System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving generated data; means for automatically generating content based on the received generated data; means for posting the generated content to a platform; means for receiving a question from a user; means for analyzing the received question and automatically generating an appropriate answer; means for returning the generated answer to the user; means for generating and sending a donation invitation message to the user; and means for directing the user to a donation page.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] For amateur sports organizations, public relations and donation solicitation activities are essential to continue their activities in situations where financial support is scarce. However, these activities require a great deal of time and effort, placing a burden on those involved, and the need for specialized knowledge makes them difficult to carry out efficiently. Furthermore, effective public relations and donation solicitation activities require consistent information dissemination and rapid response, but manual work has its limitations. Given this background, there is a need to provide a system that allows amateur sports organizations to carry out public relations and donation solicitation activities relatively easily and efficiently. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving generated data, a means for automatically generating content based on the received generated data, a means for posting the generated content to a platform, a means for receiving questions from users, a means for analyzing the received questions and automatically generating appropriate answers, a means for replying to the users with the generated answers, a means for generating and sending donation solicitation messages to users, and a means for directing users to a donation page. Specifically, the system posts content automatically generated by a generative AI model using data such as the sports organization's game results, player information, and event schedules on its official LINE account, and analyzes user questions using a natural language processing engine and automatically generates and replies with appropriate answers, thereby significantly reducing the effort required for public relations and donation solicitation. This allows amateur sports organizations to continue their activities efficiently and effectively.

[0006] "Generated data" refers to data provided by amateur sports organizations related to public relations and fundraising activities, such as match results, player information, and event schedules.

[0007] "Content" refers to information such as news articles, event information, and activity reports that are automatically generated using generative AI models based on generated data.

[0008] A "platform" is an online communication tool (such as the LINE official account) that is accessible to a wide range of users.

[0009] "Question" means an inquiry sent by a User to an Amateur Sports Organization via the Platform.

[0010] A "generative AI model" is a technology that uses artificial intelligence technology to automatically generate natural language sentences based on input data.

[0011] A "natural language processing engine" is a technology that analyzes questions from users and understands their intentions.

[0012] A "donation solicitation message" is an automatically generated message that requests financial support from users.

[0013] A "donation page" is an online page where users can make donations to an amateur sports organization.

[0014] "Users" are members of the public who use the system to receive information about sports organizations, ask questions, and make donations.

[0015] A "server" is a computer system that performs processes such as importing generated data, automatically generating content, analyzing questions, automatically generating answers, and sending donation solicitation messages on the platform. [Brief explanation of the drawings]

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

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

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

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

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

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

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

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] This invention is a system for automating the public relations and fundraising activities of amateur sports organizations. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[0038] System Configuration

[0039] This system mainly consists of the following components:

[0040] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing, and automatic message sending.

[0041] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0042] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[0043] Program Overview

[0044] The program processing of this system will be explained in natural language below.

[0045] 1. Data collection and content generation

[0046] The server periodically obtains data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0047] Based on the data acquired by the server, a generative AI model is used to automatically generate promotional content (news articles, event information, etc.).

[0048] The generated content is first reviewed by an administrator (a sports organization staff member) and then posted to the LINE official account timeline after approval.

[0049] 2. Automated responses to user questions

[0050] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0051] The server analyzes the received question using a natural language processing engine to understand the intent of the question.

[0052] Based on the analysis results, the server retrieves relevant information from the database and uses generation AI to generate an appropriate response.

[0053] The generated answer is automatically sent back to the user.

[0054] 3. Sending donation messages

[0055] The server periodically generates messages to collect donations (e.g., seasonal campaigns, collections before specific events, etc.).

[0056] The generated message is sent to all users as a push notification.

[0057] When the user clicks on the link to the donation page based on the message received, the terminal displays the donation page and the user can make a donation.

[0058] Specific examples

[0059] Example 1: Creating promotional content for match results

[0060] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[0061] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[0062] Example 2: Automatic response to user questions

[0063] A user submits a question: "When is the next game?"

[0064] The server analyzes the question, retrieves information from the database such as "The next game is on August 15th," and generates a sentence using a generative AI.

[0065] The generated response is automatically sent back to the user.

[0066] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[0067] The processing flow will be explained below.

[0068] Data collection and content generation process steps

[0069] Step 1:

[0070] The server acquires generated data such as match results, player information, and event schedules from the management terminal of the sports organization.

[0071] Step 2:

[0072] The generated data obtained by the server is saved in a database.

[0073] Step 3:

[0074] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0075] Step 4:

[0076] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0077] Step 5:

[0078] The administrator (user) approves the content and makes corrections as necessary.

[0079] Step 6:

[0080] The server automatically posts the approved content to the LINE Official Account's timeline.

[0081] Processing steps for automatic responses to user questions

[0082] Step 1:

[0083] A user sends a question via the LINE official account.

[0084] Step 2:

[0085] The server receives the question and analyzes it using a natural language processing engine.

[0086] Step 3:

[0087] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0088] Step 4:

[0089] The information acquired by the server is input into a generative AI model to generate an appropriate answer to the question.

[0090] Step 5:

[0091] The server returns the generated answer to the user.

[0092] Process steps for sending a donation message

[0093] Step 1:

[0094] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0095] Step 2:

[0096] The server sends the generated message to all users as a push notification from the LINE official account.

[0097] Step 3:

[0098] A user receives a push notification and clicks a link in the message.

[0099] Step 4:

[0100] The device (such as the user's smartphone) opens the link and displays the donation page.

[0101] Step 5:

[0102] The user completes the donation process and enters the amount on the donation page.

[0103] Step 6:

[0104] The device will complete the donation process and display a notification that the donation is complete.

[0105] These processing steps allow amateur sports organizations to efficiently and effectively automate their public relations and fundraising activities.

[0106] Example 1

[0107] 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."

[0108] Improving the efficiency of public relations and fundraising activities for amateur sports organizations is an important issue for many organizations. However, conventional methods require manual content creation, responding to user inquiries, and creating donation solicitation messages. These tasks are burdensome due to labor shortages and time constraints, making efficient management difficult. In addition, maintaining the quality and consistency of content is difficult, limiting the effectiveness of public relations and fundraising activities.

[0109] 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.

[0110] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to an external service, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means for an administrator to approve the content generation process. This automates public relations and donation solicitation activities, reducing the workload and improving quality.

[0111] "Generated data" refers to data received by the system, such as sporting events, player information, and event schedules.

[0112] "Content" refers to public relations information such as news articles and event information created using a generative AI model based on generated data.

[0113] "External Services" refers to online communication tools and social media platforms that allow users to interact with the website.

[0114] "User" refers to any person or entity that submits a question, receives content, or makes a donation through the System.

[0115] A "generative AI model" is a model that uses artificial intelligence technology to generate sentences in natural language from input data.

[0116] A "natural language processing engine" refers to technology that analyzes questions from users and understands their intent and content.

[0117] A "database" is a collection of data that a system uses to store information and search and retrieve it as needed.

[0118] A "donation solicitation message" is a message generated to encourage users to make donations.

[0119] "Push notifications" are notifications sent directly to users' devices, providing them with immediate access to important information and messages.

[0120] A "prompt" is a command or instruction given to a generative AI model to make it generate specific sentences based on input data.

[0121] The present invention is a system for automating public relations and fundraising activities for amateur sports organizations. This system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[0122] System Configuration

[0123] This system mainly consists of the following components:

[0124] 1. Server: Collects data, automatically generates content using generative AI models, answers questions using natural language processing, and sends automatic messages.

