminal sends a request to the server.

[0355] The server retrieves the corresponding translated message from the database.

[0356] The server transmits the obtained translated message to the terminal.

[0357] The terminal displays the received translated message on the screen.

[0358] 4. User Emotion Recognition by Emotion Engine

[0359] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[0360] The terminal collects the content posted by the user and transmits it to the server.

[0361] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[0362] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[0363] 5. Understanding anxieties and worries and proposing content

[0364] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[0365] The server transmits the selected content to the terminal.

[0366] The terminal displays and plays the selected content on the screen.

[0367] 6. Providing free content during disasters

[0368] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[0369] The server selects live videos and music to provide to the user free of charge.

[0370] The server transmits the selected free content to the terminal.

[0371] The terminal is enabled to display and play the received free content on the screen.

[0372] 7. Rapid collection and distribution of disaster information

[0373] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[0374] The server will then properly organize and classify the collected disaster information.

[0375] The server distributes organized disaster information to users in the affected areas.

[0376] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0377] Specific examples

[0378] Example 1: Collecting and translating social media messages

[0379] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[0380] Example 2: Understanding Anxiety and Proposing Content

[0381] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[0382] Example 3: Using an Emotion Engine

[0383] If a user posts a voice message within the app and the message is negative, such as "I'm so tired," the device collects the voice data and sends it to the server. The server uses an emotion engine to analyze the voice data and determine that the user is tired. The server selects relaxing music or an uplifting message to soothe the user and sends it to the device. The device then displays and plays this content.

[0384] As described above, by combining emotion engines, it is possible to more accurately grasp the user's emotions and respond to them individually, thereby providing psychological support even in the event of a disaster.

[0385] The processing flow will be explained below.

[0386] Processing flow of the system including the emotion engine

[0387] SNS message collection

[0388] Step 1:

[0389] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[0390] Step 2:

[0391] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[0392] Step 3:

[0393] The server stores the retrieved new messages in a database.

[0394] AI-powered message analysis and translation

[0395] Step 1:

[0396] The server reads the collected SNS messages from the database.

[0397] Step 2:

[0398] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[0399] Step 3:

[0400] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[0401] Step 4:

[0402] The server stores the translated messages in a database.

[0403] Providing multilingual translated messages

[0404] Step 1:

[0405] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[0406] Step 2:

[0407] The terminal sends a request to the server.

[0408] Step 3:

[0409] The server retrieves the corresponding translated message from the database.

[0410] Step 4:

[0411] The server transmits the obtained translated message to the terminal.

[0412] Step 5:

[0413] The terminal displays the received translated message on the screen.

[0414] Recognizing user emotions with an emotion engine

[0415] Step 1:

[0416] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[0417] Step 2:

[0418] The terminal collects the content posted by the user and transmits it to the server.

[0419] Step 3:

[0420] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[0421] Step 4:

[0422] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[0423] Understanding anxieties and worries and proposing content

[0424] Step 1:

[0425] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[0426] Step 2:

[0427] The server transmits the selected content to the terminal.

[0428] Step 3:

[0429] The terminal displays and plays the selected content on the screen.

[0430] Providing free content during disasters

[0431] Step 1:

[0432] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[0433] Step 2:

[0434] The server creates a list of users who have lost contact when a disaster occurs.

[0435] Step 3:

[0436] The server selects live videos and music to offer to the user free of charge.

[0437] Step 4:

[0438] The server transmits the selected free content to the terminal.

[0439] Step 5:

[0440] The terminal is enabled to display and play the received free content on the screen.

[0441] Rapid collection and distribution of disaster information

[0442] Step 1:

[0443] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[0444] Step 2:

[0445] The server will then properly organize and classify the collected disaster information.

[0446] Step 3:

[0447] The server distributes organized disaster information to users in the affected areas.

[0448] Step 4:

[0449] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0450] Example 2

[0451] 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."

[0452] In recent years, with the spread of social networking services (SNS), messages from idols and celebrities have become an important source of information for users. However, these messages are not available in multiple languages ​​and lack emotional support. Furthermore, in the event of a disaster, users are likely to face an environment where they feel anxious and lonely. In such situations, there is a need for a system that can provide users with psychological support and prompt information.

[0453] 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 collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing multilingual translated messages to users' communication devices; means for collecting user posts and behavioral history to understand their anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for analyzing users' emotions using an emotion engine; means for detecting a cessation of user activity when a disaster occurs; means for providing live video and music to users' communication devices free of charge when a disaster occurs; and means for collecting necessary disaster information from various disaster information sources and distributing it to users. This allows users to understand messages from idols and celebrities in multiple languages, reducing anxiety and loneliness in everyday life and during disasters, and receiving emotional support.

[0454] "Idols and celebrities" refer to individuals who are recognized by many people and have social influence.

[0455] A "social networking service account" is an authenticated user profile used by an individual or organization to disseminate and share information on a social networking service.

[0456] An "artificial intelligence analysis model" refers to a program or system that uses AI technology to analyze text and data and understand its context and emotions.

[0457] A "multilingual translation model" is a program or system for automatically translating text into different languages.

[0458] A "database" is an information system that efficiently stores large amounts of data and allows them to be quickly retrieved when needed.

[0459] "Communication devices" are devices such as smartphones, tablets, and personal computers that are used to send and receive information via the Internet or other communication networks.

[0460] "Posted content" refers to all information such as text, images, audio, and video that users share through social media or applications.

[0461] "Behavioral history" refers to a record of all actions a user takes within a social networking site or application, such as posting messages, viewing content, clicking "like," etc.

[0462] An "emotion engine" is a system that automatically analyzes a user's emotions from text, voice, images, etc. and outputs the results.

[0463] "Disaster information sources" refer to disaster-related information provided by government agencies, public institutions, and other reliable organizations.

[0464] An "application program interface (API)" refers to a defined method or interface that allows software to interact with other software and utilize its functionality.

[0465] The present invention combines an emotion engine with a system that provides a safe environment in the event of a disaster, preventing fans from feeling lonely or anxious. A specific embodiment of this system is described below.

[0466] Overall system overview

[0467] The system operates around a server, devices, and users. The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, and analyzes their context and sentiment using an artificial intelligence analysis model. The server also inputs the messages into a multilingual translation model for automatic translation, and stores the translation results in a database. Users can then view these messages using a dedicated application.

[0468] Hardware and Software Configuration

[0469] server

[0470] The server is hardware with a powerful processor and large memory capacity, and runs the following software components:

[0471] SNS Collection Module: Collects messages using the SNS API.

[0472] Artificial intelligence analysis models: For example, using the BERT model to analyze the context and sentiment of messages.

[0473] Multilingual translation model: Translate messages into multiple languages ​​using Google Translate API and DeepL Translator.

[0474] Database system: stores the collected and analyzed messages.

[0475] Terminal

[0476] The device is a user's communication device such as a smartphone, tablet, or PC. It has a dedicated application installed and provides the following functions:

[0477] Message request function: Users request translation messages.

[0478] Message display function: Displays the translated message received from the server.

[0479] Posting function: Sends user posts to the server.

[0480] User operation procedure

[0481] Users can launch the dedicated application and read the latest social media messages of idols and celebrities, translating them. Users can also post about their feelings and situations in the application, and the system will analyze the user's emotions and suggest appropriate content.

[0482] Specific examples

[0483] Example 1: Collecting and translating social media messages

[0484] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an artificial intelligence analysis model (e.g., the BERT model) and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[0485] Example 2: Understanding Anxiety and Proposing Content

[0486] A user posts in the app, "I've been feeling a lot of anxiety lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease the anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[0487] Prompt Sentence Examples

[0488] Request a translation of Idol A's latest tweet.

[0489] There's been a lot of anxiety lately, so please provide some uplifting content.

[0490] Analyze my voice messages to understand my emotional state and recommend relaxing music.

[0491] In this way, by combining AI and an emotion engine, the system of the present invention can accurately grasp the user's emotions and provide psychological support even in the event of a disaster.

[0492] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0493] Specific processing steps of the program

[0494] Step 1: Collect SNS messages

[0495] The server collects new messages from the social networking service accounts of idols and celebrities.

[0496] Input: Your SNS API key and account information.

[0497] Processing: The server retrieves the message using an API, for example calling the Twitter API GET / 2 / tweets endpoint to get the latest tweets.

[0498] Output: A new SNS message (e.g., tweet data in JSON format).

[0499] Specific operation: The server periodically calls the Twitter API, retrieves the latest messages, and temporarily stores them.

[0500] Step 2: Analyzing the message with AI

[0501] The server inputs the collected SNS messages into an AI analysis model and assigns emotion labels.

[0502] Input: Collected SNS messages (e.g. tweet data in JSON format).

[0503] Processing: The message is fed into an AI analysis model, such as the BERT model, to analyze the context and sentiment, which then generates a sentiment label, such as "positive," "negative," or "neutral."

[0504] Output: Message data with sentiment labels.

[0505] Specific operation: The server calls the BERT model to analyze SNS messages and assign sentiment labels.

[0506] Step 3: Translate your messages into multiple languages

[0507] The server inputs the parsed message into a multilingual translation model and translates it into the specified language.

[0508] Input: Message data with emotion labels.

[0509] Processing: Input the message into Google Translate API or DeepL Translator and translate it into the specified language, such as English, Chinese, or Korean.

[0510] Output: Multilingual translated message data.

[0511] Specific operation: The server uses the translation API to translate the message into a different language and obtains the translation result.

[0512] Step 4: Save the translated message

[0513] The server stores the translated messages in a database.

[0514] Input: Multilingual translated message data.

[0515] Processing: Store the translated messages in a database system.

[0516] Output: The translated message stored in the database.

[0517] What happens: The server connects to the database and executes a SQL query to store the translated message.

[0518] Step 5: Providing multilingual translated messages

[0519] The user launches the app from their communication device and requests a translation of a social media message.

[0520] Input: User request (e.g. "Show me the translation of idol A's latest tweet in English").

[0521] Process: The device sends a request to the server, which retrieves the corresponding translation message from the database and sends it to the device.

[0522] Output: The translated message that will be displayed on the user's communication device.

[0523] What happens: The server executes a database query to retrieve the message and sends it to the device, which then displays the received translated message on its screen.

[0524] Step 6: Recognizing user emotions with the emotion engine

[0525] Users post about their feelings and situations on social media or within apps.

[0526] Input: User post (e.g., "I've been feeling a lot of anxiety lately").

[0527] Processing: The device collects the posted content and sends it to the server. The server inputs the posted content into the emotion engine and analyzes the user's emotions.

[0528] Output: Post data with sentiment labels.

[0529] Specific operation: The server calls the emotion engine, analyzes the user's post content, and assigns emotion labels.

[0530] Step 7: Understanding anxieties and worries and proposing content

[0531] The server selects the most suitable content based on the analysis results and provides it to the user.

[0532] Input: Post data with sentiment labels.

[0533] Processing: The server selects the most suitable content (e.g., fun videos or uplifting music) from the database.

[0534] Output: Content sent to the user's communication device.

[0535] Specific operations: The server executes a database query to select content and sends it to the device, which then displays or plays the received content.

[0536] Step 8: Providing free content during disasters

[0537] The server detects that user activity has ceased when a disaster occurs.

[0538] Input: User activity log (e.g. login and posting frequency).

[0539] Processing: The server analyzes disaster information and user activity logs, receives notifications when activity stops, selects free content, and sends it to the device.

[0540] Output: Free content sent to the user's communication device.

[0541] Specific operation: The server monitors the user's activity based on disaster information, and if the activity stops, it selects free live video or music and sends it to the device.

[0542] Step 9: Rapid collection and distribution of disaster information

[0543] The server accesses the API of the disaster information source to collect new disaster information.

[0544] Input: API key and endpoint information of the disaster information source.

[0545] Processing: The server calls the API to obtain the latest disaster information, organizes and categorizes it appropriately, and delivers the organized information to users.

[0546] Output: Disaster information delivered to users' communication devices.

[0547] Specific operation: The server collects disaster information from public institution APIs and distributes organized information to users in the affected areas. The device notifies the user of the received information.

[0548] The above is the specific processing flow of the system.

[0549] (Application example 2)

[0550] 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."

[0551] When a disaster occurs, providing a safe environment where users can feel free to avoid feeling lonely or anxious is an important issue. While existing systems provide disaster information and multilingual translation, they lack the ability to grasp the user's emotional state and provide appropriate content accordingly. Furthermore, there are insufficient effective support methods that can provide a sense of security free of charge when a disaster occurs. Therefore, a system that can respond to users' emotions and provide a sense of security in real time is needed.

[0552] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in an information recording device; means for providing the multilingual translated messages to a user's terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting optimal content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing video content and music content to a user's terminal free of charge when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and distributing it to users; and means for analyzing users' emotions when a disaster occurs and selecting and providing content that provides a sense of security. This makes it possible to create an environment in which users can feel safe even during a disaster by understanding the user's emotional state and providing appropriate content.

[0553] "Idol" refers to a talent or artist who is popular in a particular entertainment field.

[0554] A "celebrity" is someone who is widely known and active in fields such as entertainment, sports, politics, or business.

[0555] "Social networking service" refers to a web service or platform that enables users to communicate with other users online.

[0556] "Message" means information transmitted between Users in the form of text, images, audio, video, etc.

[0557] "Artificial intelligence analytical model" refers to the algorithms and programs used to analyze collected data and understand its context and sentiment.

[0558] A "multilingual translation model" refers to an algorithm or program that automatically translates a sentence in one language into another language.

[0559] "Information recording device" refers to a computer or medium for temporarily or permanently storing data.

[0560] "User" refers to an individual who uses a system or application.

[0561] "Device" refers to a computing device such as a smartphone, tablet, or PC.

[0562] "Posted content" refers to information such as text, images, audio, and video that users post on social media or applications.

[0563] "Behavioral history" refers to a record of a user's actions within a system or application.

[0564] "Anxiety" refers to feelings of worry and fear that users have about their safety and the future.

[0565] "Problems" refers to the problems and distress that users face.

[0566] "Content" refers to any form of information or entertainment (e.g., music, video, text) provided to a user.

[0567] "Means for detecting inactivity" refers to a system that detects when a user's normal activity has ceased for a period of time.

[0568] "Video content" refers to content that includes visual information such as videos and live footage.

[0569] "Music Content" refers to content that contains auditory information, such as music tracks or live music.

[0570] "Disaster information sources" refer to data sources that provide information about disasters, such as those provided by government agencies and disaster response organizations.

[0571] An "application program interface (API)" refers to the definitions and protocols that allow software to communicate and interact with each other.

[0572] "Means for analyzing emotions" refers to systems or methods that analyze users' posted content and behavioral history to identify their emotional state.

[0573] In this invention, the following system is constructed to provide a safe environment for users even in the event of a disaster. The system is composed of a server, a user terminal, and related software modules.

[0574] Server Roles

[0575] The server has the following functions:

[0576] 1. Message collection and analysis:

[0577] The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, using the SNS API.

[0578] The collected messages are then fed into an artificial intelligence (AI) analysis model to analyze their context and sentiment, using natural language processing toolkits and machine learning models (e.g., Google BERT).

[0579] 2. Multilingual translation and storage:

[0580] The parsed message is input into a multilingual translation model and automatically translated into other languages ​​as specified, for example, using the Google Translation API.

[0581] The translation results are stored in an information recording device (database), for example, using a database management system such as MySQL or PostgreSQL.

[0582] 3. Understanding anxieties and worries and suggesting content:

[0583] The server collects users' posts and behavioral history, and uses an AI analysis model to identify their anxieties and worries.

[0584] Depending on the identified anxieties and worries, the most appropriate content (e.g., relaxing music, uplifting videos) is selected and provided to the user's device.

[0585] 4. Disaster response:

[0586] The server detects when user activity ceases in the event of a disaster and provides video and music content free of charge.

[0587] It also accesses the APIs of various disaster information sources to collect the latest disaster information, organize and classify it, and quickly deliver it to users in the affected areas.

[0588] 5. Sentiment analysis and reassurance:

[0589] When a disaster occurs, the server analyzes users' emotions and selects and provides content that will provide a sense of security.

[0590] Specifically, an artificial intelligence analysis model is used to analyze voice data and text and select appropriate content.

[0591] Device Role

[0592] The user's device works in conjunction with the server to perform the following tasks:

[0593] 1. Retrieving and displaying messages:

[0594] When a user requests a multilingual translated message, the terminal retrieves the corresponding translated message from the server and displays it on the screen.

[0595] 2. Posting and sending your concerns and worries:

[0596] The content posted by users and audio data are collected and sent to the server.

[0597] 3. Viewing and playing content:

[0598] The terminal receives the content provided by the server and displays or plays it on the screen.

[0599] Specific examples

[0600] Here's a concrete example of how this system can be used:

[0601] When a user posts in the app, "I've been feeling anxious lately," the device collects this content and sends it to the server. The server uses an emotion analysis engine to analyze the post and determine that the user is feeling anxious. The server then selects uplifting music and sends it to the user's device, which then plays it.