[0125] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0126] 3. External services: Online communication tools that act as a medium for exchanging information between users and sports organizations.

[0127] Specific hardware and software

[0128] Hardware:

[0129] Database Server: Used to store data.

[0130] High-performance GPU server: Used to run generative AI models.

[0131] software:

[0132] Data Collection API: Used to retrieve data from sports organization management tools.

[0133] Generative AI models (e.g., OpenAI's GPT-4): Used for content generation.

[0134] Natural language processing engine (e.g. BERT): Used to analyze user questions.

[0135] External service APIs (e.g., LINE API): Used to post generated content and communicate with users.

[0136] Specific examples of data collection and content generation

[0137] The server periodically retrieves data such as match results, player information, and event schedules from the sports organization's management terminal. This data is received in JSON format using HTTP requests. The server then analyzes the retrieved data and inputs the following prompts to the generative AI model:

[0138] "Generate news articles for your fans using yesterday's game results."

[0139] The generative AI model generates content based on this prompt as follows:

[0140] "Yesterday's game was a close one, but we won 3-2."

[0141] The generated content is sent to the sports organization's administrator, who reviews and approves it. Once approved, the content is automatically posted to the LINE Official Account's timeline using the LINE API.

[0142] Examples of automated responses to user questions

[0143] The user sends a question via the official LINE account: "When is the next game?" The server analyzes the received question using a natural language processing engine and understands the intent of the question. The server retrieves the information "The next game is on August 15th" from the database and inputs the following prompt sentence into the generative AI model:

[0144] "Generate an answer using the date of the next match."

[0145] Based on this prompt, the generative AI model generates an answer such as "The next game is on August 15th," and automatically replies to the user using the LINE API.

[0146] Examples of donation solicitation messages

[0147] The server periodically generates donation messages using a generative AI model. For example, before the next game, it inputs the following prompt:

[0148] "Create a message before your next game asking for donations."

[0149] Based on this prompt, the generative AI model generates a message saying, "Please support the next game. Donate here!" The generated message is sent to all users as a push notification using the notification API. Users receive the notification and click the link to be taken to a donation page where they can enter the required information to complete their donation.

[0150] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1: Data collection

[0153] The server retrieves data such as match results, player information, and event schedules from the sports organization's management terminal, using an HTTP request to retrieve data in JSON format.

[0154] Input: Data obtained from the management terminal API (JSON format).

[0155] Data processing: Analyze the received data and extract the necessary information (e.g., match results, player information).

[0156] Output: Extracted sports team data (match results, player information, event schedules).

[0157] Step 2: Content generation

[0158] The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) based on the data extracted in step 1, and generates promotional content.

[0159] Input: Extracted sports organization data, prompt (e.g., "Generate a news article for fans using yesterday's game results.").

[0160] Data processing: Generative AI models generate news articles and event information in natural language based on prompts and data.

[0161] Output: Generated promotional content (news articles, event information).

[0162] Step 3: Administrator Verification

[0163] The server sends the generated content to the sports organization's administrator for review and approval.

[0164] Input: Generated promotional content (news articles, event information).

[0165] Data processing: The administrator will be notified by email or via the management screen to request that the content be reviewed.

[0166] Output: Approval or corrective feedback from management.

[0167] Step 4: Post your content

[0168] After administrator approval, the server automatically posts the content to the LINE Official Account's timeline.

[0169] Input: Content approved by administrator.

[0170] Data processing: Uses the LINE API to post approved content to the timeline.

[0171] Output: Content posted to an external service.

[0172] Step 5: Receiving user questions

[0173] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0174] Input: A question message from the user.

[0175] Data processing: Receive questions using LINE's chat function.

[0176] Output: The query message received.

[0177] Step 6: Question Analysis

[0178] The server analyzes the received question using a natural language processing engine (e.g., BERT) to understand the intent of the question.

[0179] Input: The received question message.

[0180] Data processing: Perform text analysis to analyze keywords and context.

[0181] Output: Analysis results (question intent, keywords).

[0182] Step 7: Answer Generation

[0183] Based on the analysis results, the server retrieves relevant information from the database and generates an appropriate response using a generative AI model (e.g., GPT-4).

[0184] Input: Analysis results, relevant information from the database, and a prompt (e.g., "Generate an answer using the date of the next game.").

[0185] Data processing: Execute a database query to obtain the necessary information, and input a prompt into the generative AI model to generate an answer.

[0186] Output: The generated answer.

[0187] Step 8: Submit your response

[0188] The server automatically returns the generated answer to the user.

[0189] Input: The generated answer text.

[0190] Data processing: Use the LINE API to send the response to the user.

[0191] Output: The answer message sent to the user.

[0192] Step 9: Generate a donation message

[0193] The server periodically generates donation solicitation messages using a generative AI model.

[0194] Input: Prompt text (e.g., "Write a message to collect donations before the next game.").

[0195] Data processing: The generative AI model generates a donation solicitation message based on the prompt text.

[0196] Output: The generated donation message.

[0197] Step 10: Send the message

[0198] The server sends the generated message to all users as a push notification.

[0199] Input: The generated donation message.

[0200] Data processing: Push notifications are sent using the notification API.

[0201] Output: The push notification sent to the user device.

[0202] Step 11: Display the donation page

[0203] When the user clicks on the link to the donation page based on the message they received, the device displays the donation page.

[0204] Input: Link to donation page.

[0205] Data processing: Launch a browser and display the URL of the donation page.

[0206] Output: The donation page displayed.

[0207] Step 12: Make a donation

[0208] The user enters the required information on the donation page and completes the donation.

[0209] Input: Donation information (donation amount, payment method, etc.).

[0210] Data processing: Enter donation information into the form and click the submit button.

[0211] Output: Notification of donation completion.

[0212] In this way, each processing step involves specific operations and data processing, and the entire system is designed to operate efficiently using generative AI models and natural language processing techniques.

[0213] (Application example 1)

[0214] 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."

[0215] The present invention provides a system for automating public relations and donation solicitation activities in fields such as amateur sports organizations and food delivery. In particular, there is a need for centralized management of user question and answering, content automatic generation and posting, and donation solicitation message generation and transmission, all of which must be performed efficiently and with high accuracy. Conventional methods require these tasks to be performed manually, which requires a significant amount of time and effort, creating a need for automation to improve efficiency.

[0216] 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.

[0217] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to users, means for generating and sending a donation solicitation message to users, means for directing users to a donation page, means for automatically generating a recommendation menu based on user preference information, and means for sending the generated content to all users using push notifications. This enables unified and efficient operation of public relations activities, user support, promotional activities, and donation solicitations.

[0218] "Generation data" refers to data that is input into the system and is information that is used to generate content.

[0219] "Content" is a collection of information created based on generated data, and is material provided to users for public relations, promotion, or information purposes.

[0220] A "platform" is a foundation for users and systems to exchange information, including online communication tools and applications.

[0221] A "user" is an entity that uses the system to send and receive information, such as asking questions, viewing publicity content, and making donations.

[0222] A "natural language processing engine" is a technology that analyzes questions and input text from users and understands their meaning.

[0223] A "generative AI model" is a type of artificial intelligence used to automatically generate content based on data, and has the ability to generate responses to specific prompts.