[0602] In addition, in the event of a disaster, the server will detect when users' normal activities have stopped and provide them with free relaxation music and live video footage.

[0603] Prompt Sentence Examples

[0604] Here are some example prompts to be input to the generative AI model that will help this system:

[0605] "You feed user posts into an emotion engine to analyze the emotion labels (positive, negative, neutral). Then, you serve appropriate content (fun videos, relaxing music, etc.) based on the analyzed emotion labels."

[0606] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0607] Step 1:

[0608] The server collects new messages from the idols' and celebrities' social networking service accounts. Specifically, it retrieves new posts and tweets using the SNS API. The input at this point is the SNS account information, and the output is the collected message data.

[0609] Step 2:

[0610] The server inputs the collected message data into an artificial intelligence analysis model to analyze the context and sentiment. Specifically, it uses a natural language processing toolkit or machine learning model (e.g., Google BERT) to analyze the message context and generate sentiment labels (e.g., "positive," "negative," or "neutral"). The input is the message data, and the output is the sentiment analysis results.

[0611] Step 3:

[0612] The server inputs the sentiment analysis results into a multilingual translation model and performs automatic translation. Specifically, it uses the Google Translation API to translate into the specified language (e.g., English, Chinese, Korean). The input is the analyzed message, and the output is the translated message.

[0613] Step 4:

[0614] The server stores the translated message in an information storage device. Specifically, it stores the translation results in a database management system (e.g., MySQL, PostgreSQL). The input is the translated message, and the output is the message stored in the database.

[0615] Step 5:

[0616] The device displays multilingual translated messages on the screen in response to user requests. Specifically, the user requests a message through the app and sends the request to the server. The input is the request, and the output is the displayed translated message.

[0617] Step 6:

[0618] The terminal collects user posts (e.g., "I've been feeling anxious lately") and sends them to the server. The input is the user's posts, and the output is the data sent to the server.

[0619] Step 7:

[0620] The server inputs the user's post content into an AI analysis model to analyze their anxieties and worries. Specifically, it uses natural language processing tools to analyze the post content and generate specific emotion labels. The input is the post content, and the output is the analyzed emotion label.

[0621] Step 8:

[0622] The server selects the most appropriate content based on the analysis results and provides it to the user's device. Specifically, it selects appropriate relaxation music or videos from a database and sends them to the device. The input is the emotion analysis results and the content database, and the output is the selected content.

[0623] Step 9:

[0624] The terminal displays and plays the transmitted content on the screen. The input is the content data, and the output is the played music or video.

[0625] Step 10:

[0626] The server detects when users stop their normal activities in the event of a disaster and provides them with free relaxation music and live video. The input is the detected activity stoppage data, and the output is the content to be provided.

[0627] Step 11:

[0628] The server accesses the APIs of various disaster information sources to collect the latest disaster information, organizes and classifies it, and quickly delivers it to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information.

[0629] Step 12:

[0630] The server analyzes users' emotions during a disaster and selects and provides content that provides a sense of security. Specifically, it analyzes voice data and text using an emotion analysis model, and selects and provides appropriate relaxation content. The input is emotional data during a disaster, and the output is content that provides a sense of security.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] [Second embodiment]

[0635] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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."

[0647] This invention is a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[0648] This system is implemented in particular by the following means:

[0649] 1. Collecting social media messages

[0650] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[0651] 2. AI-powered message analysis and translation

[0652] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, for example, assigning emotional labels such as positive, negative, or neutral.

[0653] The server inputs the analyzed message into a multilingual translation model and performs automatic translation, translating Japanese messages into English, Chinese, Korean, etc.

[0654] 3. Providing multilingual translated messages

[0655] The user requests a social media message from their favorite person using an application on their device.

[0656] The server retrieves the corresponding translated message from the database in response to the user's request and transmits it to the terminal.

[0657] The terminal displays the transmitted translated message on the screen and provides it to the user.

[0658] 4. Understanding fans' concerns and worries and proposing content

[0659] A user posts on social media or within an application, for example, "I've been feeling a lot of anxiety lately."

[0660] The terminal collects the content posted by the user and transmits it to the server.

[0661] The server inputs the user's submitted post into an AI analysis model to understand their anxieties and worries.

[0662] The server selects the most suitable song or video based on the user's anxieties and worries and sends it to the device.

[0663] The device will display the selected song or video for playback.

[0664] 5. Providing free content during disasters

[0665] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[0666] The server provides live video and music to its users free of charge.

[0667] The terminal displays the received free live video and music on the screen, making them available to the user.

[0668] 6. Rapid collection and distribution of disaster information

[0669] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[0670] The server will then properly organize the collected disaster information and deliver relevant information to users.

[0671] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0672] Specific examples

[0673] Example 1: Collecting and translating social media messages

[0674] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[0675] Example 2: Understanding Anxiety and Proposing Content

[0676] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an AI analysis model and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[0677] As described above, the system of the present invention allows fans to receive emotional support even in the event of a disaster, and can quickly provide necessary information.

[0678] The processing flow will be explained below.

[0679] SNS message collection

[0680] Step 1:

[0681] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[0682] Step 2:

[0683] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[0684] Step 3:

[0685] The server stores the retrieved new messages in a database.

[0686] AI-powered message analysis and translation

[0687] Step 1:

[0688] The server reads the collected SNS messages from the database.

[0689] Step 2:

[0690] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[0691] Step 3:

[0692] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[0693] Step 4:

[0694] The server stores the translated messages in a database.

[0695] Providing multilingual translated messages

[0696] Step 1:

[0697] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[0698] Step 2:

[0699] The terminal sends a request to the server.

[0700] Step 3:

[0701] The server retrieves the corresponding translated message from the database.

[0702] Step 4:

[0703] The server transmits the obtained translated message to the terminal.

[0704] Step 5:

[0705] The terminal displays the received translated message on the screen.

[0706] Understanding fans' concerns and worries and proposing content

[0707] Step 1:

[0708] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[0709] Step 2:

[0710] The terminal collects the content posted by the user and transmits it to the server.

[0711] Step 3:

[0712] The server inputs the posted content into an AI analysis model to analyze and identify the user's anxieties and worries.

[0713] Step 4:

[0714] The server selects from a database the most appropriate content to address the user's anxieties and worries (for example, fun videos or uplifting music).

[0715] Step 5:

[0716] The server transmits the selected content to the terminal.

[0717] Step 6:

[0718] The terminal displays and plays the selected content on the screen.

[0719] Providing free content during disasters

[0720] Step 1:

[0721] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[0722] Step 2:

[0723] The server creates a list of users who have lost contact when a disaster occurs.

[0724] Step 3:

[0725] The server selects live videos and music to offer to the user free of charge.

[0726] Step 4:

[0727] The server transmits the selected free content to the terminal.

[0728] Step 5:

[0729] The terminal is enabled to display and play the received free content on the screen.

[0730] Rapid collection and distribution of disaster information

[0731] Step 1:

[0732] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[0733] Step 2:

[0734] The server will then properly organize and classify the collected disaster information.

[0735] Step 3:

[0736] The server distributes organized disaster information to users in the affected areas.

[0737] Step 4:

[0738] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0739] Example 1

[0740] 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."

[0741] In the event of a disaster, there are currently insufficient methods to provide a safe environment where fans can feel free to avoid feeling isolated or anxious. Furthermore, since it is difficult to understand the content of social media messages in foreign languages ​​and there are limited ways to quickly obtain necessary disaster information, it is necessary to create an environment where fans can act with peace of mind.

[0742] 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.

[0743] In this invention, the server includes a means for collecting new messages from idols' and celebrities' posting accounts, a means for inputting the collected messages into a generative AI model and analyzing the context and sentiment, and a means for inputting the analyzed messages into a multilingual translation model for automatic translation. This allows fans to understand idols' and celebrities' SNS messages in real time even in the event of a disaster, providing an environment where they can feel safe and secure without feeling lonely or anxious. Furthermore, by providing a means for collecting necessary disaster information from various disaster information sources and distributing it to users, it is possible to quickly provide necessary disaster information and create an environment where users can act with peace of mind.

[0744] "Idols and celebrities" are people who are widely recognized by the public and famous for their entertainment or social activities.

[0745] "Posting Account" means an account on a social networking site or other social media platform used by an individual or entity to post messages or content.

[0746] A "generative AI model" is an algorithm or program that uses artificial intelligence techniques to analyze data and understand context and emotions.

[0747] A "multilingual translation model" is an artificial intelligence model that performs automatic translation between different languages ​​and is used to convert content from one language to another.

[0748] A "recording medium" refers to a device or system for storing data or information, and includes databases, hard disks, flash memory, etc.

[0749] A "communication terminal" is a device that a user uses to send and receive information, and includes smartphones, tablets, computers, etc.

[0750] "Operation history" is a record of the operations performed by a user when using the system, and includes login history, click history, posting history, etc.

[0751] "Recommended content" refers to appropriate and optimal content such as music, video, and text selected by the system based on the user's behavior and status.

[0752] "Disaster information sources" are channels for disseminating disaster-related information provided by government agencies, public organizations, and reliable private organizations.

[0753] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0754] This invention relates to a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[0755] Hardware and software used

[0756] 1. Server

[0757] Use the API key to get SNS messages.

[0758] It uses generative AI models such as the Hugging Face sentiment analysis model and the Google Translate API.

[0759] A database (e.g., MySQL, PostgreSQL) is used as the recording medium for storing data.

[0760] 2. Terminal

[0761] Use a smartphone, tablet or computer to run the application.

[0762] 3. Users

[0763] A user of a system who requests and receives information through an application.

[0764] Data processing and calculation

[0765] 1. Collecting social media messages

[0766] The server authenticates to the SNS API using the API key and secret, periodically collects new messages, and stores them in a database.

[0767] 2. Message Analysis and Translation

[0768] The server inputs the collected messages into a generative AI model to analyze the context and sentiment. Based on the analysis results, the messages are input into a multilingual translation model to translate them from Japanese to English, Chinese, Korean, etc.

[0769] 3. Provision to Users

[0770] When a user requests a translation message through the application, the server retrieves the corresponding message from the database and sends it to the terminal, which then displays the translation message.

[0771] 4. Understanding anxieties and worries and proposing content

[0772] When a user enters their post content into the application, the device sends it to the server, which then uses a generative AI model to analyze their anxieties and worries. Based on the analysis results, the server selects the most appropriate content (e.g., uplifting songs or videos) and sends it to the device.

[0773] 5. Disaster response

[0774] The server detects when a user's regular activity has stopped and provides free live video and music.

[0775] The terminal receives and displays this information, allowing the user to use it with peace of mind.

[0776] 6. Collection and distribution of disaster information

[0777] The server accesses APIs for government and various disaster information sources, collects new disaster information, organizes it, and distributes it to users.

[0778] The terminal displays the received information on the screen, allowing the user to respond promptly.

[0779] Specific examples

[0780] Example 1: Collecting and translating social media messages

[0781] The server collects new messages from idols' social media accounts via API. The collected messages are analyzed for emotion using the Hugging Face model and then translated into multiple languages ​​using the Google Translate API. The translated results are stored in a database, and when a user makes a request through the app, the server retrieves the corresponding message, sends it to the device, and displays it.

[0782] Example 2: Understanding Anxiety and Proposing Content

[0783] When a user posts in the app, "I've been feeling more and more anxious lately," the device sends the post to the server, where it is analyzed by the generative AI model. The server then selects upbeat songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content, which the user can view or play.

[0784] Prompt Sentence Examples

[0785] "Get the latest tweets from Twitter, perform sentiment analysis, and translate them from Japanese to English."

[0786] "Please use AI to analyze user posts and recommend appropriate songs and videos."

[0787] Through the specific processing described above, the present invention is a system that can provide an environment in which fans can feel safe even in the event of a disaster and quickly deliver necessary information.

[0788] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0789] Step 1:

[0790] The server uses the social networking service's API to collect new messages from idol and celebrity posting accounts.

[0791] Input: SNS API key and secret, and the ID of the specific account.

[0792] Specific operation: The server authenticates with SNS using the API key and secret and sends a request to the "statuses / user_timeline" endpoint.

[0793] Data processing: Message data obtained from SNS is received in JSON format.

[0794] Output: The retrieved SNS message.

[0795] Step 2:

[0796] The server inputs the collected social media messages into a generative AI model and analyzes the context and emotions.

[0797] Input: Collected SNS messages.

[0798] What it does: The server analyzes the message using Hugging Face's sentiment analysis model.

[0799] Data computation: AI models analyze the context and sentiment of messages and assign sentiment labels as positive, negative, or neutral.

[0800] Output: Social media messages with sentiment labels.

[0801] Step 3:

[0802] The server inputs the emotion-analyzed message into a multilingual translation model for automatic translation.

[0803] Input: SNS messages with sentiment labels.

[0804] Specific operation: The server uses the Google Translate API to translate the message from Japanese to other languages ​​(English, Chinese, Korean, etc.).

[0805] Data processing: The model translates the text and generates messages in multiple languages.

[0806] Output: Multilingual translated message.

[0807] Step 4:

[0808] The server stores the translation results in a recording medium.

[0809] Input: A message translated into multiple languages.

[0810] Specific operation: The server stores the translation results in a database (e.g., MySQL, PostgreSQL).

[0811] Data processing: Store the message ID and the corresponding translation text in the appropriate table.

[0812] Output: The translated message stored in the database.

[0813] Step 5:

[0814] The user requests a social media message from their favorite person using an application on their device.

[0815] Input: User request (e.g. Idol A's recent posts).

[0816] What happens: A user clicks a button in the app, and a request is sent from the device to the server.

[0817] Data Calculation: The server retrieves the corresponding translation message from the database upon request.

[0818] Output: The translated message is sent from the server to the device.

[0819] Step 6:

[0820] The terminal displays the received translated message on the screen and provides it to the user.

[0821] Input: The translated message sent from the server.

[0822] Specific operation: The terminal displays the received message on the application screen.

[0823] Output: The translated message that is visible to the user.

[0824] Step 7:

[0825] Users post their concerns and worries within the application.

[0826] Input: A text post typed by the user (e.g., "I've been worrying a lot lately").

[0827] Specific operation: The user clicks the post button, and the post content is sent from the device to the server.

[0828] Output: The post sent to the server.

[0829] Step 8:

[0830] The server inputs the submitted post content into a generative AI model to understand the person's anxieties and worries.

[0831] Input: User submissions.

[0832] What happens: The server parses the post using a natural language processing model (e.g., the BERT model).

[0833] Data calculation: The model extracts keywords related to anxiety and worries from the content of the post.

[0834] Output: Analysis results identifying anxieties and worries.

[0835] Step 9:

[0836] The server selects the most appropriate content based on the user's anxieties and worries and sends it to the device.

[0837] Input: Analysis results of anxiety and worries.

[0838] Specific operation: The server searches the database for upbeat songs and videos and selects the most suitable content.

[0839] Data processing: Obtain information about the selected content and send it to the device.

[0840] Output: The selected content is sent to the device.

[0841] Step 10:

[0842] The terminal displays the selected content on the screen for the user to view or play.

[0843] Input: Content information sent from the server.

[0844] Specific operation: The terminal displays the received content on the application screen and provides a play button.

[0845] Output: Content that can be viewed and played by the user.

[0846] Step 11:

[0847] The server accesses the API of the disaster information source to collect the latest disaster information.

[0848] Input: API key and endpoint of disaster information source.

[0849] Specific operation: The server sends a request to the API to obtain new disaster information.

[0850] Data processing: Receive disaster information in JSON format and organize it appropriately.

[0851] Output: Retrieved and organized disaster information.

[0852] Step 12:

[0853] The server distributes the organized disaster information to users.

[0854] Input: Organized disaster information.

[0855] Specific operation: The server identifies users in the affected area and registers them in a distribution list for disaster information.

[0856] Data Processing: Generate a unique notification message and send it to the corresponding user.

[0857] Output: Disaster information notification sent to the user's device.

[0858] Step 13:

[0859] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0860] Input: Disaster information notification sent from the server.

[0861] Specific operation: The terminal displays the received information on the application screen and notifies the user.

[0862] Output: A screen displaying disaster information and user response preparation.

[0863] (Application example 1)

[0864] 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."

[0865] Many fans tend to feel lonely and anxious during disasters, and there is a need for a system to alleviate this psychological burden. However, many current systems are unable to respond quickly and appropriately due to language barriers and a lack of information. Furthermore, they do not adequately provide content tailored to the psychological state of fans, and are unable to provide sufficient emotional support. This invention aims to utilize AI technology to translate multiple languages, grasp psychological states, and quickly distribute information during disasters, thereby providing an environment where fans can feel safe.

[0866] 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.

[0867] In this invention, the server includes: means for collecting new messages from the social media accounts of idols and celebrities; means for inputting the collected messages into an automatic analysis model and analyzing the context and sentiment; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing the multilingual translated messages to a terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing free viewing content to a terminal when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and delivering it to users; means for using a generative AI model to provide recommended content; and means for creating prompts for the generative AI model. This allows fans to feel safe and secure even during disasters without worrying about information.

[0868] "Idols and celebrities" generally refer to individuals who have a large number of fans and social influence.

[0869] "Social media account" refers to an account held by an individual or company within a social networking service for disseminating information, whether public or private.

[0870] "New messages" refers to the latest posts, comments, tweets, and other information from social media accounts.

[0871] An "automatic analysis model" refers to an algorithm that uses machine learning and natural language processing technology to analyze input text data and parse its content and emotions.

[0872] "Context and sentiment" refers to the meaning of the information contained within a text message and its emotional tenor, such as positive, negative, or neutral.

[0873] "Multilingual translation model" refers to an algorithm that uses artificial intelligence technology to automatically translate from a specific language to another language.

[0874] "Database" refers to a system for storing and managing data in a structured format.

[0875] A "terminal" refers to a device with communication capabilities that can be operated by a user, such as a smartphone, tablet, or personal computer.

[0876] "User posts and behavioral history" refers to data such as text, images, and activity logs posted by users on social media or within applications.

[0877] "Understanding anxieties and worries" refers to analyzing the content posted by users and their behavioral history to identify their mental state.

[0878] "Recommended content" refers to information such as songs, videos, articles, etc. that are deemed most suitable based on the user's emotions and interests.

[0879] "Detecting a cessation of user activity when a disaster occurs" means that the system detects a sudden decrease in the frequency of normal logins and posts.

[0880] "Providing viewing content free of charge" refers to providing entertainment content such as live footage and music without charging fees in the event of a disaster.

[0881] "Disaster information sources" refers to up-to-date information about disasters provided by government agencies and disaster information services.

[0882] A "generative AI model" refers to an artificial intelligence system that uses machine learning and deep learning to generate information and content in response to user requests.

[0883] A "prompt sentence" refers to an input sentence that causes a generative AI model to perform a specific task.

[0884] The present invention provides a system that reduces the loneliness and anxiety felt by users when a disaster occurs, and provides a safe environment. Specific embodiments of the system are described below.

[0885] System Configuration

[0886] This system consists of the following elements:

[0887] server

[0888] Terminal

[0889] User

[0890] Hardware and software used

[0891] API: Twitter API, YouTube API

[0892] AI analysis model: Google Cloud Natural Language API

[0893] Multilingual Translation: Google Translation API

[0894] Database: Firebase Realtime Database

[0895] Frontend: React Native for mobile app

[0896] System processing overview

[0897] 1. Collecting social media messages

[0898] The server uses the Twitter API to periodically collect new messages from the social media accounts of idols and celebrities.

[0899] 2. Message Analysis

[0900] The server analyzes the collected messages using the Google Cloud Natural Language API to analyze context and sentiment.

[0901] 3. Multilingual Translation

[0902] The server automatically translates the parsed messages into multiple languages ​​using the Google Translation API, and the translated messages are stored in the Firebase Realtime Database.

[0903] 4. Provision to Users

[0904] The server provides the multilingual translated message to the terminal when requested by the user through the terminal, and the terminal displays the message to the user.

[0905] 5. Understanding anxieties and worries and proposing content

[0906] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries and selects recommended content based on that information. The selected content is then provided to the user via the device.

[0907] 6. Providing free content during disasters

[0908] The server detects when a user's regular activities (logins and posts) are interrupted during a disaster. If this is detected, the server provides free viewing content (live video and music) to the device, allowing the user to use it.

[0909] 7. Collection and distribution of disaster information

[0910] The server accesses the APIs of various disaster information sources to collect the latest disaster information, which is then organized and quickly distributed to users in the affected areas.

[0911] Specific examples

[0912] Collecting and translating social media messages

[0913] The server collects new tweets from idols' and celebrities' social media accounts and assigns them sentiment labels. The server then translates the messages into multiple languages ​​(English, Chinese, Korean, etc.) and stores them in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message and sends it to the device. The device then displays the translated message.

[0914] Providing content based on user emotions

[0915] When a user posts "I've been feeling down lately" from their device, the device sends the post to a server. The server uses an AI analysis model to analyze the post and identify the anxiety. The server then selects uplifting songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content for the user to view and play.

[0916] Prompt Sentence Examples

[0917] "Please suggest appropriate content (uplifting songs or videos) in response to the recent user post, 'I've been feeling down lately.'"

[0918] The above is an embodiment of the present invention. This system allows fans to feel at ease even in the event of a disaster and to quickly obtain the information they need.

[0919] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0920] Step 1:

[0921] Collecting SNS messages

[0922] The server periodically collects new messages from idols and celebrities using the APIs of their social media accounts. Specifically, it uses the Twitter API to retrieve the latest tweets from the accounts. The input is the response from the social media API, and the output is the retrieved messages.

[0923] Step 2:

[0924] Message Parsing

[0925] The server inputs the collected messages into the Google Cloud Natural Language API to analyze the context and sentiment. This process determines whether the message contains positive, negative, or neutral sentiment. The input is the collected messages, and the output is messages with sentiment labels attached. Specifically, the server sends messages to the Natural Language API and receives sentiment labels in response.

[0926] Step 3:

[0927] Multilingual Translation

[0928] The server uses the Google Translation API to translate the analyzed message into multiple languages. The target languages ​​for translation include English, Chinese, and Korean. The input is a message with an emotion label attached, and the output is a multilingual translation of the message. Specifically, the server sends the message to the Translation API and receives the translated text in each language.

[0929] Step 4:

[0930] Saving to a database

[0931] The server stores the translated messages in the Firebase Realtime Database. The input is the multilingual translated message, and the output is a data entry stored in the database. Specifically, the translated message is written to the database and a new data entry is created.

[0932] Step 5:

[0933] Provision to users

[0934] When a user requests a translation message through a terminal, the server retrieves the corresponding translation message from the database and sends it to the terminal. The input is the user request, and the output is the translation message. Specifically, the server queries the database based on the request and sends the result to the terminal.

[0935] Step 6:

[0936] Understanding anxieties and worries

[0937] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries. The input is the user's posts and behavioral history, and the output is the identified anxieties and worries. Specifically, the collected data is sent to the server, where it is analyzed.

[0938] Step 7:

[0939] Content Suggestion

[0940] The server selects recommended content, such as uplifting music or videos, based on the analyzed anxieties and worries. The selected content is provided to the user via the device. The input is the identified anxieties and worries, and the output is the selected content. Specifically, the server creates a prompt for the generative AI model, inputs the analysis results, and generates appropriate content.

[0941] Step 8:

[0942] Disaster activity detection

[0943] The server detects when a user's regular activity (logins or posts) has ceased. The input is the login and post activity history, and the output is the result of the detection of the cessation of activity. Specifically, it periodically checks the user's activity log and confirms the cessation of activity if there has been no activity for a certain period of time.

[0944] Step 9:

[0945] Providing free content during disasters

[0946] The server provides free content to inactive users. The input is the result of the inactivity detection, and the output is free content. Specifically, the server provides live video and music data via a generative AI model.

[0947] Step 10:

[0948] Collection and distribution of disaster information

[0949] The server accesses the APIs of various disaster information sources, collects and organizes the latest disaster information, and then distributes the organized information to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information. Specifically, the server obtains information from the APIs and notifies users of the organized information in real time.

[0950] The above are the specific processing steps for carrying out the present invention. The input, data processing, and output performed at each step have been described in detail, and specific operations have been shown.

[0951] 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.

[0952] This invention combines an emotion engine with a system that provides a safe environment for fans in the event of a disaster, preventing them from feeling lonely or anxious. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and in addition to translating them into multiple languages, it also has an emotion engine that recognizes the user's emotions, aiming to provide more appropriate content.

[0953] This system is implemented by the following means:

[0954] 1. Collecting social media messages

[0955] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[0956] 2. AI-powered message analysis and translation

[0957] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, generating emotion labels such as "positive," "negative," and "neutral."

[0958] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[0959] The server stores the translated messages in a database.

[0960] 3. Providing multilingual translated messages

[0961] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[0962] The terminal sends a request to the server.

[0963] The server retrieves the corresponding translated message from the database.

[0964] The server transmits the obtained translated message to the terminal.

[0965] The terminal displays the received translated message on the screen.

[0966] 4. User Emotion Recognition by Emotion Engine

[0967] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[0968] The terminal collects the content posted by the user and transmits it to the server.

[0969] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[0970] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[0971] 5. Understanding anxieties and worries and proposing content

[0972] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[0973] The server transmits the selected content to the terminal.

[0974] The terminal displays and plays the selected content on the screen.

[0975] 6. Providing free content during disasters

[0976] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[0977] The server selects live videos and music to provide to the user free of charge.

[0978] The server transmits the selected free content to the terminal.

[0979] The terminal is enabled to display and play the received free content on the screen.

[0980] 7. Rapid collection and distribution of disaster information

[0981] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[0982] The server will then properly organize and classify the collected disaster information.

[0983] The server distributes organized disaster information to users in the affected areas.

[0984] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[0985] Specific examples

[0986] Example 1: Collecting and translating social media messages

[0987] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[0988] Example 2: Understanding Anxiety and Proposing Content

[0989] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[0990] Example 3: Using an Emotion Engine

[0991] If a user posts a voice message within the app and the message is negative, such as "I'm so tired," the device collects the voice data and sends it to the server. The server uses an emotion engine to analyze the voice data and determine that the user is tired. The server selects relaxing music or an uplifting message to soothe the user and sends it to the device. The device then displays and plays this content.

[0992] As described above, by combining emotion engines, it is possible to more accurately grasp the user's emotions and respond to them individually, thereby providing psychological support even in the event of a disaster.

[0993] The processing flow will be explained below.

[0994] Processing flow of the system including the emotion engine

[0995] SNS message collection

[0996] Step 1:

[0997] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[0998] Step 2:

[0999] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[1000] Step 3:

[1001] The server stores the retrieved new messages in a database.

[1002] AI-powered message analysis and translation

[1003] Step 1:

[1004] The server reads the collected SNS messages from the database.

[1005] Step 2:

[1006] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[1007] Step 3:

[1008] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[1009] Step 4:

[1010] The server stores the translated messages in a database.

[1011] Providing multilingual translated messages

[1012] Step 1:

[1013] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[1014] Step 2:

[1015] The terminal sends a request to the server.

[1016] Step 3:

[1017] The server retrieves the corresponding translated message from the database.

[1018] Step 4:

[1019] The server transmits the obtained translated message to the terminal.

[1020] Step 5:

[1021] The terminal displays the received translated message on the screen.

[1022] Recognizing user emotions with an emotion engine

[1023] Step 1:

[1024] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[1025] Step 2:

[1026] The terminal collects the content posted by the user and transmits it to the server.

[1027] Step 3:

[1028] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[1029] Step 4:

[1030] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[1031] Understanding anxieties and worries and proposing content

[1032] Step 1:

[1033] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[1034] Step 2:

[1035] The server transmits the selected content to the terminal.

[1036] Step 3:

[1037] The terminal displays and plays the selected content on the screen.

[1038] Providing free content during disasters

[1039] Step 1:

[1040] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[1041] Step 2:

[1042] The server creates a list of users who have lost contact when a disaster occurs.

[1043] Step 3:

[1044] The server selects live videos and music to offer to the user free of charge.

[1045] Step 4:

[1046] The server transmits the selected free content to the terminal.

[1047] Step 5:

[1048] The terminal is enabled to display and play the received free content on the screen.

[1049] Rapid collection and distribution of disaster information

[1050] Step 1:

[1051] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[1052] Step 2:

[1053] The server will then properly organize and classify the collected disaster information.

[1054] Step 3:

[1055] The server distributes organized disaster information to users in the affected areas.

[1056] Step 4:

[1057] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1058] Example 2

[1059] 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."

[1060] In recent years, with the spread of social networking services (SNS), messages from idols and celebrities have become an important source of information for users. However, these messages are not available in multiple languages ​​and lack emotional support. Furthermore, in the event of a disaster, users are likely to face an environment where they feel anxious and lonely. In such situations, there is a need for a system that can provide users with psychological support and prompt information.

[1061] 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 collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing multilingual translated messages to users' communication devices; means for collecting user posts and behavioral history to understand their anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for analyzing users' emotions using an emotion engine; means for detecting a cessation of user activity when a disaster occurs; means for providing live video and music to users' communication devices free of charge when a disaster occurs; and means for collecting necessary disaster information from various disaster information sources and distributing it to users. This allows users to understand messages from idols and celebrities in multiple languages, reducing anxiety and loneliness in everyday life and during disasters, and receiving emotional support.

[1062] "Idols and celebrities" refer to individuals who are recognized by many people and have social influence.

[1063] A "social networking service account" is an authenticated user profile used by an individual or organization to disseminate and share information on a social networking service.

[1064] An "artificial intelligence analysis model" refers to a program or system that uses AI technology to analyze text and data and understand its context and emotions.

[1065] A "multilingual translation model" is a program or system for automatically translating text into different languages.

[1066] A "database" is an information system that efficiently stores large amounts of data and allows them to be quickly retrieved when needed.

[1067] "Communication devices" are devices such as smartphones, tablets, and personal computers that are used to send and receive information via the Internet or other communication networks.

[1068] "Posted content" refers to all information such as text, images, audio, and video that users share through social media or applications.

[1069] "Behavioral history" refers to a record of all actions a user takes within a social networking site or application, such as posting messages, viewing content, clicking "like," etc.

[1070] An "emotion engine" is a system that automatically analyzes a user's emotions from text, voice, images, etc. and outputs the results.

[1071] "Disaster information sources" refer to disaster-related information provided by government agencies, public institutions, and other reliable organizations.

[1072] An "application program interface (API)" refers to a defined method or interface that allows software to interact with other software and utilize its functionality.

[1073] The present invention combines an emotion engine with a system that provides a safe environment in the event of a disaster, preventing fans from feeling lonely or anxious. A specific embodiment of this system is described below.

[1074] Overall system overview

[1075] The system operates around a server, devices, and users. The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, and analyzes their context and sentiment using an artificial intelligence analysis model. The server also inputs the messages into a multilingual translation model for automatic translation, and stores the translation results in a database. Users can then view these messages using a dedicated application.

[1076] Hardware and Software Configuration

[1077] server

[1078] The server is hardware with a powerful processor and large memory capacity, and runs the following software components:

[1079] SNS Collection Module: Collects messages using the SNS API.

[1080] Artificial intelligence analysis models: For example, using the BERT model to analyze the context and sentiment of messages.

[1081] Multilingual translation model: Translate messages into multiple languages ​​using Google Translate API and DeepL Translator.

[1082] Database system: stores the collected and analyzed messages.

[1083] Terminal

[1084] The device is a user's communication device such as a smartphone, tablet, or PC. It has a dedicated application installed and provides the following functions:

[1085] Message request function: Users request translation messages.

[1086] Message display function: Displays the translated message received from the server.

[1087] Posting function: Sends user posts to the server.

[1088] User operation procedure

[1089] Users can launch the dedicated application and read the latest social media messages of idols and celebrities, translating them. Users can also post about their feelings and situations in the application, and the system will analyze the user's emotions and suggest appropriate content.

[1090] Specific examples

[1091] Example 1: Collecting and translating social media messages

[1092] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an artificial intelligence analysis model (e.g., the BERT model) and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[1093] Example 2: Understanding Anxiety and Proposing Content

[1094] A user posts in the app, "I've been feeling a lot of anxiety lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease the anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[1095] Prompt Sentence Examples

[1096] Request a translation of Idol A's latest tweet.

[1097] There's been a lot of anxiety lately, so please provide some uplifting content.

[1098] Analyze my voice messages to understand my emotional state and recommend relaxing music.

[1099] In this way, by combining AI and an emotion engine, the system of the present invention can accurately grasp the user's emotions and provide psychological support even in the event of a disaster.

[1100] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1101] Specific processing steps of the program

[1102] Step 1: Collect SNS messages

[1103] The server collects new messages from the social networking service accounts of idols and celebrities.

[1104] Input: Your SNS API key and account information.

[1105] Processing: The server retrieves the message using an API, for example calling the Twitter API GET / 2 / tweets endpoint to get the latest tweets.

[1106] Output: A new SNS message (e.g., tweet data in JSON format).

[1107] Specific operation: The server periodically calls the Twitter API, retrieves the latest messages, and temporarily stores them.

[1108] Step 2: Analyzing the message with AI

[1109] The server inputs the collected SNS messages into an AI analysis model and assigns emotion labels.

[1110] Input: Collected SNS messages (e.g. tweet data in JSON format).

[1111] Processing: The message is fed into an AI analysis model, such as the BERT model, to analyze the context and sentiment, which then generates a sentiment label, such as "positive," "negative," or "neutral."