[0224] A "donation solicitation message" is a message sent to users to encourage them to make donations, and includes information related to a specific campaign or event.

[0225] A "donation page" is a web page or screen within an application that allows users to make donations.

[0226] A "recommended menu" is a list of dishes and services that is automatically generated using a generative AI model based on the user's preference information.

[0227] "Push notification" is a feature that sends content or messages directly to a user's device, with the aim of attracting the user's attention.

[0228] A detailed embodiment of the system according to the present invention will be described below. The system automates public relations and donation collection activities, and particularly improves the efficiency of food delivery service operations. The system is composed of a server, a user terminal, and a platform.

[0229] The server implements the following main functions:

[0230] 1. Receiving generated data

[0231] 2. Automated content generation

[0232] 3. Posting Generated Content to the Platform

[0233] 4. Receiving and analyzing user questions

[0234] 5. Generate and reply to appropriate answers

[0235] 6. Creating and sending donation solicitation messages

[0236] 7. Direct users to a donation page

[0237] 8. Automatic Generation of Recommendation Menus Based on User Preferences

[0238] 9. Sending to all users using push notifications

[0239] The user terminal is used to input questions, generate content, and access the donation page. The platform uses online communication tools to mediate the exchange of information between the user and the server.

[0240] Hardware and software used

[0241] The system's server uses a database (MySQL), a generative AI model (OpenAI GPT-3), a natural language processing engine (NLTK), and a push notification service (Firebase Cloud Messaging). User devices are primarily mobile devices such as smartphones and tablets. The platform used is a common online communication tool (e.g., LINE).

[0242] Data processing and calculation

[0243] 1. Data collection and content generation

[0244] The server collects the latest menu and promotion information from the food delivery company's database. Based on this data, it uses a generative AI model to automatically generate content for users. For example, it generates a promotional message such as, "Today's recommended menu items are sushi, tempura, and ramen."

[0245] 2. Responding to user questions

[0246] When a user submits a question (e.g., "What's your recommendation today?"), the server uses a natural language processing engine to analyze the intent of the question. Based on the analysis results, it uses a generative AI model to generate an appropriate answer and sends it back to the user.

[0247] 3. Generate and send donation messages

[0248] The server periodically generates donation solicitation messages and sends them to all users using the push notification service. For example, a message generated in response to the prompt "Please create a new donation campaign message" might include something like "Would you like to donate 50 meals as part of our winter campaign?"

[0249] Specific examples

[0250] Example of generating a recommended menu: Using a generative AI model (OpenAI GPT-3) based on user preference information, a prompt such as "User preference: spicy food" is input, and "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot" is generated.

[0251] Example of response to user question: The server analyzes the question "What is your recommendation today?" and generates and returns the answer "Today, we recommend Margherita pizza and Caprese salad."

[0252] Example of a donation message prompt: "Write a message for our new donation campaign."

[0253] In this way, this system can achieve automation and efficiency in food delivery operations by utilizing generative AI models and natural language processing technology.

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] The server collects the latest menu and promotion information from food delivery companies' databases.

[0257] Input: Latest menu and promotion information available in the database

[0258] Output: Collected menu and promotion information

[0259] What happens: The server uses a REST API to access the database and retrieve the latest menu and promotion information, which is then used in subsequent processing steps.

[0260] Step 2:

[0261] Based on the collected data, the server automatically generates public relations or promotional content using a generative AI model (OpenAI GPT-3).

[0262] Input: Latest menu and promotion information

[0263] Output: Auto-generated promotional or promotional content

[0264] Specific operation: The server inputs the prompt "Please create a promotional message based on the latest food delivery menu" into the generative AI model, and generates content such as "Today's recommended menu items are sushi, tempura, and ramen."

[0265] Step 3:

[0266] The server posts the generated content to a platform (e.g., an online communication tool).

[0267] Input: Auto-generated PR or promotional content

[0268] Output: Content posted to the platform

[0269] Specific operation: The server sends the generated content to the platform's submission endpoint using the REST API so that it can be displayed to the user.

[0270] Step 4:

[0271] The user uses the device to enter a question within the app and send it to the server.

[0272] Input: A question from the user (e.g., "What's your recommendation today?")

[0273] Output: User question sent to server

[0274] Specific operation: The user enters a question in the message input field of the app and presses the send button, and the question is sent to the server.

[0275] Step 5:

[0276] The server analyzes the received question using a natural language processing engine (NLTK) to understand the intent of the question.

[0277] Input: User question sent to server

[0278] Output: Parsed question intent

[0279] Specific operation: The server uses a natural language processing engine to analyze the text of the received question and extract the intent (e.g., "I would like to know what menu items are recommended").

[0280] Step 6:

[0281] Based on the intent of the analyzed question, the server uses a generative AI model to generate an appropriate answer and returns it to the user.

[0282] Input: Parsed question intent

[0283] Output: Auto-generated answer

[0284] Specific operation: The server inputs the prompt "What's your recommendation today?" into the generative AI model, which generates the answer "Margherita pizza and Caprese salad are recommended today." It then sends this answer back to the user.

[0285] Step 7:

[0286] The server generates a donation solicitation message using a generative AI model and sends it to all users via push notification.

[0287] Input: Generate Donation Message Prompt

[0288] Output: The donation message sent to the user

[0289] Specific operation: The server inputs the prompt "Please create a message for a new donation campaign" into the generative AI model, which generates the message "Would you like to donate 50 meals as part of our winter campaign?". It then uses Firebase Cloud Messaging to send a push notification to all users.

[0290] Step 8:

[0291] A user receives a donation message and clicks on a link to a donation page.

[0292] Input: Donation message received as a push notification

[0293] Output: Display of donation page

[0294] What happens: When a user clicks on the link in the push notification, a browser or in-app browser opens to a donation page where the user can make a donation.

[0295] Step 9:

[0296] The server automatically generates a recommendation menu based on user preference information using a generative AI model.

[0297] Input: User preference information

[0298] Output: Recommendation menu

[0299] Specific operation: The server inputs the prompt "User preference: spicy food" into the generative AI model, and automatically generates candidates such as "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot." The generated recommended menu is then presented to the user.

[0300] 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.

[0301] This invention combines a system for automating public relations and fundraising activities for amateur sports organizations with an emotion engine that recognizes user emotions. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users. Furthermore, the emotion engine enables appropriate responses based on the user's emotional state.

[0302] System Configuration

[0303] This system mainly consists of the following components:

[0304] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing and an emotion engine, and automatic message sending.

[0305] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0306] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[0307] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[0308] Program Overview

[0309] The program processing of this system will be explained in natural language below.

[0310] 1. Data collection and content generation

[0311] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0312] The generated data obtained by the server is saved in a database.

[0313] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0314] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0315] The administrator (user) approves the content and makes corrections as necessary.

[0316] The server automatically posts the approved content to the LINE Official Account's timeline.

[0317] 2. Automated responses to user questions

[0318] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0319] The server receives the question and analyzes it using a natural language processing engine.

[0320] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0321] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0322] The server inputs the information it obtains and the results of the emotion engine into a generative AI model to generate an appropriate answer to the question.

[0323] The generated response is automatically sent back to the user in a tone and content that reflects the user's emotional state.

[0324] 3. Sending donation messages

[0325] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0326] The server sends the generated message to all users as a push notification from the LINE official account.

[0327] A user receives a push notification and clicks a link in the message.

[0328] The device (such as the user's smartphone) opens the link and displays the donation page.