[1112] Output: Message data with sentiment labels.

[1113] Specific operation: The server calls the BERT model to analyze SNS messages and assign sentiment labels.

[1114] Step 3: Translate your messages into multiple languages

[1115] The server inputs the parsed message into a multilingual translation model and translates it into the specified language.

[1116] Input: Message data with emotion labels.

[1117] Processing: Input the message into Google Translate API or DeepL Translator and translate it into the specified language, such as English, Chinese, or Korean.

[1118] Output: Multilingual translated message data.

[1119] Specific operation: The server uses the translation API to translate the message into a different language and obtains the translation result.

[1120] Step 4: Save the translated message

[1121] The server stores the translated messages in a database.

[1122] Input: Multilingual translated message data.

[1123] Processing: Store the translated messages in a database system.

[1124] Output: The translated message stored in the database.

[1125] What happens: The server connects to the database and executes a SQL query to store the translated message.

[1126] Step 5: Providing multilingual translated messages

[1127] The user launches the app from their communication device and requests a translation of a social media message.

[1128] Input: User request (e.g. "Show me the translation of idol A's latest tweet in English").

[1129] Process: The device sends a request to the server, which retrieves the corresponding translation message from the database and sends it to the device.

[1130] Output: The translated message that will be displayed on the user's communication device.

[1131] What happens: The server executes a database query to retrieve the message and sends it to the device, which then displays the received translated message on its screen.

[1132] Step 6: Recognizing user emotions with the emotion engine

[1133] Users post about their feelings and situations on social media or within apps.

[1134] Input: User post (e.g., "I've been feeling a lot of anxiety lately").

[1135] Processing: The device collects the posted content and sends it to the server. The server inputs the posted content into the emotion engine and analyzes the user's emotions.

[1136] Output: Post data with sentiment labels.

[1137] Specific operation: The server calls the emotion engine, analyzes the user's post content, and assigns emotion labels.

[1138] Step 7: Understanding anxieties and worries and proposing content

[1139] The server selects the most suitable content based on the analysis results and provides it to the user.

[1140] Input: Post data with sentiment labels.

[1141] Processing: The server selects the most suitable content (e.g., fun videos or uplifting music) from the database.

[1142] Output: Content sent to the user's communication device.

[1143] Specific operations: The server executes a database query to select content and sends it to the device, which then displays or plays the received content.

[1144] Step 8: Providing free content during disasters

[1145] The server detects that user activity has ceased when a disaster occurs.

[1146] Input: User activity log (e.g. login and posting frequency).

[1147] Processing: The server analyzes disaster information and user activity logs, receives notifications when activity stops, selects free content, and sends it to the device.

[1148] Output: Free content sent to the user's communication device.

[1149] Specific operation: The server monitors the user's activity based on disaster information, and if the activity stops, it selects free live video or music and sends it to the device.

[1150] Step 9: Rapid collection and distribution of disaster information

[1151] The server accesses the API of the disaster information source to collect new disaster information.

[1152] Input: API key and endpoint information for the disaster information source.

[1153] Processing: The server calls the API to obtain the latest disaster information, organizes and categorizes it appropriately, and delivers the organized information to users.

[1154] Output: Disaster information delivered to users' communication devices.

[1155] Specific operation: The server collects disaster information from public institution APIs and distributes organized information to users in the affected areas. The device notifies the user of the received information.

[1156] The above is the specific processing flow of the system.

[1157] (Application example 2)

[1158] 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."

[1159] When a disaster occurs, providing a safe environment where users can feel free to avoid feeling lonely or anxious is an important issue. While existing systems provide disaster information and multilingual translation, they lack the ability to grasp the user's emotional state and provide appropriate content accordingly. Furthermore, there are insufficient effective support methods that can provide a sense of security free of charge when a disaster occurs. Therefore, a system that can respond to users' emotions and provide a sense of security in real time is needed.

[1160] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in an information recording device; means for providing the multilingual translated messages to a user's terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting optimal content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing video content and music content to a user's terminal free of charge when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and distributing it to users; and means for analyzing users' emotions when a disaster occurs and selecting and providing content that provides a sense of security. This makes it possible to create an environment in which users can feel safe even during a disaster by understanding the user's emotional state and providing appropriate content.

[1161] "Idol" refers to a talent or artist who is popular in a particular entertainment field.

[1162] A "celebrity" is someone who is widely known and active in fields such as entertainment, sports, politics, or business.

[1163] "Social networking service" refers to a web service or platform that enables users to communicate with other users online.

[1164] "Message" means information transmitted between Users in the form of text, images, audio, video, etc.

[1165] "Artificial intelligence analytical model" refers to the algorithms and programs used to analyze collected data and understand its context and sentiment.

[1166] A "multilingual translation model" refers to an algorithm or program that automatically translates a sentence in one language into another language.

[1167] "Information recording device" refers to a computer or medium for temporarily or permanently storing data.

[1168] "User" refers to an individual who uses a system or application.

[1169] "Device" refers to a computing device such as a smartphone, tablet, or PC.

[1170] "Posted content" refers to information such as text, images, audio, and video that users post on social media or applications.

[1171] "Behavioral history" refers to a record of a user's actions within a system or application.

[1172] "Anxiety" refers to feelings of worry and fear that users have about their safety and the future.

[1173] "Problems" refers to the problems and distress that users face.

[1174] "Content" refers to any form of information or entertainment (e.g., music, video, text) provided to a user.

[1175] "Means for detecting inactivity" refers to a system that detects when a user's normal activity has ceased for a period of time.

[1176] "Video content" refers to content that includes visual information such as videos and live footage.

[1177] "Music Content" refers to content that contains auditory information, such as music tracks or live music.

[1178] "Disaster information sources" refer to data sources that provide information about disasters, such as those provided by government agencies and disaster response organizations.

[1179] An "application program interface (API)" refers to the definitions and protocols that allow software to communicate and interact with each other.

[1180] "Means for analyzing emotions" refers to systems or methods that analyze users' posted content and behavioral history to identify their emotional state.

[1181] In this invention, the following system is constructed to provide a safe environment for users even in the event of a disaster. The system is composed of a server, a user terminal, and related software modules.

[1182] Server Roles

[1183] The server has the following functions:

[1184] 1. Message collection and analysis:

[1185] The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, using the SNS API.

[1186] The collected messages are then fed into an artificial intelligence (AI) analysis model to analyze their context and sentiment, using natural language processing toolkits and machine learning models (e.g., Google BERT).

[1187] 2. Multilingual translation and storage:

[1188] The parsed message is input into a multilingual translation model and automatically translated into other languages ​​as specified, for example, using the Google Translation API.

[1189] The translation results are stored in an information recording device (database), for example, using a database management system such as MySQL or PostgreSQL.

[1190] 3. Understanding anxieties and worries and suggesting content:

[1191] The server collects users' posts and behavioral history, and uses an AI analysis model to identify their anxieties and worries.

[1192] Depending on the identified anxieties and worries, the most appropriate content (e.g., relaxing music, uplifting videos) is selected and provided to the user's device.

[1193] 4. Disaster response:

[1194] The server detects when user activity ceases in the event of a disaster and provides video and music content free of charge.

[1195] It also accesses the APIs of various disaster information sources to collect the latest disaster information, organize and classify it, and quickly deliver it to users in the affected areas.

[1196] 5. Sentiment analysis and reassurance:

[1197] When a disaster occurs, the server analyzes users' emotions and selects and provides content that will provide a sense of security.

[1198] Specifically, an artificial intelligence analysis model is used to analyze voice data and text and select appropriate content.

[1199] Device Role

[1200] The user's device works in conjunction with the server to perform the following tasks:

[1201] 1. Retrieving and displaying messages:

[1202] When a user requests a multilingual translated message, the terminal retrieves the corresponding translated message from the server and displays it on the screen.

[1203] 2. Posting and sending your concerns and worries:

[1204] The content posted by users and audio data are collected and sent to the server.

[1205] 3. Viewing and playing content:

[1206] The terminal receives the content provided by the server and displays or plays it on the screen.

[1207] Specific examples

[1208] Here's a concrete example of how this system can be used:

[1209] When a user posts in the app, "I've been feeling anxious lately," the device collects this content and sends it to the server. The server uses an emotion analysis engine to analyze the post and determine that the user is feeling anxious. The server then selects uplifting music and sends it to the user's device, which then plays it.

[1210] In addition, in the event of a disaster, the server will detect when users' normal activities have stopped and provide them with free relaxation music and live video footage.

[1211] Prompt Sentence Examples

[1212] Here are some example prompts to be input to the generative AI model that will help this system work:

[1213] "You feed user posts into an emotion engine to analyze the emotion labels (positive, negative, neutral). Then, you serve appropriate content (fun videos, relaxing music, etc.) based on the analyzed emotion labels."

[1214] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1215] Step 1:

[1216] The server collects new messages from the idols' and celebrities' social networking service accounts. Specifically, it retrieves new posts and tweets using the SNS API. The input at this point is the SNS account information, and the output is the collected message data.

[1217] Step 2:

[1218] The server inputs the collected message data into an artificial intelligence analysis model to analyze the context and sentiment. Specifically, it uses a natural language processing toolkit or machine learning model (e.g., Google BERT) to analyze the message context and generate sentiment labels (e.g., "positive," "negative," or "neutral"). The input is the message data, and the output is the sentiment analysis results.

[1219] Step 3:

[1220] The server inputs the sentiment analysis results into a multilingual translation model and performs automatic translation. Specifically, it uses the Google Translation API to translate into the specified language (e.g., English, Chinese, Korean). The input is the analyzed message, and the output is the translated message.

[1221] Step 4:

[1222] The server stores the translated message in an information storage device. Specifically, it stores the translation results in a database management system (e.g., MySQL, PostgreSQL). The input is the translated message, and the output is the message stored in the database.

[1223] Step 5:

[1224] The device displays multilingual translated messages on the screen in response to user requests. Specifically, the user requests a message through the app and sends the request to the server. The input is the request, and the output is the displayed translated message.

[1225] Step 6:

[1226] The terminal collects user posts (e.g., "I've been feeling anxious lately") and sends them to the server. The input is the user's posts, and the output is the data sent to the server.

[1227] Step 7:

[1228] The server inputs the user's post content into an AI analysis model to analyze their anxieties and worries. Specifically, it uses natural language processing tools to analyze the post content and generate specific emotion labels. The input is the post content, and the output is the analyzed emotion label.

[1229] Step 8:

[1230] The server selects the most appropriate content based on the analysis results and provides it to the user's device. Specifically, it selects appropriate relaxation music or videos from a database and sends them to the device. The input is the emotion analysis results and the content database, and the output is the selected content.

[1231] Step 9:

[1232] The terminal displays and plays the transmitted content on the screen. The input is the content data, and the output is the played music or video.

[1233] Step 10:

[1234] The server detects when users stop their normal activities in the event of a disaster and provides them with free relaxation music and live video. The input is the detected activity stoppage data, and the output is the content to be provided.

[1235] Step 11:

[1236] The server accesses the APIs of various disaster information sources to collect the latest disaster information, organizes and classifies it, and quickly delivers it to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information.

[1237] Step 12:

[1238] The server analyzes users' emotions during a disaster and selects and provides content that provides a sense of security. Specifically, it analyzes voice data and text using an emotion analysis model, and selects and provides appropriate relaxation content. The input is emotional data during a disaster, and the output is content that provides a sense of security.

[1239] 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.

[1240] 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.

[1241] 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.

[1242] [Third embodiment]

[1243] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1244] 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.

[1245] 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).

[1246] 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.

[1247] 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.

[1248] 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).

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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."

[1255] This invention is a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[1256] This system is implemented in particular by the following means:

[1257] 1. Collecting social media messages

[1258] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[1259] 2. AI-powered message analysis and translation

[1260] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, for example, assigning emotional labels such as positive, negative, or neutral.

[1261] The server inputs the analyzed message into a multilingual translation model and performs automatic translation, translating Japanese messages into English, Chinese, Korean, etc.

[1262] 3. Providing multilingual translated messages

[1263] The user requests a social media message from their favorite person using an application on their device.

[1264] The server retrieves the corresponding translated message from the database in response to the user's request and transmits it to the terminal.

[1265] The terminal displays the transmitted translated message on the screen and provides it to the user.

[1266] 4. Understanding fans' concerns and worries and proposing content

[1267] A user posts on social media or within an application, for example, "I've been feeling a lot of anxiety lately."

[1268] The terminal collects the content posted by the user and transmits it to the server.

[1269] The server inputs the user's submitted post into an AI analysis model to understand their anxieties and worries.

[1270] The server selects the most suitable song or video based on the user's anxieties and worries and sends it to the device.

[1271] The device will display the selected song or video for playback.

[1272] 5. Providing free content during disasters

[1273] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[1274] The server provides live video and music to its users free of charge.

[1275] The terminal displays the received free live video and music on the screen, making them available to the user.

[1276] 6. Rapid collection and distribution of disaster information

[1277] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[1278] The server will then properly organize the collected disaster information and deliver relevant information to users.

[1279] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1280] Specific examples

[1281] Example 1: Collecting and translating social media messages

[1282] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[1283] Example 2: Understanding Anxiety and Proposing Content

[1284] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an AI analysis model and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[1285] As described above, the system of the present invention allows fans to receive emotional support even in the event of a disaster, and can quickly provide necessary information.

[1286] The processing flow will be explained below.

[1287] SNS message collection

[1288] Step 1:

[1289] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[1290] Step 2:

[1291] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[1292] Step 3:

[1293] The server stores the retrieved new messages in a database.

[1294] AI-powered message analysis and translation

[1295] Step 1:

[1296] The server reads the collected SNS messages from the database.

[1297] Step 2:

[1298] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[1299] Step 3:

[1300] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[1301] Step 4:

[1302] The server stores the translated messages in a database.

[1303] Providing multilingual translated messages

[1304] Step 1:

[1305] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[1306] Step 2:

[1307] The terminal sends a request to the server.

[1308] Step 3:

[1309] The server retrieves the corresponding translated message from the database.

[1310] Step 4:

[1311] The server transmits the obtained translated message to the terminal.

[1312] Step 5:

[1313] The terminal displays the received translated message on the screen.

[1314] Understanding fans' concerns and worries and proposing content

[1315] Step 1:

[1316] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[1317] Step 2:

[1318] The terminal collects the content posted by the user and transmits it to the server.

[1319] Step 3:

[1320] The server inputs the posted content into an AI analysis model to analyze and identify the user's anxieties and worries.

[1321] Step 4:

[1322] The server selects from a database the most appropriate content to address the user's anxieties and worries (for example, fun videos or uplifting music).

[1323] Step 5:

[1324] The server transmits the selected content to the terminal.

[1325] Step 6:

[1326] The terminal displays and plays the selected content on the screen.

[1327] Providing free content during disasters

[1328] Step 1:

[1329] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[1330] Step 2:

[1331] The server creates a list of users who have lost contact when a disaster occurs.

[1332] Step 3:

[1333] The server selects live videos and music to offer to the user free of charge.

[1334] Step 4:

[1335] The server transmits the selected free content to the terminal.

[1336] Step 5:

[1337] The terminal is enabled to display and play the received free content on the screen.

[1338] Rapid collection and distribution of disaster information

[1339] Step 1:

[1340] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[1341] Step 2:

[1342] The server will then properly organize and classify the collected disaster information.

[1343] Step 3:

[1344] The server distributes organized disaster information to users in the affected areas.

[1345] Step 4:

[1346] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1347] Example 1

[1348] 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."

[1349] In the event of a disaster, there are currently insufficient methods to provide a safe environment where fans can feel free to avoid feeling isolated or anxious. Furthermore, since it is difficult to understand the content of social media messages in foreign languages ​​and there are limited ways to quickly obtain necessary disaster information, it is necessary to create an environment where fans can act with peace of mind.

[1350] 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.

[1351] In this invention, the server includes a means for collecting new messages from idols' and celebrities' posting accounts, a means for inputting the collected messages into a generative AI model and analyzing the context and sentiment, and a means for inputting the analyzed messages into a multilingual translation model for automatic translation. This allows fans to understand idols' and celebrities' SNS messages in real time even in the event of a disaster, providing an environment where they can feel safe and secure without feeling lonely or anxious. Furthermore, by providing a means for collecting necessary disaster information from various disaster information sources and distributing it to users, it is possible to quickly provide necessary disaster information and create an environment where users can act with peace of mind.

[1352] "Idols and celebrities" are people who are widely recognized by the public and famous for their entertainment or social activities.

[1353] "Posting Account" means an account on a social networking site or other social media platform used by an individual or entity to post messages or content.

[1354] A "generative AI model" is an algorithm or program that uses artificial intelligence techniques to analyze data and understand context and emotions.

[1355] A "multilingual translation model" is an artificial intelligence model that performs automatic translation between different languages ​​and is used to convert content from one language to another.