[0329] The user completes the donation process and enters the amount on the donation page.

[0330] The device will complete the donation process and display a notification that the donation is complete.

[0331] Specific examples

[0332] Example 1: Creating promotional content for match results

[0333] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[0334] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[0335] Example 2: Automatic response to user questions

[0336] A user submits a question: "When is the next game?"

[0337] The server analyzes the question and uses an emotion engine to determine the user's emotional state as "excited."

[0338] The server retrieves information from the database that "The next game is on August 15th," and uses a generation AI to generate a response that reads, "The next game is on August 15th. Please cheer us on!"

[0339] The generated response is automatically sent back to the user.

[0340] In this way, by utilizing generative AI and natural language processing technology as well as an emotion engine, this system is able to automate more appropriate public relations and donation solicitation activities according to the user's emotional state.

[0341] The processing flow will be explained below.

[0342] Data collection and content generation process steps

[0343] Step 1:

[0344] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations.

[0345] Step 2:

[0346] The generated data obtained by the server is saved in a database.

[0347] Step 3:

[0348] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0349] Step 4:

[0350] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0351] Step 5:

[0352] The administrator (user) approves the content and makes corrections as necessary.

[0353] Step 6:

[0354] The server automatically posts the approved content to the LINE Official Account's timeline.

[0355] Processing steps for automatic responses to user questions

[0356] Step 1:

[0357] A user sends a question via the LINE official account.

[0358] Step 2:

[0359] The server receives the question and analyzes it using a natural language processing engine.

[0360] Step 3:

[0361] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0362] Step 4:

[0363] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0364] Step 5:

[0365] The information acquired by the server is input into a generative AI model to generate an answer appropriate to the user's emotional state.

[0366] Step 6:

[0367] The server returns the generated answer to the user.

[0368] Process steps for sending a donation message

[0369] Step 1:

[0370] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0371] Step 2:

[0372] The server sends the generated message to all users as a push notification from the LINE official account.

[0373] Step 3:

[0374] A user receives a push notification and clicks a link in the message.

[0375] Step 4:

[0376] The device (such as the user's smartphone) opens the link and displays the donation page.

[0377] Step 5:

[0378] The user completes the donation process and enters the amount on the donation page.

[0379] Step 6:

[0380] The device will complete the donation process and display a notification that the donation is complete.

[0381] Example: Automated responses to user questions

[0382] Example 1: If the user's question is "When is the next game?"

[0383] Step 1: A user sends a question to the LINE Official Account asking, "When is the next game?"

[0384] Step 2: The server receives the question and uses its natural language processing engine to parse it as asking "When is the next game?"

[0385] Step 3: When the server parses the question, it uses an emotion engine to detect the emotion of "excitement" from the user's text.

[0386] Step 4: The server retrieves the game schedule data from the database and confirms the information: "The next game is on August 15th."

[0387] Step 5: The information obtained by the server is input into the generative AI model, which generates a response that matches the user's emotions: "The next game is on August 15th. Please cheer us on!"

[0388] Step 6: The server automatically sends the generated answer back to the user.

[0389] In this way, by combining generative AI, natural language processing, and emotion engine technologies, this system can automate appropriate public relations and donation collection activities according to the user's emotional state.

[0390] Example 2

[0391] 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."

[0392] Today, amateur sports organizations spend a lot of time and manpower on effective public relations and fundraising activities. However, there is a lack of systems to automate these activities, making it particularly difficult to respond appropriately to user emotions. Furthermore, there is a lack of mechanisms for quickly and accurately answering user questions. This makes it difficult to increase user satisfaction, and as a result, fundraising is not effective.

[0393] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to a communication network, means for receiving questions from users, means for analyzing the received questions and acquiring information related to the questions from a database, means for automatically generating appropriate answers based on the acquired information, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to donation pages, means for analyzing the emotional state of the users, and means for generating appropriate content and answers based on the analyzed emotional state. This makes it possible to automate public relations activities and donation solicitation activities and respond appropriately according to the user's emotional state, thereby increasing user satisfaction and enabling effective donation solicitation.

[0394] "Means for receiving generated data" refers to a device or software that has the function of obtaining data such as match results, player information, and event schedules from the sports organization's management terminal or other information sources.

[0395] "Means for automatically generating content" refers to a device or software that has the function of automatically generating advertising or public relations content based on received data.

[0396] A "means for posting to a communications network" is any device or software capable of automatically posting generated content to an online platform.

[0397] The "means for receiving questions from users" refers to a device or software that has the function of receiving messages or questions from users via the online platform.

[0398] The "means for analyzing a question" is a device or software that has the function of analyzing a received question using a natural language processing engine and understanding the content of the question.

[0399] The "means for retrieving information from a database" is a device or software that has the function of extracting information related to the analyzed question from a database.

[0400] The "means for automatically generating appropriate answers" refers to a device or software that has the function of automatically generating answers to user questions based on the acquired information and analysis results.

[0401] The "means for returning an answer to a user" is a device or software that has the function of automatically sending the generated answer to the user.

[0402] A "means for generating a solicitation message" is a device or software capable of automatically generating a message suitable for a solicitation campaign.

[0403] The "means for sending to a user" is a device or software that has the function of sending the generated solicitation message to a user.

[0404] A "means for directing users to a donation page" is a device or software that has the function of automatically providing a message containing a link or instructions that directs users to a web page where they can make a donation.

[0405] A "means for analyzing a user's emotional state" is a device or software that has an engine or algorithm that analyzes emotions from a user's input text or voice.

[0406] The "means for generating appropriate content and answers" refers to a device or software that has the function of automatically generating content and answers that correspond to the user's emotions based on the analyzed emotional state.

[0407] This invention relates to a system that automates the public relations and fundraising activities of amateur sports organizations and enables them to respond to user emotions. This system has the functionality to automatically generate content based on generated data and to automatically generate answers to user questions.

[0408] System Configuration

[0409] This system consists of the following components:

[0410] 1. Server: Mainly manages membership, collects data, automatically generates content using generative AI models, answers questions using natural language processing and an emotion engine, and sends automatic messages.

[0411] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0412] 3. Platform: A medium for exchanging information between users and sports organizations via online communication tools (e.g., LINE official account).

[0413] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[0414] Detailed function description

[0415] 1. Data collection and content generation

[0416] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0417] Store the generated data in a relational database (e.g., MySQL).

[0418] The server inputs the saved data into a generative AI model (e.g., OpenAI GPT-3) to automatically generate PR content. For example, the prompt could be, "Generate PR content about yesterday's game results. The game score was 3-2, and the game date was yesterday."

[0419] The generated content is sent to an administrator's terminal, where the administrator checks the content and makes corrections as necessary.

[0420] When the administrator clicks the approval button, the server posts the approved content to the LINE Official Account's timeline.

[0421] 2. Automated responses to user questions

[0422] The server receives the question (e.g., "When is the next game?") sent by the user via the LINE Official Account.

[0423] The server uses a natural language processing engine (e.g., spaCy) to analyze the question and extract key content.

[0424] An emotion engine (e.g., IBM Watson Tone Analyzer) is used to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0425] The server retrieves relevant information (e.g. "date of next game") from a database using an SQL query.

[0426] Based on the acquired information and the results of sentiment analysis, a prompt is input into the generative AI model to generate a response. A possible prompt might be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[0427] The generated answer is automatically sent back to the user via the LINE Messaging API.