[1356] A "recording medium" refers to a device or system for storing data or information, and includes databases, hard disks, flash memory, etc.

[1357] A "communication terminal" is a device that a user uses to send and receive information, and includes smartphones, tablets, computers, etc.

[1358] "Operation history" is a record of the operations performed by a user when using the system, and includes login history, click history, posting history, etc.

[1359] "Recommended content" refers to appropriate and optimal content such as music, video, and text selected by the system based on the user's behavior and status.

[1360] "Disaster information sources" are channels for disseminating disaster-related information provided by government agencies, public organizations, and reliable private organizations.

[1361] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[1362] This invention relates to a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[1363] Hardware and software used

[1364] 1. Server

[1365] Use the API key to get SNS messages.

[1366] It uses generative AI models such as the Hugging Face sentiment analysis model and the Google Translate API.

[1367] A database (e.g., MySQL, PostgreSQL) is used as the recording medium for storing data.

[1368] 2. Terminal

[1369] Use a smartphone, tablet or computer to run the application.

[1370] 3. Users

[1371] A user of a system who requests and receives information through an application.

[1372] Data processing and calculation

[1373] 1. Collecting social media messages

[1374] The server authenticates to the SNS API using the API key and secret, periodically collects new messages, and stores them in a database.

[1375] 2. Message Analysis and Translation

[1376] The server inputs the collected messages into a generative AI model to analyze the context and sentiment. Based on the analysis results, the messages are input into a multilingual translation model to translate them from Japanese to English, Chinese, Korean, etc.

[1377] 3. Provision to Users

[1378] When a user requests a translation message through the application, the server retrieves the corresponding message from the database and sends it to the terminal, which then displays the translation message.

[1379] 4. Understanding anxieties and worries and proposing content

[1380] When a user enters their post content into the application, the device sends it to the server, which then uses a generative AI model to analyze their anxieties and worries. Based on the analysis results, the server selects the most appropriate content (e.g., uplifting songs or videos) and sends it to the device.

[1381] 5. Disaster response

[1382] The server detects when a user's regular activity has stopped and provides free live video and music.

[1383] The terminal receives and displays this information, allowing the user to use it with peace of mind.

[1384] 6. Collection and distribution of disaster information

[1385] The server accesses APIs for government and various disaster information sources, collects new disaster information, organizes it, and distributes it to users.

[1386] The terminal displays the received information on the screen, allowing the user to respond promptly.

[1387] Specific examples

[1388] Example 1: Collecting and translating social media messages

[1389] The server collects new messages from idols' social media accounts via API. The collected messages are analyzed for emotion using the Hugging Face model and then translated into multiple languages ​​using the Google Translate API. The translated results are stored in a database, and when a user makes a request through the app, the server retrieves the corresponding message, sends it to the device, and displays it.

[1390] Example 2: Understanding Anxiety and Proposing Content

[1391] When a user posts in the app, "I've been feeling more and more anxious lately," the device sends the post to the server, where it is analyzed by the generative AI model. The server then selects upbeat songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content, which the user can view or play.

[1392] Prompt Sentence Examples

[1393] "Get the latest tweets from Twitter, perform sentiment analysis, and translate them from Japanese to English."

[1394] "Please use AI to analyze user posts and recommend appropriate songs and videos."

[1395] Through the specific processing described above, the present invention is a system that can provide an environment in which fans can feel safe even in the event of a disaster and quickly deliver necessary information.

[1396] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1397] Step 1:

[1398] The server uses the social networking service's API to collect new messages from idol and celebrity posting accounts.

[1399] Input: SNS API key and secret, and the ID of the specific account.

[1400] Specific operation: The server authenticates with SNS using the API key and secret and sends a request to the "statuses / user_timeline" endpoint.

[1401] Data processing: Message data obtained from SNS is received in JSON format.

[1402] Output: The retrieved SNS message.

[1403] Step 2:

[1404] The server inputs the collected social media messages into a generative AI model and analyzes the context and emotions.

[1405] Input: Collected SNS messages.

[1406] What it does: The server analyzes the message using Hugging Face's sentiment analysis model.

[1407] Data computation: AI models analyze the context and sentiment of messages and assign sentiment labels as positive, negative, or neutral.

[1408] Output: Social media messages with sentiment labels.

[1409] Step 3:

[1410] The server inputs the emotion-analyzed message into a multilingual translation model for automatic translation.

[1411] Input: SNS messages with sentiment labels.

[1412] Specific operation: The server uses the Google Translate API to translate the message from Japanese to other languages ​​(English, Chinese, Korean, etc.).

[1413] Data processing: The model translates the text and generates messages in multiple languages.

[1414] Output: Multilingual translated message.

[1415] Step 4:

[1416] The server stores the translation results in a recording medium.

[1417] Input: A message translated into multiple languages.

[1418] Specific operation: The server stores the translation results in a database (e.g., MySQL, PostgreSQL).

[1419] Data processing: Store the message ID and the corresponding translation text in the appropriate table.

[1420] Output: The translated message stored in the database.

[1421] Step 5:

[1422] The user requests a social media message from their favorite person using an application on their device.

[1423] Input: User request (e.g. Idol A's recent posts).

[1424] What happens: A user clicks a button in the app, and a request is sent from the device to the server.

[1425] Data Calculation: The server retrieves the corresponding translation message from the database upon request.

[1426] Output: The translated message is sent from the server to the device.

[1427] Step 6:

[1428] The terminal displays the received translated message on the screen and provides it to the user.

[1429] Input: The translated message sent from the server.

[1430] Specific operation: The terminal displays the received message on the application screen.

[1431] Output: The translated message that is visible to the user.

[1432] Step 7:

[1433] Users post their concerns and worries within the application.

[1434] Input: A text post typed by the user (e.g., "I've been worrying a lot lately").

[1435] Specific operation: The user clicks the post button, and the post content is sent from the device to the server.

[1436] Output: The post sent to the server.

[1437] Step 8:

[1438] The server inputs the submitted post content into a generative AI model to understand the person's anxieties and worries.

[1439] Input: User submissions.

[1440] What happens: The server parses the post using a natural language processing model (e.g., the BERT model).

[1441] Data calculation: The model extracts keywords related to anxiety and worries from the content of the post.

[1442] Output: Analysis results identifying anxieties and worries.

[1443] Step 9:

[1444] The server selects the most appropriate content based on the user's anxieties and worries and sends it to the device.

[1445] Input: Analysis results of anxiety and worries.

[1446] Specific operation: The server searches the database for upbeat songs and videos and selects the most suitable content.

[1447] Data processing: Obtain information about the selected content and send it to the device.

[1448] Output: The selected content is sent to the device.

[1449] Step 10:

[1450] The terminal displays the selected content on the screen for the user to view or play.

[1451] Input: Content information sent from the server.

[1452] Specific operation: The terminal displays the received content on the application screen and provides a play button.

[1453] Output: Content that can be viewed and played by the user.

[1454] Step 11:

[1455] The server accesses the API of the disaster information source to collect the latest disaster information.

[1456] Input: API key and endpoint of disaster information source.

[1457] Specific operation: The server sends a request to the API to obtain new disaster information.

[1458] Data processing: Receive disaster information in JSON format and organize it appropriately.

[1459] Output: Retrieved and organized disaster information.

[1460] Step 12:

[1461] The server distributes the organized disaster information to users.

[1462] Input: Organized disaster information.

[1463] Specific operation: The server identifies users in the affected area and registers them in a distribution list for disaster information.

[1464] Data Processing: Generate a unique notification message and send it to the corresponding user.

[1465] Output: Disaster information notification sent to the user's device.

[1466] Step 13:

[1467] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1468] Input: Disaster information notification sent from the server.

[1469] Specific operation: The terminal displays the received information on the application screen and notifies the user.

[1470] Output: A screen displaying disaster information and user response preparation.

[1471] (Application example 1)

[1472] 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."

[1473] Many fans tend to feel lonely and anxious during disasters, and there is a need for a system to alleviate this psychological burden. However, many current systems are unable to respond quickly and appropriately due to language barriers and a lack of information. Furthermore, they do not adequately provide content tailored to the psychological state of fans, and are unable to provide sufficient emotional support. This invention aims to utilize AI technology to translate multiple languages, grasp psychological states, and quickly distribute information during disasters, thereby providing an environment where fans can feel safe.

[1474] 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.

[1475] In this invention, the server includes: means for collecting new messages from the social media accounts of idols and celebrities; means for inputting the collected messages into an automatic analysis model and analyzing the context and sentiment; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing the multilingual translated messages to a terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing free viewing content to a terminal when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and delivering it to users; means for using a generative AI model to provide recommended content; and means for creating prompts for the generative AI model. This allows fans to feel safe and secure even during disasters without worrying about information.

[1476] "Idols and celebrities" generally refer to individuals who have a large number of fans and social influence.

[1477] "Social media account" refers to an account held by an individual or company within a social networking service for disseminating information, whether public or private.

[1478] "New messages" refers to the latest posts, comments, tweets, and other information from social media accounts.

[1479] An "automatic analysis model" refers to an algorithm that uses machine learning and natural language processing technology to analyze input text data and parse its content and emotions.

[1480] "Context and sentiment" refers to the meaning of the information contained within a text message and its emotional tenor, such as positive, negative, or neutral.

[1481] "Multilingual translation model" refers to an algorithm that uses artificial intelligence technology to automatically translate from a specific language to another language.

[1482] "Database" refers to a system for storing and managing data in a structured format.

[1483] A "terminal" refers to a device with communication capabilities that can be operated by a user, such as a smartphone, tablet, or personal computer.

[1484] "User posts and behavioral history" refers to data such as text, images, and activity logs posted by users on social media or within applications.

[1485] "Understanding anxieties and worries" refers to analyzing the content posted by users and their behavioral history to identify their mental state.

[1486] "Recommended content" refers to information such as songs, videos, articles, etc. that are deemed most suitable based on the user's emotions and interests.

[1487] "Detecting a cessation of user activity when a disaster occurs" means that the system detects a sudden decrease in the frequency of normal logins and posts.

[1488] "Providing viewing content free of charge" refers to providing entertainment content such as live footage and music without charging fees in the event of a disaster.

[1489] "Disaster information sources" refers to up-to-date information about disasters provided by government agencies and disaster information services.

[1490] A "generative AI model" refers to an artificial intelligence system that uses machine learning and deep learning to generate information and content in response to user requests.

[1491] A "prompt sentence" refers to an input sentence that causes a generative AI model to perform a specific task.

[1492] The present invention provides a system that reduces the loneliness and anxiety felt by users when a disaster occurs, and provides a safe environment. Specific embodiments of the system are described below.

[1493] System Configuration

[1494] This system consists of the following elements:

[1495] server

[1496] Terminal

[1497] User

[1498] Hardware and software used

[1499] API: Twitter API, YouTube API

[1500] AI analysis model: Google Cloud Natural Language API

[1501] Multilingual Translation: Google Translation API

[1502] Database: Firebase Realtime Database

[1503] Frontend: React Native for mobile app

[1504] System processing overview

[1505] 1. Collecting social media messages

[1506] The server uses the Twitter API to periodically collect new messages from the social media accounts of idols and celebrities.

[1507] 2. Message Analysis

[1508] The server analyzes the collected messages using the Google Cloud Natural Language API to analyze context and sentiment.

[1509] 3. Multilingual Translation

[1510] The server automatically translates the parsed messages into multiple languages ​​using the Google Translation API, and the translated messages are stored in the Firebase Realtime Database.

[1511] 4. Provision to Users

[1512] The server provides the multilingual translated message to the terminal when requested by the user through the terminal, and the terminal displays the message to the user.

[1513] 5. Understanding anxieties and worries and proposing content

[1514] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries and selects recommended content based on that information. The selected content is then provided to the user via the device.

[1515] 6. Providing free content during disasters

[1516] The server detects when a user's regular activities (logins and posts) are interrupted during a disaster. If this is detected, the server provides free viewing content (live video and music) to the device, allowing the user to use it.

[1517] 7. Collection and distribution of disaster information

[1518] The server accesses the APIs of various disaster information sources to collect the latest disaster information, which is then organized and quickly distributed to users in the affected areas.

[1519] Specific examples

[1520] Collecting and translating social media messages

[1521] The server collects new tweets from idols' and celebrities' social media accounts and assigns them sentiment labels. The server then translates the messages into multiple languages ​​(English, Chinese, Korean, etc.) and stores them in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message and sends it to the device. The device then displays the translated message.

[1522] Providing content based on user emotions

[1523] When a user posts "I've been feeling down lately" from their device, the device sends the post to a server. The server uses an AI analysis model to analyze the post and identify the anxiety. The server then selects uplifting songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content for the user to view and play.

[1524] Prompt Sentence Examples

[1525] "Please suggest appropriate content (uplifting songs or videos) in response to the recent user post, 'I've been feeling down lately.'"

[1526] The above is an embodiment of the present invention. This system allows fans to feel at ease even in the event of a disaster and to quickly obtain the information they need.

[1527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1528] Step 1:

[1529] Collecting SNS messages

[1530] The server periodically collects new messages from idols and celebrities using the APIs of their social media accounts. Specifically, it uses the Twitter API to retrieve the latest tweets from the accounts. The input is the response from the social media API, and the output is the retrieved messages.

[1531] Step 2:

[1532] Message Parsing

[1533] The server inputs the collected messages into the Google Cloud Natural Language API to analyze the context and sentiment. This process determines whether the message contains positive, negative, or neutral sentiment. The input is the collected messages, and the output is messages with sentiment labels attached. Specifically, the server sends messages to the Natural Language API and receives sentiment labels in response.

[1534] Step 3:

[1535] Multilingual Translation

[1536] The server uses the Google Translation API to translate the analyzed message into multiple languages. The target languages ​​for translation include English, Chinese, and Korean. The input is a message with an emotion label attached, and the output is a multilingual translation of the message. Specifically, the server sends the message to the Translation API and receives the translated text in each language.

[1537] Step 4:

[1538] Saving to a database

[1539] The server stores the translated messages in the Firebase Realtime Database. The input is the multilingual translated message, and the output is a data entry stored in the database. Specifically, the translated message is written to the database and a new data entry is created.

[1540] Step 5:

[1541] Provision to users

[1542] When a user requests a translation message through a terminal, the server retrieves the corresponding translation message from the database and sends it to the terminal. The input is the user request, and the output is the translation message. Specifically, the server queries the database based on the request and sends the result to the terminal.

[1543] Step 6:

[1544] Understanding anxieties and worries

[1545] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries. The input is the user's posts and behavioral history, and the output is the identified anxieties and worries. Specifically, the collected data is sent to the server, where it is analyzed.

[1546] Step 7:

[1547] Content Suggestion

[1548] The server selects recommended content, such as uplifting music or videos, based on the analyzed anxieties and worries. The selected content is provided to the user via the device. The input is the identified anxieties and worries, and the output is the selected content. Specifically, the server creates a prompt for the generative AI model, inputs the analysis results, and generates appropriate content.

[1549] Step 8:

[1550] Disaster activity detection

[1551] The server detects when a user's regular activity (logins or posts) has ceased. The input is the login and post activity history, and the output is the result of the detection of the cessation of activity. Specifically, it periodically checks the user's activity log and confirms the cessation of activity if there has been no activity for a certain period of time.

[1552] Step 9:

[1553] Providing free content during disasters

[1554] The server provides free content to inactive users. The input is the result of the inactivity detection, and the output is free content. Specifically, the server provides live video and music data via a generative AI model.

[1555] Step 10:

[1556] Collection and distribution of disaster information

[1557] The server accesses the APIs of various disaster information sources, collects and organizes the latest disaster information, and then distributes the organized information to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information. Specifically, the server obtains information from the APIs and notifies users of the organized information in real time.

[1558] The above are the specific processing steps for carrying out the present invention. The input, data processing, and output performed at each step have been described in detail, and specific operations have been shown.

[1559] 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.

[1560] This invention combines an emotion engine with a system that provides a safe environment for fans in the event of a disaster, preventing them from feeling lonely or anxious. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and in addition to translating them into multiple languages, it also has an emotion engine that recognizes the user's emotions, aiming to provide more appropriate content.

[1561] This system is implemented by the following means:

[1562] 1. Collecting social media messages

[1563] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[1564] 2. AI-powered message analysis and translation

[1565] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, generating emotion labels such as "positive," "negative," and "neutral."

[1566] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[1567] The server stores the translated messages in a database.

[1568] 3. Providing multilingual translated messages

[1569] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[1570] The terminal sends a request to the server.

[1571] The server retrieves the corresponding translated message from the database.

[1572] The server transmits the obtained translated message to the terminal.

[1573] The terminal displays the received translated message on the screen.

[1574] 4. User Emotion Recognition by Emotion Engine

[1575] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[1576] The terminal collects the content posted by the user and transmits it to the server.

[1577] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[1578] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[1579] 5. Understanding anxieties and worries and proposing content

[1580] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[1581] The server transmits the selected content to the terminal.

[1582] The terminal displays and plays the selected content on the screen.

[1583] 6. Providing free content during disasters

[1584] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[1585] The server selects live videos and music to provide to the user free of charge.

[1586] The server transmits the selected free content to the terminal.

[1587] The terminal is enabled to display and play the received free content on the screen.

[1588] 7. Rapid collection and distribution of disaster information

[1589] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[1590] The server will then properly organize and classify the collected disaster information.

[1591] The server distributes organized disaster information to users in the affected areas.

[1592] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1593] Specific examples

[1594] Example 1: Collecting and translating social media messages

[1595] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[1596] Example 2: Understanding Anxiety and Proposing Content

[1597] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[1598] Example 3: Using an Emotion Engine

[1599] If a user posts a voice message within the app and the message is negative, such as "I'm so tired," the device collects the voice data and sends it to the server. The server uses an emotion engine to analyze the voice data and determine that the user is tired. The server selects relaxing music or an uplifting message to soothe the user and sends it to the device. The device then displays and plays this content.

[1600] As described above, by combining emotion engines, it is possible to more accurately grasp the user's emotions and respond to them individually, thereby providing psychological support even in the event of a disaster.

[1601] The processing flow will be explained below.

[1602] Processing flow of the system including the emotion engine

[1603] SNS message collection

[1604] Step 1:

[1605] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[1606] Step 2:

[1607] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[1608] Step 3:

[1609] The server stores the retrieved new messages in a database.

[1610] AI-powered message analysis and translation

[1611] Step 1:

[1612] The server reads the collected SNS messages from the database.

[1613] Step 2:

[1614] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[1615] Step 3:

[1616] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[1617] Step 4:

[1618] The server stores the translated messages in a database.

[1619] Providing multilingual translated messages

[1620] Step 1:

[1621] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[1622] Step 2:

[1623] The terminal sends a request to the server.

[1624] Step 3:

[1625] The server retrieves the corresponding translated message from the database.

[1626] Step 4:

[1627] The server transmits the obtained translated message to the terminal.

[1628] Step 5:

[1629] The terminal displays the received translated message on the screen.

[1630] Recognizing user emotions with an emotion engine

[1631] Step 1:

[1632] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[1633] Step 2:

[1634] The terminal collects the content posted by the user and transmits it to the server.

[1635] Step 3:

[1636] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[1637] Step 4:

[1638] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[1639] Understanding anxieties and worries and proposing content

[1640] Step 1:

[1641] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[1642] Step 2:

[1643] The server transmits the selected content to the terminal.

[1644] Step 3:

[1645] The terminal displays and plays the selected content on the screen.

[1646] Providing free content during disasters

[1647] Step 1:

[1648] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[1649] Step 2:

[1650] The server creates a list of users who have lost contact when a disaster occurs.

[1651] Step 3:

[1652] The server selects live videos and music to offer to the user free of charge.

[1653] Step 4:

[1654] The server transmits the selected free content to the terminal.

[1655] Step 5:

[1656] The terminal is enabled to display and play the received free content on the screen.

[1657] Rapid collection and distribution of disaster information

[1658] Step 1:

[1659] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[1660] Step 2:

[1661] The server will then properly organize and classify the collected disaster information.

[1662] Step 3:

[1663] The server distributes organized disaster information to users in the affected areas.

[1664] Step 4:

[1665] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1666] Example 2

[1667] 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."

[1668] In recent years, with the spread of social networking services (SNS), messages from idols and celebrities have become an important source of information for users. However, these messages are not available in multiple languages ​​and lack emotional support. Furthermore, in the event of a disaster, users are likely to face an environment where they feel anxious and lonely. In such situations, there is a need for a system that can provide users with psychological support and prompt information.

[1669] 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 collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing multilingual translated messages to users' communication devices; means for collecting user posts and behavioral history to understand their anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for analyzing users' emotions using an emotion engine; means for detecting a cessation of user activity when a disaster occurs; means for providing live video and music to users' communication devices free of charge when a disaster occurs; and means for collecting necessary disaster information from various disaster information sources and distributing it to users. This allows users to understand messages from idols and celebrities in multiple languages, reducing anxiety and loneliness in everyday life and during disasters, and receiving emotional support.

[1670] "Idols and celebrities" refer to individuals who are recognized by many people and have social influence.

[1671] A "social networking service account" is an authenticated user profile used by an individual or organization to disseminate and share information on a social networking service.

[1672] An "artificial intelligence analysis model" refers to a program or system that uses AI technology to analyze text and data and understand its context and emotions.

[1673] A "multilingual translation model" is a program or system for automatically translating text into different languages.

[1674] A "database" is an information system that efficiently stores large amounts of data and allows them to be quickly retrieved when needed.

[1675] "Communication devices" are devices such as smartphones, tablets, and personal computers that are used to send and receive information via the Internet or other communication networks.

[1676] "Posted content" refers to all information such as text, images, audio, and video that users share through social media or applications.

[1677] "Behavioral history" refers to a record of all actions a user takes within a social networking site or application, such as posting messages, viewing content, clicking "like," etc.

[1678] An "emotion engine" is a system that automatically analyzes a user's emotions from text, voice, images, etc. and outputs the results.

[1679] "Disaster information sources" refer to disaster-related information provided by government agencies, public institutions, and other reliable organizations.

[1680] An "application program interface (API)" refers to a defined method or interface that allows software to interact with other software and utilize its functionality.

[1681] The present invention combines an emotion engine with a system that provides a safe environment in the event of a disaster, preventing fans from feeling lonely or anxious. A specific embodiment of this system is described below.

[1682] Overall system overview

[1683] The system operates around a server, devices, and users. The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, and analyzes their context and sentiment using an artificial intelligence analysis model. The server also inputs the messages into a multilingual translation model for automatic translation, and stores the translation results in a database. Users can then view these messages using a dedicated application.

[1684] Hardware and Software Configuration

[1685] server

[1686] The server is hardware with a powerful processor and large memory capacity, and runs the following software components:

[1687] SNS Collection Module: Collects messages using the SNS API.

[1688] Artificial intelligence analysis models: For example, using the BERT model to analyze the context and sentiment of messages.

[1689] Multilingual translation model: Translate messages into multiple languages ​​using Google Translate API and DeepL Translator.

[1690] Database system: stores the collected and analyzed messages.

[1691] Terminal

[1692] The device is a user's communication device such as a smartphone, tablet, or PC. It has a dedicated application installed and provides the following functions:

[1693] Message request function: Users request translation messages.

[1694] Message display function: Displays the translated message received from the server.

[1695] Posting function: Sends user posts to the server.

[1696] User operation procedure

[1697] Users can launch the dedicated application and read the latest social media messages of idols and celebrities, translating them. Users can also post about their feelings and situations in the application, and the system will analyze the user's emotions and suggest appropriate content.

[1698] Specific examples

[1699] Example 1: Collecting and translating social media messages

[1700] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an artificial intelligence analysis model (e.g., the BERT model) and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[1701] Example 2: Understanding Anxiety and Proposing Content

[1702] A user posts in the app, "I've been feeling a lot of anxiety lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease the anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[1703] Prompt Sentence Examples

[1704] Request a translation of Idol A's latest tweet.

[1705] There's been a lot of anxiety lately, so please provide some uplifting content.

[1706] Analyze my voice messages to understand my emotional state and recommend relaxing music.

[1707] In this way, by combining AI and an emotion engine, the system of the present invention can accurately grasp the user's emotions and provide psychological support even in the event of a disaster.

[1708] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1709] Specific processing steps of the program

[1710] Step 1: Collect SNS messages

[1711] The server collects new messages from the social networking service accounts of idols and celebrities.

[1712] Input: Your SNS API key and account information.

[1713] Processing: The server retrieves the message using an API, for example calling the Twitter API GET / 2 / tweets endpoint to get the latest tweets.

[1714] Output: A new SNS message (e.g., tweet data in JSON format).

[1715] Specific operation: The server periodically calls the Twitter API, retrieves the latest messages, and temporarily stores them.

[1716] Step 2: Analyzing the message with AI

[1717] The server inputs the collected SNS messages into an AI analysis model and assigns emotion labels.

[1718] Input: Collected SNS messages (e.g. tweet data in JSON format).

[1719] Processing: The message is fed into an AI analysis model, such as the BERT model, to analyze the context and sentiment, which then generates a sentiment label, such as "positive," "negative," or "neutral."

[1720] Output: Message data with sentiment labels.

[1721] Specific operation: The server calls the BERT model to analyze SNS messages and assign sentiment labels.

[1722] Step 3: Translate your messages into multiple languages

[1723] The server inputs the parsed message into a multilingual translation model and translates it into the specified language.

[1724] Input: Message data with emotion labels.

[1725] Processing: Input the message into Google Translate API or DeepL Translator and translate it into the specified language, such as English, Chinese, or Korean.

[1726] Output: Multilingual translated message data.

[1727] Specific operation: The server uses the translation API to translate the message into a different language and obtains the translation result.

[1728] Step 4: Save the translated message

[1729] The server stores the translated messages in a database.

[1730] Input: Multilingual translated message data.

[1731] Processing: Store the translated messages in a database system.

[1732] Output: The translated message stored in the database.

[1733] What happens: The server connects to the database and executes a SQL query to store the translated message.

[1734] Step 5: Providing multilingual translated messages

[1735] The user launches the app from their communication device and requests a translation of a social media message.

[1736] Input: User request (e.g. "Show me the translation of idol A's latest tweet in English").

[1737] Process: The device sends a request to the server, which retrieves the corresponding translation message from the database and sends it to the device.

[1738] Output: The translated message that will be displayed on the user's communication device.

[1739] What happens: The server executes a database query to retrieve the message and sends it to the device, which then displays the received translated message on its screen.

[1740] Step 6: Recognizing user emotions with the emotion engine

[1741] Users post about their feelings and situations on social media or within apps.

[1742] Input: User post (e.g., "I've been feeling a lot of anxiety lately").

[1743] Processing: The device collects the posted content and sends it to the server. The server inputs the posted content into the emotion engine and analyzes the user's emotions.

[1744] Output: Post data with sentiment labels.

[1745] Specific operation: The server calls the emotion engine, analyzes the user's post content, and assigns emotion labels.

[1746] Step 7: Understanding anxieties and worries and proposing content

[1747] The server selects the most suitable content based on the analysis results and provides it to the user.

[1748] Input: Post data with sentiment labels.

[1749] Processing: The server selects the most suitable content (e.g., fun videos or uplifting music) from the database.

[1750] Output: Content sent to the user's communication device.

[1751] Specific operations: The server executes a database query to select content and sends it to the device, which then displays or plays the received content.

[1752] Step 8: Providing free content during disasters

[1753] The server detects that user activity has ceased when a disaster occurs.

[1754] Input: User activity log (e.g. login and posting frequency).

[1755] Processing: The server analyzes disaster information and user activity logs, receives notifications when activity stops, selects free content, and sends it to the device.

[1756] Output: Free content sent to the user's communication device.

[1757] Specific operation: The server monitors the user's activity based on disaster information, and if the activity stops, it selects free live video or music and sends it to the device.

[1758] Step 9: Rapid collection and distribution of disaster information

[1759] The server accesses the API of the disaster information source to collect new disaster information.

[1760] Input: API key and endpoint information for the disaster information source.

[1761] Processing: The server calls the API to obtain the latest disaster information, organizes and categorizes it appropriately, and delivers the organized information to users.

[1762] Output: Disaster information delivered to users' communication devices.

[1763] Specific operation: The server collects disaster information from public institution APIs and distributes organized information to users in the affected areas. The device notifies the user of the received information.

[1764] The above is the specific processing flow of the system.

[1765] (Application example 2)

[1766] 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."

[1767] When a disaster occurs, providing a safe environment where users can feel free to avoid feeling lonely or anxious is an important issue. While existing systems provide disaster information and multilingual translation, they lack the ability to grasp the user's emotional state and provide appropriate content accordingly. Furthermore, there are insufficient effective support methods that can provide a sense of security free of charge when a disaster occurs. Therefore, a system that can respond to users' emotions and provide a sense of security in real time is needed.

[1768] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in an information recording device; means for providing the multilingual translated messages to a user's terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting optimal content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing video content and music content to a user's terminal free of charge when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and distributing it to users; and means for analyzing users' emotions when a disaster occurs and selecting and providing content that provides a sense of security. This makes it possible to create an environment in which users can feel safe even during a disaster by understanding the user's emotional state and providing appropriate content.

[1769] "Idol" refers to a talent or artist who is popular in a particular entertainment field.

[1770] A "celebrity" is someone who is widely known and active in fields such as entertainment, sports, politics, or business.

[1771] "Social networking service" refers to a web service or platform that enables users to communicate with other users online.

[1772] "Message" means information transmitted between Users in the form of text, images, audio, video, etc.

[1773] "Artificial intelligence analytical model" refers to the algorithms and programs used to analyze collected data and understand its context and sentiment.

[1774] A "multilingual translation model" refers to an algorithm or program that automatically translates a sentence in one language into another language.

[1775] "Information recording device" refers to a computer or medium for temporarily or permanently storing data.

[1776] "User" refers to an individual who uses a system or application.

[1777] "Device" refers to a computing device such as a smartphone, tablet, or PC.

[1778] "Posted content" refers to information such as text, images, audio, and video that users post on social media or applications.

[1779] "Behavioral history" refers to a record of a user's actions within a system or application.

[1780] "Anxiety" refers to feelings of worry and fear that users have about their safety and the future.

[1781] "Problems" refers to the problems and distress that users face.

[1782] "Content" refers to any form of information or entertainment (e.g., music, video, text) provided to a user.

[1783] "Means for detecting inactivity" refers to a system that detects when a user's normal activity has ceased for a period of time.

[1784] "Video content" refers to content that includes visual information such as videos and live footage.

[1785] "Music Content" refers to content that contains auditory information, such as music tracks or live music.

[1786] "Disaster information sources" refer to data sources that provide information about disasters, such as those provided by government agencies and disaster response organizations.

[1787] An "application program interface (API)" refers to the definitions and protocols that allow software to communicate and interact with each other.

[1788] "Means for analyzing emotions" refers to systems or methods that analyze users' posted content and behavioral history to identify their emotional state.

[1789] In this invention, the following system is constructed to provide a safe environment for users even in the event of a disaster. The system is composed of a server, a user terminal, and related software modules.

[1790] Server Roles

[1791] The server has the following functions:

[1792] 1. Message collection and analysis:

[1793] The server collects new messages from the social networking service (SNS) accounts of idols and celebrities, using the SNS API.

[1794] The collected messages are then fed into an artificial intelligence (AI) analysis model to analyze their context and sentiment, using natural language processing toolkits and machine learning models (e.g., Google BERT).

[1795] 2. Multilingual translation and storage:

[1796] The parsed message is input into a multilingual translation model and automatically translated into other languages ​​as specified, for example, using the Google Translation API.

[1797] The translation results are stored in an information recording device (database), for example, using a database management system such as MySQL or PostgreSQL.

[1798] 3. Understanding anxieties and worries and suggesting content:

[1799] The server collects users' posts and behavioral history, and uses an AI analysis model to identify their anxieties and worries.

[1800] Depending on the identified anxieties and worries, the most appropriate content (e.g., relaxing music, uplifting videos) is selected and provided to the user's device.

[1801] 4. Disaster response:

[1802] The server detects when user activity ceases in the event of a disaster and provides video and music content free of charge.

[1803] It also accesses the APIs of various disaster information sources to collect the latest disaster information, organize and classify it, and quickly deliver it to users in the affected areas.

[1804] 5. Sentiment analysis and reassurance:

[1805] When a disaster occurs, the server analyzes users' emotions and selects and provides content that will provide a sense of security.

[1806] Specifically, an artificial intelligence analysis model is used to analyze voice data and text and select appropriate content.

[1807] Device Role

[1808] The user's device works in conjunction with the server to perform the following tasks:

[1809] 1. Retrieving and displaying messages:

[1810] When a user requests a multilingual translated message, the terminal retrieves the corresponding translated message from the server and displays it on the screen.

[1811] 2. Posting and sending your concerns and worries:

[1812] The content posted by users and audio data are collected and sent to the server.

[1813] 3. Viewing and playing content:

[1814] The terminal receives the content provided by the server and displays or plays it on the screen.

[1815] Specific examples

[1816] Here's a concrete example of how this system can be used:

[1817] When a user posts in the app, "I've been feeling anxious lately," the device collects this content and sends it to the server. The server uses an emotion analysis engine to analyze the post and determine that the user is feeling anxious. The server then selects uplifting music and sends it to the user's device, which then plays it.

[1818] In addition, in the event of a disaster, the server will detect when users' normal activities have stopped and provide them with free relaxation music and live video footage.