[0428] 3. Sending donation messages

[0429] The server automatically generates a donation message by inputting it into the AI ​​model depending on the start time of the donation campaign. For example, it uses the prompt, "Please create a message for the donation campaign starting next week. The main points are to explain the importance of donations and how to donate."

[0430] The server sends the generated message to all users as a push notification from the LINE official account.

[0431] When a user receives the push notification and clicks on the link in the message, the user's device will display a donation page.

[0432] The user completes the donation procedure on the donation page, and the device displays a notification that the donation has been completed.

[0433] This system automates public relations and fundraising activities and responds appropriately to the user's emotional state, which not only increases user satisfaction but also enables effective fundraising.

[0434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] The server obtains generated data (e.g., match results, player information, event schedules) from the sports organization's management terminal. The server periodically issues API requests, receives data from the management terminal, and imports it in JSON format. The obtained generated data is stored in a relational database (e.g., MySQL).

[0437] Input: Data generated from the management terminal of a sports organization

[0438] Data processing: Convert JSON data into database format

[0439] Output: Generated data stored in a database

[0440] Step 2:

[0441] The server inputs PR content into a generative AI model (e.g., OpenAI GPT-3) based on the saved generated data, and generates automatic content. For example, the prompt text could be, "Generate a PR text about the results of yesterday's game. The game score was 3-2, and the game date was yesterday." The generated content is temporarily saved in JSON format.

[0442] Input: Generated data stored in a database

[0443] Data processing: Input to generative AI models

[0444] Output: Generated promotional content

[0445] Step 3:

[0446] The generated PR content is sent to the administrator's terminal. The administrator (user) uses the management interface to check the generated content and make corrections as necessary. Once corrections are complete, the administrator clicks the approval button.

[0447] Input: Generated PR content, admin modifications

[0448] Data processing: Correction and approval through the management interface

[0449] Output: Approved content

[0450] Step 4:

[0451] The server posts the approved content to the LINE Official Account's timeline automatically using the LINE API.

[0452] Input: Approved Content

[0453] Data processing: Posting using LINE API

[0454] Output: Content posted to the LINE Official Account's timeline

[0455] Step 5:

[0456] A user sends a question (e.g., "When is the next game?") via the LINE Official Account. The server receives the message using the LINE Messaging API and obtains the text data.

[0457] Input: Question message from user

[0458] Data processing: Receiving text data

[0459] Output: User question data

[0460] Step 6:

[0461] The server uses a natural language processing engine (e.g., spaCy) to analyze the question, and based on the extracted intent and keywords, retrieves relevant information (e.g., "next game schedule") from a database using an SQL query.

[0462] Input: User question data

[0463] Data processing: Question analysis using natural language processing, database search using SQL queries

[0464] Output: Related information (e.g., next game date)

[0465] Step 7:

[0466] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state. Based on the analyzed emotional state (e.g., joy, anger, sadness, etc.), a generative AI model is used to generate a response with an appropriate tone and content. An example of a prompt could be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[0467] Input: relevant information, user's emotional state

[0468] Data processing: Generating answers using a generative AI model

[0469] Output: Generated answer

[0470] Step 8:

[0471] The generated response is sent back to the user via the LINE Messaging API. The server sends the generated response as a message.

[0472] Input: Generated answer

[0473] Data processing: Sending messages using the LINE API

[0474] Output: The answer message sent to the user

[0475] Step 9:

[0476] The server inputs the donation solicitation message into the AI ​​model according to the start date of the donation solicitation campaign and automatically generates the message. For example, it uses the prompt "Please create a message for the donation campaign starting next week. The main points should be to explain the importance of donations and how to donate." The generated message is sent to all users as a push notification to the LINE official account.

[0477] Input: Donation message template

[0478] Data processing: Message generation using generative AI models

[0479] Output: Generated donation message

[0480] Step 10:

[0481] When a user receives a push notification and clicks the link in the message, the user's device will display a donation page. The user enters the donation amount and provides the necessary information on the donation page. The device will then complete the donation process and display a notification that the donation has been completed.

[0482] Input: Push notification link, user input information

[0483] Data processing: Displaying donation pages and carrying out donation procedures

[0484] Output: Notification of donation completion

[0485] In this way, the system can effectively promote and solicit donations throughout the entire process.

[0486] (Application example 2)

[0487] 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."

[0488] Amateur sports organizations are required to streamline their public relations and donation-raising activities while also providing appropriate responses based on user emotions. Conventional systems lack sufficient emotional responses, which can lead to a poor user experience. In particular, accurately capturing the enthusiasm and emotions of sports fans and providing appropriate public relations and donation-raising messages is crucial for expanding support for the organization.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0490] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means including an emotion analysis engine for analyzing the user's emotions and adjusting the content and answer sentences based on the analyzed emotions. This enables public relations activities and donation solicitations to be conducted in accordance with the user's emotions, thereby effectively spreading support for sports organizations.

[0491] "Generated Data" refers to information generated by sports organizations, such as match results, player information, and event schedules.

[0492] "Content" refers to promotional text and images automatically generated by a generative AI model based on generated data.

[0493] "Platform" refers to an intermediary system, such as an online communication tool or social networking site, that allows users to exchange information with sports organizations.

[0494] A "question" refers to an inquiry that a user inputs to the system.

[0495] "Emotion analysis engine" refers to an engine for analyzing a user's emotional state from input text or voice.

[0496] A "donation solicitation message" refers to a message created by a sports organization to call on users for donations.

[0497] A "generative AI model" refers to an artificial intelligence model that performs natural language processing, image generation, etc. based on input data.

[0498] A "natural language processing engine" refers to an engine that analyzes text data entered by a user, understands its meaning, and generates an appropriate response.

[0499] MODE FOR CARRYING OUT THE INVENTION

[0500] The present invention is a system for automating public relations and donation collection activities for amateur sports organizations, and in particular, a system that uses an emotion analysis engine to realize appropriate responses according to the user's emotions. To achieve this purpose, the system includes the following components and processing steps:

[0501] 1. System Configuration

[0502] This system mainly consists of a server, a user terminal, and a platform.

[0503] server:

[0504] The server is the main component that receives generated data, automatically generates content, analyzes questions and generates answers, generates and sends donation messages, and analyzes user sentiment. Specifically, the following hardware and software are used:

[0505] Hardware: High-performance server unit

[0506] Software: Generative AI models, natural language processing engines, sentiment analysis engines (e.g., Hugging Face Transformers)

[0507] User device:

[0508] A user terminal is the device that a user uses to enter questions, receive answers, and access the donation page, typically a smartphone or personal computer.

[0509] Platform:

[0510] The platform is a system that includes online communication tools and social networking sites, and serves as a medium for the smooth exchange of information between users and sports organizations.

[0511] 2. Program Processing

[0512] The server performs the following process:

[0513] 1. Receiving generated data and generating content

[0514] The server periodically acquires generated data such as match results, player information, and event schedules from the sports organization's management terminal, and then uses this data to apply a generative AI model to automatically generate promotional content.

[0515] 2. Question Analysis and Answer Generation

[0516] When a user submits a question through the platform, the server receives the question and analyzes it using a natural language processing engine. Based on the analysis results, the sentiment analysis engine identifies the user's emotional state, retrieves relevant information from the database, and generates an appropriate answer that is tailored to the user's emotions.