[1819] Prompt Sentence Examples

[1820] Here are some example prompts to be input to the generative AI model that will help this system work:

[1821] "You feed user posts into an emotion engine to analyze the emotion labels (positive, negative, neutral). Then, you serve appropriate content (fun videos, relaxing music, etc.) based on the analyzed emotion labels."

[1822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1823] Step 1:

[1824] The server collects new messages from the idols' and celebrities' social networking service accounts. Specifically, it retrieves new posts and tweets using the SNS API. The input at this point is the SNS account information, and the output is the collected message data.

[1825] Step 2:

[1826] The server inputs the collected message data into an artificial intelligence analysis model to analyze the context and sentiment. Specifically, it uses a natural language processing toolkit or machine learning model (e.g., Google BERT) to analyze the message context and generate sentiment labels (e.g., "positive," "negative," or "neutral"). The input is the message data, and the output is the sentiment analysis results.

[1827] Step 3:

[1828] The server inputs the sentiment analysis results into a multilingual translation model and performs automatic translation. Specifically, it uses the Google Translation API to translate into the specified language (e.g., English, Chinese, Korean). The input is the analyzed message, and the output is the translated message.

[1829] Step 4:

[1830] The server stores the translated message in an information storage device. Specifically, it stores the translation results in a database management system (e.g., MySQL, PostgreSQL). The input is the translated message, and the output is the message stored in the database.

[1831] Step 5:

[1832] The device displays multilingual translated messages on the screen in response to user requests. Specifically, the user requests a message through the app and sends the request to the server. The input is the request, and the output is the displayed translated message.

[1833] Step 6:

[1834] The terminal collects user posts (e.g., "I've been feeling anxious lately") and sends them to the server. The input is the user's posts, and the output is the data sent to the server.

[1835] Step 7:

[1836] The server inputs the user's post content into an AI analysis model to analyze their anxieties and worries. Specifically, it uses natural language processing tools to analyze the post content and generate specific emotion labels. The input is the post content, and the output is the analyzed emotion label.

[1837] Step 8:

[1838] The server selects the most appropriate content based on the analysis results and provides it to the user's device. Specifically, it selects appropriate relaxation music or videos from a database and sends them to the device. The input is the emotion analysis results and the content database, and the output is the selected content.

[1839] Step 9:

[1840] The terminal displays and plays the transmitted content on the screen. The input is the content data, and the output is the played music or video.

[1841] Step 10:

[1842] The server detects when users stop their normal activities in the event of a disaster and provides them with free relaxation music and live video. The input is the detected activity stoppage data, and the output is the content to be provided.

[1843] Step 11:

[1844] The server accesses the APIs of various disaster information sources to collect the latest disaster information, organizes and classifies it, and quickly delivers it to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information.

[1845] Step 12:

[1846] The server analyzes users' emotions during a disaster and selects and provides content that provides a sense of security. Specifically, it analyzes voice data and text using an emotion analysis model, and selects and provides appropriate relaxation content. The input is emotional data during a disaster, and the output is content that provides a sense of security.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] [Fourth embodiment]

[1851] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1852] 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.

[1853] 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).

[1854] 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.

[1855] 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.

[1856] 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).

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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.

[1862] 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.

[1863] 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."

[1864] This invention is a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[1865] This system is implemented in particular by the following means:

[1866] 1. Collecting social media messages

[1867] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[1868] 2. AI-powered message analysis and translation

[1869] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, for example, assigning emotional labels such as positive, negative, or neutral.

[1870] The server inputs the analyzed message into a multilingual translation model and performs automatic translation, translating Japanese messages into English, Chinese, Korean, etc.

[1871] 3. Providing multilingual translated messages

[1872] The user requests a social media message from their favorite person using an application on their device.

[1873] The server retrieves the corresponding translated message from the database in response to the user's request and transmits it to the terminal.

[1874] The terminal displays the transmitted translated message on the screen and provides it to the user.

[1875] 4. Understanding fans' concerns and worries and proposing content

[1876] A user posts on social media or within an application, for example, "I've been feeling a lot of anxiety lately."

[1877] The terminal collects the content posted by the user and transmits it to the server.

[1878] The server inputs the user's submitted post into an AI analysis model to understand their anxieties and worries.

[1879] The server selects the most suitable song or video based on the user's anxieties and worries and sends it to the device.

[1880] The device will display the selected song or video for playback.

[1881] 5. Providing free content during disasters

[1882] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[1883] The server provides live video and music to its users free of charge.

[1884] The terminal displays the received free live video and music on the screen, making them available to the user.

[1885] 6. Rapid collection and distribution of disaster information

[1886] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[1887] The server will then properly organize the collected disaster information and deliver relevant information to users.

[1888] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1889] Specific examples

[1890] Example 1: Collecting and translating social media messages

[1891] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[1892] Example 2: Understanding Anxiety and Proposing Content

[1893] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an AI analysis model and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[1894] As described above, the system of the present invention allows fans to receive emotional support even in the event of a disaster, and can quickly provide necessary information.

[1895] The processing flow will be explained below.

[1896] SNS message collection

[1897] Step 1:

[1898] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[1899] Step 2:

[1900] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[1901] Step 3:

[1902] The server stores the retrieved new messages in a database.

[1903] AI-powered message analysis and translation

[1904] Step 1:

[1905] The server reads the collected SNS messages from the database.

[1906] Step 2:

[1907] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[1908] Step 3:

[1909] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[1910] Step 4:

[1911] The server stores the translated messages in a database.

[1912] Providing multilingual translated messages

[1913] Step 1:

[1914] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[1915] Step 2:

[1916] The terminal sends a request to the server.

[1917] Step 3:

[1918] The server retrieves the corresponding translated message from the database.

[1919] Step 4:

[1920] The server transmits the obtained translated message to the terminal.

[1921] Step 5:

[1922] The terminal displays the received translated message on the screen.

[1923] Understanding fans' concerns and worries and proposing content

[1924] Step 1:

[1925] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[1926] Step 2:

[1927] The terminal collects the content posted by the user and transmits it to the server.

[1928] Step 3:

[1929] The server inputs the posted content into an AI analysis model to analyze and identify the user's anxieties and worries.

[1930] Step 4:

[1931] The server selects from a database the most appropriate content to address the user's anxieties and worries (for example, fun videos or uplifting music).

[1932] Step 5:

[1933] The server transmits the selected content to the terminal.

[1934] Step 6:

[1935] The terminal displays and plays the selected content on the screen.

[1936] Providing free content during disasters

[1937] Step 1:

[1938] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[1939] Step 2:

[1940] The server creates a list of users who have lost contact when a disaster occurs.

[1941] Step 3:

[1942] The server selects live videos and music to offer to the user free of charge.

[1943] Step 4:

[1944] The server transmits the selected free content to the terminal.

[1945] Step 5:

[1946] The terminal is enabled to display and play the received free content on the screen.

[1947] Rapid collection and distribution of disaster information

[1948] Step 1:

[1949] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[1950] Step 2:

[1951] The server will then properly organize and classify the collected disaster information.

[1952] Step 3:

[1953] The server distributes organized disaster information to users in the affected areas.

[1954] Step 4:

[1955] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[1956] Example 1

[1957] 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."

[1958] In the event of a disaster, there are currently insufficient methods to provide a safe environment where fans can feel free to avoid feeling isolated or anxious. Furthermore, since it is difficult to understand the content of social media messages in foreign languages ​​and there are limited ways to quickly obtain necessary disaster information, it is necessary to create an environment where fans can act with peace of mind.

[1959] 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.

[1960] In this invention, the server includes a means for collecting new messages from idols' and celebrities' posting accounts, a means for inputting the collected messages into a generative AI model and analyzing the context and sentiment, and a means for inputting the analyzed messages into a multilingual translation model for automatic translation. This allows fans to understand idols' and celebrities' SNS messages in real time even in the event of a disaster, providing an environment where they can feel safe and secure without feeling lonely or anxious. Furthermore, by providing a means for collecting necessary disaster information from various disaster information sources and distributing it to users, it is possible to quickly provide necessary disaster information and create an environment where users can act with peace of mind.

[1961] "Idols and celebrities" are people who are widely recognized by the public and famous for their entertainment or social activities.

[1962] "Posting Account" means an account on a social networking site or other social media platform used by an individual or entity to post messages or content.

[1963] A "generative AI model" is an algorithm or program that uses artificial intelligence techniques to analyze data and understand context and emotions.

[1964] A "multilingual translation model" is an artificial intelligence model that performs automatic translation between different languages ​​and is used to convert content from one language to another.

[1965] A "recording medium" refers to a device or system for storing data or information, and includes databases, hard disks, flash memory, etc.

[1966] A "communication terminal" is a device that a user uses to send and receive information, and includes smartphones, tablets, computers, etc.

[1967] "Operation history" is a record of the operations performed by a user when using the system, and includes login history, click history, posting history, etc.

[1968] "Recommended content" refers to appropriate and optimal content such as music, video, and text selected by the system based on the user's behavior and status.

[1969] "Disaster information sources" are channels for disseminating disaster-related information provided by government agencies, public organizations, and reliable private organizations.

[1970] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[1971] This invention relates to a system that provides a safe environment for fans, preventing them from feeling lonely or anxious, even in the event of a disaster. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and translates them into multiple languages. It also identifies fans' anxieties and concerns, providing appropriate recommended content and offering free live video and music in the event of a disaster. It also has the ability to quickly collect and distribute necessary disaster information.

[1972] Hardware and software used

[1973] 1. Server

[1974] Use the API key to get SNS messages.

[1975] It uses generative AI models such as the Hugging Face sentiment analysis model and the Google Translate API.

[1976] A database (e.g., MySQL, PostgreSQL) is used as the recording medium for storing data.

[1977] 2. Terminal

[1978] Use a smartphone, tablet or computer to run the application.

[1979] 3. Users

[1980] A user of a system who requests and receives information through an application.

[1981] Data processing and calculation

[1982] 1. Collecting social media messages

[1983] The server authenticates to the SNS API using the API key and secret, periodically collects new messages, and stores them in a database.

[1984] 2. Message Analysis and Translation

[1985] The server inputs the collected messages into a generative AI model to analyze the context and sentiment. Based on the analysis results, the messages are input into a multilingual translation model to translate them from Japanese to English, Chinese, Korean, etc.

[1986] 3. Provision to Users

[1987] When a user requests a translation message through the application, the server retrieves the corresponding message from the database and sends it to the terminal, which then displays the translation message.

[1988] 4. Understanding anxieties and worries and proposing content

[1989] When a user enters their post content into the application, the device sends it to the server, which then uses a generative AI model to analyze their anxieties and worries. Based on the analysis results, the server selects the most appropriate content (e.g., uplifting songs or videos) and sends it to the device.

[1990] 5. Disaster response

[1991] The server detects when a user's regular activity has stopped and provides free live video and music.

[1992] The terminal receives and displays this information, allowing the user to use it with peace of mind.

[1993] 6. Collection and distribution of disaster information

[1994] The server accesses APIs for government and various disaster information sources, collects new disaster information, organizes it, and distributes it to users.

[1995] The terminal displays the received information on the screen, allowing the user to respond promptly.

[1996] Specific examples

[1997] Example 1: Collecting and translating social media messages

[1998] The server collects new messages from idols' social media accounts via API. The collected messages are analyzed for emotion using the Hugging Face model and then translated into multiple languages ​​using the Google Translate API. The translated results are stored in a database, and when a user makes a request through the app, the server retrieves the corresponding message, sends it to the device, and displays it.

[1999] Example 2: Understanding Anxiety and Proposing Content

[2000] When a user posts in the app, "I've been feeling more and more anxious lately," the device sends the post to the server, where it is analyzed by the generative AI model. The server then selects upbeat songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content, which the user can view or play.

[2001] Prompt Sentence Examples

[2002] "Get the latest tweets from Twitter, perform sentiment analysis, and translate them from Japanese to English."

[2003] "Please use AI to analyze user posts and recommend appropriate songs and videos."

[2004] Through the specific processing described above, the present invention is a system that can provide an environment in which fans can feel safe even in the event of a disaster and quickly deliver necessary information.

[2005] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2006] Step 1:

[2007] The server uses the social networking service's API to collect new messages from idol and celebrity posting accounts.

[2008] Input: SNS API key and secret, and the ID of the specific account.

[2009] Specific operation: The server authenticates with SNS using the API key and secret and sends a request to the "statuses / user_timeline" endpoint.

[2010] Data processing: Message data obtained from SNS is received in JSON format.

[2011] Output: The retrieved SNS message.

[2012] Step 2:

[2013] The server inputs the collected social media messages into a generative AI model and analyzes the context and emotions.

[2014] Input: Collected SNS messages.

[2015] What it does: The server analyzes the message using Hugging Face's sentiment analysis model.

[2016] Data computation: AI models analyze the context and sentiment of messages and assign sentiment labels as positive, negative, or neutral.

[2017] Output: Social media messages with sentiment labels.

[2018] Step 3:

[2019] The server inputs the emotion-analyzed message into a multilingual translation model for automatic translation.

[2020] Input: SNS messages with sentiment labels.

[2021] Specific operation: The server uses the Google Translate API to translate the message from Japanese to other languages ​​(English, Chinese, Korean, etc.).

[2022] Data processing: The model translates the text and generates messages in multiple languages.

[2023] Output: Multilingual translated message.

[2024] Step 4:

[2025] The server stores the translation results in a recording medium.

[2026] Input: A message translated into multiple languages.

[2027] Specific operation: The server stores the translation results in a database (e.g., MySQL, PostgreSQL).

[2028] Data processing: Store the message ID and the corresponding translation text in the appropriate table.

[2029] Output: The translated message stored in the database.

[2030] Step 5:

[2031] The user requests a social media message from their favorite person using an application on their device.

[2032] Input: User request (e.g. Idol A's recent posts).

[2033] What happens: A user clicks a button in the app, and a request is sent from the device to the server.

[2034] Data Calculation: The server retrieves the corresponding translation message from the database upon request.

[2035] Output: The translated message is sent from the server to the device.

[2036] Step 6:

[2037] The terminal displays the received translated message on the screen and provides it to the user.

[2038] Input: The translated message sent from the server.

[2039] Specific operation: The terminal displays the received message on the application screen.

[2040] Output: The translated message that is visible to the user.

[2041] Step 7:

[2042] Users post their concerns and worries within the application.

[2043] Input: A text post typed by the user (e.g., "I've been worrying a lot lately").

[2044] Specific operation: The user clicks the post button, and the post content is sent from the device to the server.

[2045] Output: The post sent to the server.

[2046] Step 8:

[2047] The server inputs the submitted post content into a generative AI model to understand the person's anxieties and worries.

[2048] Input: User submissions.

[2049] What happens: The server parses the post using a natural language processing model (e.g., the BERT model).

[2050] Data calculation: The model extracts keywords related to anxiety and worries from the content of the post.

[2051] Output: Analysis results identifying anxieties and worries.

[2052] Step 9:

[2053] The server selects the most appropriate content based on the user's anxieties and worries and sends it to the device.

[2054] Input: Analysis results of anxiety and worries.

[2055] Specific operation: The server searches the database for upbeat songs and videos and selects the most suitable content.

[2056] Data processing: Obtain information about the selected content and send it to the device.

[2057] Output: The selected content is sent to the device.

[2058] Step 10:

[2059] The terminal displays the selected content on the screen for the user to view or play.

[2060] Input: Content information sent from the server.

[2061] Specific operation: The terminal displays the received content on the application screen and provides a play button.

[2062] Output: Content that can be viewed and played by the user.

[2063] Step 11:

[2064] The server accesses the API of the disaster information source to collect the latest disaster information.

[2065] Input: API key and endpoint of disaster information source.

[2066] Specific operation: The server sends a request to the API to obtain new disaster information.

[2067] Data processing: Receive disaster information in JSON format and organize it appropriately.

[2068] Output: Retrieved and organized disaster information.

[2069] Step 12:

[2070] The server distributes the organized disaster information to users.

[2071] Input: Organized disaster information.

[2072] Specific operation: The server identifies users in the affected area and registers them in a distribution list for disaster information.

[2073] Data Processing: Generate a unique notification message and send it to the corresponding user.

[2074] Output: Disaster information notification sent to the user's device.

[2075] Step 13:

[2076] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[2077] Input: Disaster information notification sent from the server.

[2078] Specific operation: The terminal displays the received information on the application screen and notifies the user.

[2079] Output: A screen displaying disaster information and user response preparation.

[2080] (Application example 1)

[2081] 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."

[2082] Many fans tend to feel lonely and anxious during disasters, and there is a need for a system to alleviate this psychological burden. However, many current systems are unable to respond quickly and appropriately due to language barriers and a lack of information. Furthermore, they do not adequately provide content tailored to the psychological state of fans, and are unable to provide sufficient emotional support. This invention aims to utilize AI technology to translate multiple languages, grasp psychological states, and quickly distribute information during disasters, thereby providing an environment where fans can feel safe.