[0517] 3. Generate and send a donation message

[0518] The server generates a donation message based on the start time of the donation campaign and sends it as a push notification to all users, who can then access the donation page.

[0519] 3. Examples and prompts

[0520] Example 1: Creating promotional content for match results

[0521] The server receives the match result data and uses a generative AI model to generate a sentence such as, "Yesterday's match was a close one, but we won 3-2." The generated sentence is then reviewed and approved by an administrator and automatically posted to the platform's timeline.

[0522] Example 2: Automatic response to user questions

[0523] When a user sends a question such as "When is the next game?", the server analyzes the question and uses an emotion analysis engine to determine the user's emotional state as "excited." The server then retrieves the information "The next game is on August 15th" from the database and uses a generative AI model to generate an automatic response saying "The next game is on August 15th. Please cheer us on!"

[0524] Prompt Sentence Examples

[0525] "Generate a news article from the following data: [generated data]"

[0526] "Generate a friendly, positive message about the next game: The next game is on August 15."

[0527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0528] Step 1:

[0529] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations. This input data is sent to the server via API and stored in a database.

[0530] Step 2:

[0531] The server inputs the generated data in the database into a generative AI model to automatically generate PR content. Specifically, it uses the prompt "Generate a news article from the following data: [generated data]" and outputs content in the form of a news article. This output is temporarily saved and awaits confirmation by an administrator.

[0532] Step 3:

[0533] The server sends the generated content to an administrator terminal, who reviews the content and makes any necessary corrections. The server then receives the approved content and automatically posts it to the platform's timeline. The output of this step is the published news content.

[0534] Step 4:

[0535] When a user submits a question through the platform, the server receives the question. The question is sent to the server in text format and input into a natural language processing engine for analysis. The engine analyzes the meaning of the question and identifies the intent of the question as its output.

[0536] Step 5:

[0537] The server identifies the user's emotional state based on the analysis results using a sentiment analysis engine, which extracts emotions from the user's text and outputs emotional states such as "excitement," "joy," and "anger."

[0538] Step 6:

[0539] The server retrieves relevant information from the database based on the results of the sentiment analysis. For example, if the question is "When is the next game?", it retrieves the date and time of the next game. This output information is used in the next step.

[0540] Step 7:

[0541] The server inputs the acquired information and the results of the sentiment analysis engine into a generative AI model to generate an appropriate response. It uses a prompt such as "Generate a friendly, positive message about the next game: The next game is on August 15." and outputs an appropriate response for the user.

[0542] Step 8:

[0543] The generated answer is automatically sent back to the user. A reply message is sent to the user's device via the platform's chat function. The output of this step is the appropriate answer delivered to the user.

[0544] Step 9:

[0545] The server generates a donation message based on the start date of the donation campaign. Using a generative AI model, it outputs a message calling for donations: "We need your support! Please donate to our team to keep us going."

[0546] Step 10:

[0547] The server sends the generated donation message to all users as a platform push notification. When a user receives the notification and clicks on the link in the message, the user's device is redirected to the donation page. The output of this step is a user directed to the donation page.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] [Second embodiment]

[0552] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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."

[0564] This invention is a system for automating the public relations and fundraising activities of amateur sports organizations. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[0565] System Configuration

[0566] This system mainly consists of the following components:

[0567] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing, and automatic message sending.

[0568] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0569] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[0570] Program Overview

[0571] The program processing of this system will be explained in natural language below.

[0572] 1. Data collection and content generation

[0573] The server periodically obtains data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0574] Based on the data acquired by the server, a generative AI model is used to automatically generate promotional content (news articles, event information, etc.).

[0575] The generated content is first reviewed by an administrator (a sports organization staff member) and then posted to the LINE official account timeline after approval.

[0576] 2. Automated responses to user questions

[0577] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0578] The server analyzes the received question using a natural language processing engine to understand the intent of the question.

[0579] Based on the analysis results, the server retrieves relevant information from the database and uses generation AI to generate an appropriate response.

[0580] The generated answer is automatically sent back to the user.

[0581] 3. Sending donation messages

[0582] The server periodically generates messages to collect donations (e.g., seasonal campaigns, collections before specific events, etc.).

[0583] The generated message is sent to all users as a push notification.

[0584] When the user clicks on the link to the donation page based on the message received, the terminal displays the donation page and the user can make a donation.

[0585] Specific examples

[0586] Example 1: Creating promotional content for match results

[0587] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[0588] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[0589] Example 2: Automatic response to user questions

[0590] A user submits a question: "When is the next game?"

[0591] The server analyzes the question, retrieves information from the database such as "The next game is on August 15th," and generates a sentence using a generative AI.

[0592] The generated response is automatically sent back to the user.

[0593] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[0594] The processing flow will be explained below.

[0595] Data collection and content generation process steps

[0596] Step 1:

[0597] The server acquires generated data such as match results, player information, and event schedules from the management terminal of the sports organization.

[0598] Step 2:

[0599] The generated data obtained by the server is saved in a database.

[0600] Step 3:

[0601] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0602] Step 4:

[0603] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0604] Step 5:

[0605] The administrator (user) approves the content and makes corrections as necessary.

[0606] Step 6:

[0607] The server automatically posts the approved content to the LINE Official Account's timeline.

[0608] Processing steps for automatic responses to user questions

[0609] Step 1:

[0610] A user sends a question via the LINE official account.

[0611] Step 2:

[0612] The server receives the question and analyzes it using a natural language processing engine.

[0613] Step 3:

[0614] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0615] Step 4:

[0616] The information acquired by the server is input into a generative AI model to generate an appropriate answer to the question.

[0617] Step 5:

[0618] The server returns the generated answer to the user.

[0619] Process steps for sending a donation message

[0620] Step 1:

[0621] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0622] Step 2:

[0623] The server sends the generated message to all users as a push notification from the LINE official account.

[0624] Step 3:

[0625] A user receives a push notification and clicks a link in the message.

[0626] Step 4:

[0627] The device (such as the user's smartphone) opens the link and displays the donation page.

[0628] Step 5:

[0629] The user completes the donation process and enters the amount on the donation page.

[0630] Step 6:

[0631] The device will complete the donation process and display a notification that the donation is complete.

[0632] These processing steps allow amateur sports organizations to efficiently and effectively automate their public relations and fundraising activities.

[0633] Example 1

[0634] 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."

[0635] Improving the efficiency of public relations and fundraising activities for amateur sports organizations is an important issue for many organizations. However, conventional methods require manual content creation, responding to user inquiries, and creating donation solicitation messages. These tasks are burdensome due to labor shortages and time constraints, making efficient management difficult. In addition, maintaining the quality and consistency of content is difficult, limiting the effectiveness of public relations and fundraising activities.

[0636] 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.

[0637] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to an external service, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means for an administrator to approve the content generation process. This automates public relations and donation solicitation activities, reducing the workload and improving quality.

[0638] "Generated data" refers to data received by the system, such as sporting events, player information, and event schedules.

[0639] "Content" refers to public relations information such as news articles and event information created using a generative AI model based on generated data.

[0640] "External Services" refers to online communication tools and social media platforms that allow users to interact with the website.

[0641] "User" refers to any person or entity that submits a question, receives content, or makes a donation through the System.

[0642] A "generative AI model" is a model that uses artificial intelligence technology to generate sentences in natural language from input data.

[0643] A "natural language processing engine" refers to technology that analyzes questions from users and understands their intent and content.