[2083] 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.

[2084] In this invention, the server includes: means for collecting new messages from the social media accounts of idols and celebrities; means for inputting the collected messages into an automatic analysis model and analyzing the context and sentiment; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing the multilingual translated messages to a terminal; means for collecting user posts and behavioral history to understand anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for detecting a cessation of user activity when a disaster occurs; means for providing free viewing content to a terminal when a disaster occurs; means for collecting necessary disaster information from various disaster information sources and delivering it to users; means for using a generative AI model to provide recommended content; and means for creating prompts for the generative AI model. This allows fans to feel safe and secure even during disasters without worrying about information.

[2085] "Idols and celebrities" generally refer to individuals who have a large number of fans and social influence.

[2086] "Social media account" refers to an account held by an individual or company within a social networking service for disseminating information, whether public or private.

[2087] "New messages" refers to the latest posts, comments, tweets, and other information from social media accounts.

[2088] An "automatic analysis model" refers to an algorithm that uses machine learning and natural language processing technology to analyze input text data and parse its content and emotions.

[2089] "Context and sentiment" refers to the meaning of the information contained within a text message and its emotional tenor, such as positive, negative, or neutral.

[2090] "Multilingual translation model" refers to an algorithm that uses artificial intelligence technology to automatically translate from a specific language to another language.

[2091] "Database" refers to a system for storing and managing data in a structured format.

[2092] A "terminal" refers to a device with communication capabilities that can be operated by a user, such as a smartphone, tablet, or personal computer.

[2093] "User posts and behavioral history" refers to data such as text, images, and activity logs posted by users on social media or within applications.

[2094] "Understanding anxieties and worries" refers to analyzing the content posted by users and their behavioral history to identify their mental state.

[2095] "Recommended content" refers to information such as songs, videos, articles, etc. that are deemed most suitable based on the user's emotions and interests.

[2096] "Detecting a cessation of user activity when a disaster occurs" means that the system detects a sudden decrease in the frequency of normal logins and posts.

[2097] "Providing viewing content free of charge" refers to providing entertainment content such as live footage and music without charging fees in the event of a disaster.

[2098] "Disaster information sources" refers to up-to-date information about disasters provided by government agencies and disaster information services.

[2099] A "generative AI model" refers to an artificial intelligence system that uses machine learning and deep learning to generate information and content in response to user requests.

[2100] A "prompt sentence" refers to an input sentence that causes a generative AI model to perform a specific task.

[2101] The present invention provides a system that reduces the loneliness and anxiety felt by users when a disaster occurs, and provides a safe environment. Specific embodiments of the system are described below.

[2102] System Configuration

[2103] This system consists of the following elements:

[2104] server

[2105] Terminal

[2106] User

[2107] Hardware and software used

[2108] API: Twitter API, YouTube API

[2109] AI analysis model: Google Cloud Natural Language API

[2110] Multilingual Translation: Google Translation API

[2111] Database: Firebase Realtime Database

[2112] Frontend: React Native for mobile app

[2113] System processing overview

[2114] 1. Collecting social media messages

[2115] The server uses the Twitter API to periodically collect new messages from the social media accounts of idols and celebrities.

[2116] 2. Message Analysis

[2117] The server analyzes the collected messages using the Google Cloud Natural Language API to analyze context and sentiment.

[2118] 3. Multilingual Translation

[2119] The server automatically translates the parsed messages into multiple languages ​​using the Google Translation API, and the translated messages are stored in the Firebase Realtime Database.

[2120] 4. Provision to Users

[2121] The server provides the multilingual translated message to the terminal when requested by the user through the terminal, and the terminal displays the message to the user.

[2122] 5. Understanding anxieties and worries and proposing content

[2123] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries and selects recommended content based on that information. The selected content is then provided to the user via the device.

[2124] 6. Providing free content during disasters

[2125] The server detects when a user's regular activities (logins and posts) are interrupted during a disaster. If this is detected, the server provides free viewing content (live video and music) to the device, allowing the user to use it.

[2126] 7. Collection and distribution of disaster information

[2127] The server accesses the APIs of various disaster information sources to collect the latest disaster information, which is then organized and quickly distributed to users in the affected areas.

[2128] Specific examples

[2129] Collecting and translating social media messages

[2130] The server collects new tweets from idols' and celebrities' social media accounts and assigns them sentiment labels. The server then translates the messages into multiple languages ​​(English, Chinese, Korean, etc.) and stores them in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message and sends it to the device. The device then displays the translated message.

[2131] Providing content based on user emotions

[2132] When a user posts "I've been feeling down lately" from their device, the device sends the post to a server. The server uses an AI analysis model to analyze the post and identify the anxiety. The server then selects uplifting songs and videos by idols to ease anxiety and sends them to the device. The device then displays the selected content for the user to view and play.

[2133] Prompt Sentence Examples

[2134] "Please suggest appropriate content (uplifting songs or videos) in response to the recent user post, 'I've been feeling down lately.'"

[2135] The above is an embodiment of the present invention. This system allows fans to feel at ease even in the event of a disaster and to quickly obtain the information they need.

[2136] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2137] Step 1:

[2138] Collecting SNS messages

[2139] The server periodically collects new messages from idols and celebrities using the APIs of their social media accounts. Specifically, it uses the Twitter API to retrieve the latest tweets from the accounts. The input is the response from the social media API, and the output is the retrieved messages.

[2140] Step 2:

[2141] Message Parsing

[2142] The server inputs the collected messages into the Google Cloud Natural Language API to analyze the context and sentiment. This process determines whether the message contains positive, negative, or neutral sentiment. The input is the collected messages, and the output is messages with sentiment labels attached. Specifically, the server sends messages to the Natural Language API and receives sentiment labels in response.

[2143] Step 3:

[2144] Multilingual Translation

[2145] The server uses the Google Translation API to translate the analyzed message into multiple languages. The target languages ​​for translation include English, Chinese, and Korean. The input is a message with an emotion label attached, and the output is a multilingual translation of the message. Specifically, the server sends the message to the Translation API and receives the translated text in each language.

[2146] Step 4:

[2147] Saving to a database

[2148] The server stores the translated messages in the Firebase Realtime Database. The input is the multilingual translated message, and the output is a data entry stored in the database. Specifically, the translated message is written to the database and a new data entry is created.

[2149] Step 5:

[2150] Provision to users

[2151] When a user requests a translation message through a terminal, the server retrieves the corresponding translation message from the database and sends it to the terminal. The input is the user request, and the output is the translation message. Specifically, the server queries the database based on the request and sends the result to the terminal.

[2152] Step 6:

[2153] Understanding anxieties and worries

[2154] The device collects user posts and behavioral history and sends them to a server. The server uses the Google Cloud Natural Language API to analyze the user's anxieties and worries. The input is the user's posts and behavioral history, and the output is the identified anxieties and worries. Specifically, the collected data is sent to the server, where it is analyzed.

[2155] Step 7:

[2156] Content Suggestion

[2157] The server selects recommended content, such as uplifting music or videos, based on the analyzed anxieties and worries. The selected content is provided to the user via the device. The input is the identified anxieties and worries, and the output is the selected content. Specifically, the server creates a prompt for the generative AI model, inputs the analysis results, and generates appropriate content.

[2158] Step 8:

[2159] Disaster activity detection

[2160] The server detects when a user's regular activity (logins or posts) has ceased. The input is the login and post activity history, and the output is the result of the detection of the cessation of activity. Specifically, it periodically checks the user's activity log and confirms the cessation of activity if there has been no activity for a certain period of time.

[2161] Step 9:

[2162] Providing free content during disasters

[2163] The server provides free content to inactive users. The input is the result of the inactivity detection, and the output is free content. Specifically, the server provides live video and music data via a generative AI model.

[2164] Step 10:

[2165] Collection and distribution of disaster information

[2166] The server accesses the APIs of various disaster information sources, collects and organizes the latest disaster information, and then distributes the organized information to users in the affected areas. The input is data from the disaster information sources, and the output is organized disaster information. Specifically, the server obtains information from the APIs and notifies users of the organized information in real time.

[2167] The above are the specific processing steps for carrying out the present invention. The input, data processing, and output performed at each step have been described in detail, and specific operations have been shown.

[2168] 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.

[2169] This invention combines an emotion engine with a system that provides a safe environment for fans in the event of a disaster, preventing them from feeling lonely or anxious. This system utilizes AI to collect and analyze social media messages from idols and celebrities, and in addition to translating them into multiple languages, it also has an emotion engine that recognizes the user's emotions, aiming to provide more appropriate content.

[2170] This system is implemented by the following means:

[2171] 1. Collecting social media messages

[2172] The server periodically collects new messages from the idols' or celebrities' social networking service (SNS) accounts, for example, by using an API to retrieve new tweets and posts.

[2173] 2. AI-powered message analysis and translation

[2174] The server inputs the collected social media messages into an AI analysis model to analyze the context and emotions, generating emotion labels such as "positive," "negative," and "neutral."

[2175] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[2176] The server stores the translated messages in a database.

[2177] 3. Providing multilingual translated messages

[2178] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[2179] The terminal sends a request to the server.

[2180] The server retrieves the corresponding translated message from the database.

[2181] The server transmits the obtained translated message to the terminal.

[2182] The terminal displays the received translated message on the screen.

[2183] 4. User Emotion Recognition by Emotion Engine

[2184] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[2185] The terminal collects the content posted by the user and transmits it to the server.

[2186] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[2187] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[2188] 5. Understanding anxieties and worries and proposing content

[2189] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[2190] The server transmits the selected content to the terminal.

[2191] The terminal displays and plays the selected content on the screen.

[2192] 6. Providing free content during disasters

[2193] The server detects that regular user activity (logins and posts) has ceased when a disaster occurs.

[2194] The server selects live videos and music to provide to the user free of charge.

[2195] The server transmits the selected free content to the terminal.

[2196] The terminal is enabled to display and play the received free content on the screen.

[2197] 7. Rapid collection and distribution of disaster information

[2198] The server accesses APIs from government and various disaster information sources to collect new disaster information.

[2199] The server will then properly organize and classify the collected disaster information.

[2200] The server distributes organized disaster information to users in the affected areas.

[2201] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[2202] Specific examples

[2203] Example 1: Collecting and translating social media messages

[2204] The server collects new tweets from Idol A's Twitter account. It analyzes the collected tweets using an AI analysis model and assigns sentiment labels. The messages are then automatically translated into English, Chinese, Korean, etc. and stored in a database. When a user requests a translated message through the app, the server retrieves the corresponding translated message from the database and sends it to the device. The device then displays the translated message.

[2205] Example 2: Understanding Anxiety and Proposing Content

[2206] A user posts in the app, "I've been worrying a lot lately." The device collects the post and sends it to the server. The server analyzes the post using an emotion engine and identifies the anxiety. The server selects upbeat songs and videos by idol A to ease anxiety and sends them to the device. The device displays the selected content, allowing the user to view and play it.

[2207] Example 3: Using an Emotion Engine

[2208] If a user posts a voice message within the app and the message is negative, such as "I'm so tired," the device collects the voice data and sends it to the server. The server uses an emotion engine to analyze the voice data and determine that the user is tired. The server selects relaxing music or an uplifting message to soothe the user and sends it to the device. The device then displays and plays this content.

[2209] As described above, by combining emotion engines, it is possible to more accurately grasp the user's emotions and respond to them individually, thereby providing psychological support even in the event of a disaster.

[2210] The processing flow will be explained below.

[2211] Processing flow of the system including the emotion engine

[2212] SNS message collection

[2213] Step 1:

[2214] The server provides API keys to access the idols' and celebrities' social media accounts (e.g., Twitter and Instagram).

[2215] Step 2:

[2216] The server sends requests to the social networking service's API on a regular schedule to retrieve new messages and posts.

[2217] Step 3:

[2218] The server stores the retrieved new messages in a database.

[2219] AI-powered message analysis and translation

[2220] Step 1:

[2221] The server reads the collected SNS messages from the database.

[2222] Step 2:

[2223] The server inputs the SNS message into an AI analysis model to analyze the emotion and context, generating emotion labels such as "positive," "negative," and "neutral."

[2224] Step 3:

[2225] The server inputs the parsed message into a multilingual translation model and translates it into the specified language (e.g., English, Chinese, Korean).

[2226] Step 4:

[2227] The server stores the translated messages in a database.

[2228] Providing multilingual translated messages

[2229] Step 1:

[2230] The user launches the app from their device (smartphone or tablet) and requests a translation of a social media message.

[2231] Step 2:

[2232] The terminal sends a request to the server.

[2233] Step 3:

[2234] The server retrieves the corresponding translated message from the database.

[2235] Step 4:

[2236] The server transmits the obtained translated message to the terminal.

[2237] Step 5:

[2238] The terminal displays the received translated message on the screen.

[2239] Recognizing user emotions with an emotion engine

[2240] Step 1:

[2241] A user posts about their feelings or situation (e.g., "I've been feeling anxious lately") on a social networking site or in an application.

[2242] Step 2:

[2243] The terminal collects the content posted by the user and transmits it to the server.

[2244] Step 3:

[2245] The server inputs the posted content into an emotion engine to recognize the user's emotions. For example, it analyzes emotions from the posted content, voice information, and facial images.

[2246] Step 4:

[2247] The server identifies the user's anxieties and worries based on the analysis results of the emotion engine.

[2248] Understanding anxieties and worries and proposing content

[2249] Step 1:

[2250] The server selects the most appropriate content (e.g., fun videos or uplifting music) from a database based on the analyzed emotions.

[2251] Step 2:

[2252] The server transmits the selected content to the terminal.

[2253] Step 3:

[2254] The terminal displays and plays the selected content on the screen.

[2255] Providing free content during disasters

[2256] Step 1:

[2257] The server runs an algorithm to detect when a user's regular activity (logins, posts) has stopped.

[2258] Step 2:

[2259] The server creates a list of users who have lost contact when a disaster occurs.

[2260] Step 3:

[2261] The server selects live videos and music to offer to the user free of charge.

[2262] Step 4:

[2263] The server transmits the selected free content to the terminal.

[2264] Step 5:

[2265] The terminal is enabled to display and play the received free content on the screen.

[2266] Rapid collection and distribution of disaster information

[2267] Step 1:

[2268] The server accesses disaster information sources (e.g., official government websites or APIs of the Japan Meteorological Agency) and collects new disaster information.

[2269] Step 2:

[2270] The server will then properly organize and classify the collected disaster information.

[2271] Step 3:

[2272] The server distributes organized disaster information to users in the affected areas.

[2273] Step 4:

[2274] The terminal displays the received disaster information on the screen, allowing the user to respond quickly.

[2275] Example 2

[2276] 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."

[2277] In recent years, with the spread of social networking services (SNS), messages from idols and celebrities have become an important source of information for users. However, these messages are not available in multiple languages ​​and lack emotional support. Furthermore, in the event of a disaster, users are likely to face an environment where they feel anxious and lonely. In such situations, there is a need for a system that can provide users with psychological support and prompt information.

[2278] 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 collecting new messages from social networking service accounts of idols and celebrities; means for inputting the collected messages into an artificial intelligence analysis model and analyzing the context and emotions; means for inputting the analyzed messages into a multilingual translation model for automatic translation; means for saving the translation results in a database; means for providing multilingual translated messages to users' communication devices; means for collecting user posts and behavioral history to understand their anxieties and worries; means for suggesting recommended content based on the understood anxieties and worries; means for analyzing users' emotions using an emotion engine; means for detecting a cessation of user activity when a disaster occurs; means for providing live video and music to users' communication devices free of charge when a disaster occurs; and means for collecting necessary disaster information from various disaster information sources and distributing it to users. This allows users to understand messages from idols and celebrities in multiple languages, reducing anxiety and loneliness in everyday life and during disasters, and receiving emotional support.

[2279] "Idols and celebrities" refer to individuals who are recogniz...

Claims

1. A way to collect new messages from the SNS accounts of idols and celebrities, The collected messages are input into an AI analysis model to analyze the context and emotions. A means for inputting the analyzed message into a multilingual translation model for automatic translation; a means for storing the translation results in a database; means for providing multilingual translated messages to a user's terminal; A means of collecting user posts and behavioral history to understand their anxieties and worries, A method to suggest recommended content based on the anxieties and worries identified, A means for detecting a cessation of user activity when a disaster occurs; A means to provide live video and music to users' devices free of charge in the event of a disaster, A means for collecting necessary disaster information from various disaster information sources and distributing it to users; A system including:

2. The collected content of posts and behavioral history will be analyzed using an AI analysis model to identify anxieties and worries, A means for selecting and providing optimal content to users based on their identified anxieties and worries; The system of claim 1 further comprising:

3. A means to access the API of disaster information sources to collect the latest disaster information, A means to organize the collected disaster information and distribute it to users in the affected areas; The system of claim 1 further comprising:

Images