[0644] A "database" is a collection of data that a system uses to store information and search and retrieve it as needed.

[0645] A "donation solicitation message" is a message generated to encourage users to make donations.

[0646] "Push notifications" are notifications sent directly to users' devices, providing them with immediate access to important information and messages.

[0647] A "prompt" is a command or instruction given to a generative AI model to make it generate specific sentences based on input data.

[0648] The present invention is a system for automating public relations and fundraising activities for amateur sports organizations. This system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[0649] System Configuration

[0650] This system mainly consists of the following components:

[0651] 1. Server: Collects data, automatically generates content using generative AI models, answers questions using natural language processing, and sends automatic messages.

[0652] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0653] 3. External services: Online communication tools that act as a medium for exchanging information between users and sports organizations.

[0654] Specific hardware and software

[0655] Hardware:

[0656] Database Server: Used to store data.

[0657] High-performance GPU server: Used to run generative AI models.

[0658] software:

[0659] Data Collection API: Used to retrieve data from sports organization management tools.

[0660] Generative AI models (e.g., OpenAI's GPT-4): Used for content generation.

[0661] Natural language processing engine (e.g. BERT): Used to analyze user questions.

[0662] External service APIs (e.g., LINE API): Used to post generated content and communicate with users.

[0663] Specific examples of data collection and content generation

[0664] The server periodically retrieves data such as match results, player information, and event schedules from the sports organization's management terminal. This data is received in JSON format using HTTP requests. The server then analyzes the retrieved data and inputs the following prompts to the generative AI model:

[0665] "Generate news articles for your fans using yesterday's game results."

[0666] The generative AI model generates content based on this prompt as follows:

[0667] "Yesterday's game was a close one, but we won 3-2."

[0668] The generated content is sent to the sports organization's administrator, who reviews and approves it. Once approved, the content is automatically posted to the LINE Official Account's timeline using the LINE API.

[0669] Examples of automated responses to user questions

[0670] The user sends a question via the official LINE account: "When is the next game?" The server analyzes the received question using a natural language processing engine and understands the intent of the question. The server retrieves the information "The next game is on August 15th" from the database and inputs the following prompt sentence into the generative AI model:

[0671] "Generate an answer using the date of the next match."

[0672] Based on this prompt, the generative AI model generates an answer such as "The next game is on August 15th," and automatically replies to the user using the LINE API.

[0673] Examples of donation solicitation messages

[0674] The server periodically generates donation messages using a generative AI model. For example, before the next game, it inputs the following prompt:

[0675] "Create a message before your next game asking for donations."

[0676] Based on this prompt, the generative AI model generates a message saying, "Please support the next game. Donate here!" The generated message is sent to all users as a push notification using the notification API. Users receive the notification and click the link to be taken to a donation page where they can enter the required information to complete their donation.

[0677] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[0678] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0679] Step 1: Data collection

[0680] The server retrieves data such as match results, player information, and event schedules from the sports organization's management terminal, using an HTTP request to retrieve data in JSON format.

[0681] Input: Data obtained from the management terminal API (JSON format).

[0682] Data processing: Analyze the received data and extract the necessary information (e.g., match results, player information).

[0683] Output: Extracted sports team data (match results, player information, event schedules).

[0684] Step 2: Content generation

[0685] The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) based on the data extracted in step 1, and generates promotional content.

[0686] Input: Extracted sports organization data, prompt (e.g., "Generate a news article for fans using yesterday's game results.").

[0687] Data processing: Generative AI models generate news articles and event information in natural language based on prompts and data.

[0688] Output: Generated promotional content (news articles, event information).

[0689] Step 3: Administrator Verification

[0690] The server sends the generated content to the sports organization's administrator for review and approval.

[0691] Input: Generated promotional content (news articles, event information).

[0692] Data processing: The administrator will be notified by email or via the management screen to request that the content be reviewed.

[0693] Output: Approval or corrective feedback from management.

[0694] Step 4: Post your content

[0695] After administrator approval, the server automatically posts the content to the LINE Official Account's timeline.

[0696] Input: Content approved by administrator.

[0697] Data processing: Uses the LINE API to post approved content to the timeline.

[0698] Output: Content posted to an external service.

[0699] Step 5: Receiving user questions

[0700] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0701] Input: A question message from the user.

[0702] Data processing: Receive questions using LINE's chat function.

[0703] Output: The query message received.

[0704] Step 6: Question Analysis

[0705] The server analyzes the received question using a natural language processing engine (e.g., BERT) to understand the intent of the question.

[0706] Input: The received question message.

[0707] Data processing: Perform text analysis to analyze keywords and context.

[0708] Output: Analysis results (question intent, keywords).

[0709] Step 7: Answer Generation

[0710] Based on the analysis results, the server retrieves relevant information from the database and generates an appropriate response using a generative AI model (e.g., GPT-4).

[0711] Input: Analysis results, relevant information from the database, and a prompt (e.g., "Generate an answer using the date of the next game.").

[0712] Data processing: Execute a database query to obtain the necessary information, and input a prompt into the generative AI model to generate an answer.

[0713] Output: The generated answer.

[0714] Step 8: Submit your response

[0715] The server automatically returns the generated answer to the user.

[0716] Input: The generated answer text.

[0717] Data processing: Use the LINE API to send the response to the user.

[0718] Output: The answer message sent to the user.

[0719] Step 9: Generate a donation message

[0720] The server periodically generates donation solicitation messages using a generative AI model.

[0721] Input: Prompt text (e.g., "Write a message to collect donations before the next game.").

[0722] Data processing: The generative AI model generates a donation solicitation message based on the prompt text.

[0723] Output: The generated donation message.

[0724] Step 10: Send the message

[0725] The server sends the generated message to all users as a push notification.

[0726] Input: The generated donation message.

[0727] Data processing: Push notifications are sent using the notification API.

[0728] Output: The push notification sent to the user device.

[0729] Step 11: Display the donation page

[0730] When the user clicks on the link to the donation page based on the message they received, the device displays the donation page.

[0731] Input: Link to donation page.

[0732] Data processing: Launch a browser and display the URL of the donation page.

[0733] Output: The donation page displayed.

[0734] Step 12: Make a donation

[0735] The user enters the required information on the donation page and completes the donation.

[0736] Input: Donation information (donation amount, payment method, etc.).

[0737] Data processing: Enter donation information into the form and click the submit button.

[0738] Output: Notification of donation completion.

[0739] In this way, each processing step involves specific operations and data processing, and the entire system is designed to operate efficiently using generative AI models and natural language processing techniques.

[0740] (Application example 1)

[0741] 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."

[0742] The present invention provides a system for automating public relations and donation solicitation activities in fields such as amateur sports organizations and food delivery. In particular, there is a need for centralized management of user question and answering, content automatic generation and posting, and donation solicitation message generation and transmission, all of which must be performed efficiently and with high accuracy. Conventional methods require these tasks to be performed manually, which requires a significant amount of time and effort, creating a need for automation to improve efficiency.

[0743] 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.

[0744] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to users, means for generating and sending a donation solicitation message to users, means for directing users to a donation page, means for automatically generating a recommendation menu based on user preference information, and means for sending the generated content to all users using push notifications. This enables unified and efficient operation of public relations activities, user support, promotional activities, and donation solicitations.

[0745] "Generation data" refers to data that is input into the system and is information that is used to generate content.

[0746] "Content" is a collection of information created based on generated data, and is material provided to users for public relations, promotion, or information purposes.

[0747] A "platform" is a foundation for users and systems to exchange information, including online communication tools and applications.

[0748] A "user" is an entity that uses the system to send and receive information, such as asking questions, viewing publicity content, and making donations.

[0749] A "natural language processing engine" is a technology that analyzes questions and input text from users and understands their meaning.

[0750] A "generative AI model" is a type of artificial intelligence used to automatically generate content based on data, and has the ability to generate responses to specific prompts.

[0751] A "donation solicitation message" is a message sent to users to encourage them to make donations, and includes information related to a specific campaign or event.

[0752] A "donation page" is a web page or screen within an application that allows users to make donations.

[0753] A "recommended menu" is a list of dishes and services that is automatically generated using a generative AI model based on the user's preference information.

[0754] "Push notification" is a feature that sends content or messages directly to a user's device, with the aim of attracting the user's attention.

[0755] A detailed embodiment of the system according to the present invention will be described below. The system automates public relations and donation collection activities, and particularly improves the efficiency of food delivery service operations. The system is composed of a server, a user terminal, and a platform.

[0756] The server implements the following main functions:

[0757] 1. Receiving generated data

[0758] 2. Automated content generation

[0759] 3. Posting Generated Content to the Platform

[0760] 4. Receiving and analyzing user questions

[0761] 5. Generate and reply to appropriate answers

[0762] 6. Creating and sending donation solicitation messages

[0763] 7. Direct users to a donation page

[0764] 8. Automatic Generation of Recommendation Menus Based on User Preferences

[0765] 9. Sending to all users using push notifications

[0766] The user terminal is used to input questions, generate content, and access the donation page. The platform uses online communication tools to mediate the exchange of information between the user and the server.

[0767] Hardware and software used

[0768] The system's server uses a database (MySQL), a generative AI model (OpenAI GPT-3), a natural language processing engine (NLTK), and a push notification service (Firebase Cloud Messaging). User devices are primarily mobile devices such as smartphones and tablets. The platform used is a common online communication tool (e.g., LINE).

[0769] Data processing and calculation

[0770] 1. Data collection and content generation

[0771] The server collects the latest menu and promotion information from the food delivery company's database. Based on this data, it uses a generative AI model to automatically generate content for users. For example, it generates a promotional message such as, "Today's recommended menu items are sushi, tempura, and ramen."

[0772] 2. Responding to user questions

[0773] When a user submits a question (e.g., "What's your recommendation today?"), the server uses a natural language processing engine to analyze the intent of the question. Based on the analysis results, it uses a generative AI model to generate an appropriate answer and sends it back to the user.

[0774] 3. Generate and send donation messages

[0775] The server periodically generates donation solicitation messages and sends them to all users using the push notification service. For example, a message generated in response to the prompt "Please create a new donation campaign message" might include something like "Would you like to donate 50 meals as part of our winter campaign?"

[0776] Specific examples

[0777] Example of generating a recommended menu: Using a generative AI model (OpenAI GPT-3) based on user preference information, a prompt such as "User preference: spicy food" is input, and "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot" is generated.

[0778] Example of response to user question: The server analyzes the question "What is your recommendation today?" and generates and returns the answer "Today, we recommend Margherita pizza and Caprese salad."

[0779] Example of a donation message prompt: "Write a message for our new donation campaign."

[0780] In this way, this system can achieve automation and efficiency in food delivery operations by utilizing generative AI models and natural language processing technology.

[0781] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0782] Step 1:

[0783] The server collects the latest menu and promotion information from food delivery companies' databases.

[0784] Input: Latest menu and promotion information available in the database

[0785] Output: Collected menu and promotion information

[0786] What happens: The server uses a REST API to access the database and retrieve the latest menu and promotion information, which is then used in subsequent processing steps.

[0787] Step 2:

[0788] Based on the collected data, the server automatically generates public relations or promotional content using a generative AI model (OpenAI GPT-3).

[0789] Input: Latest menu and promotion information

[0790] Output: Auto-generated promotional or promotional content

[0791] Specific operation: The server inputs the prompt "Please create a promotional message based on the latest food delivery menu" into the generative AI model, and generates content such as "Today's recommended menu items are sushi, tempura, and ramen."

[0792] Step 3:

[0793] The server posts the generated content to a platform (e.g., an online communication tool).

[0794] Input: Auto-generated PR or promotional content

[0795] Output: Content posted to the platform

[0796] Specific operation: The server sends the generated content to the platform's submission endpoint using the REST API so that it can be displayed to the user.

[0797] Step 4:

[0798] The user uses the device to enter a question within the app and send it to the server.

[0799] Input: A question from the user (e.g., "What's your recommendation today?")

[0800] Output: User question sent to server

[0801] Specific operation: The user enters a question in the message input field of the app and presses the send button, and the question is sent to the server.

[0802] Step 5:

[0803] The server analyzes the received question using a natural language processing engine (NLTK) to understand the intent of the question.

[0804] Input: User question sent to server

[0805] Output: Parsed question intent

[0806] Specific operation: The server uses a natural language processing engine to analyze the text of the received question and extract the intent (e.g., "I would like to know what menu items are recommended").

[0807] Step 6:

[0808] Based on the intent of the analyzed question, the server uses a generative AI model to generate an appropriate answer and returns it to the user.

[0809] Input: Parsed question intent

[0810] Output: Auto-generated answer

[0811] Specific operation: The server inputs the prompt "What's your recommendation today?" into the generative AI model, which generates the answer "Margherita pizza and Caprese salad are recommended today." It then sends this answer back to the user.

[0812] Step 7:

[0813] The server generates a donation solicitation message using a generative AI model and sends it to all users via push notification.

[0814] Input: Generate Donation Message Prompt

[0815] Output: The donation message sent to the user

[0816] Specific operation: The server inputs the prompt "Please create a message for a new donation campaign" into the generative AI model, which generates the message "Would you like to donate 50 meals as part of our winter campaign?". It then uses Firebase Cloud Messaging to send a push notification to all users.

[0817] Step 8:

[0818] A user receives a donation message and clicks on a link to a donation page.

[0819] Input: Donation message received as a push notification

[0820] Output: Display of donation page

[0821] What happens: When a user clicks on the link in the push notification, a browser or in-app browser opens to a donation page where the user can make a donation.

[0822] Step 9:

[0823] The server automatically generates a recommendation menu based on user preference information using a generative AI model.

[0824] Input: User preference information

[0825] Output: Recommendation menu

[0826] Specific operation: The server inputs the prompt "User preference: spicy food" into the generative AI model, and automatically generates candidates such as "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot." The generated recommended menu is then presented to the user.

[0827] 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.

[0828] This invention combines a system for automating public relations and fundraising activities for amateur sports organizations with an emotion engine that recognizes user emotions. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users. Furthermore, the emotion engine enables appropriate responses based on the user's emotional state.

[0829] System Configuration

[0830] This system mainly consists of the following components:

[0831] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing and an emotion engine, and automatic message sending.

[0832] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0833] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[0834] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[0835] Program Overview

[0836] The program processing of this system will be explained in natural language below.

[0837] 1. Data collection and content generation

[0838] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0839] The generated data obtained by the server is saved in a database.

[0840] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0841] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0842] The administrator (user) approves the content and makes corrections as necessary.

[0843] The server automatically posts the approved content to the LINE Official Account's timeline.

[0844] 2. Automated responses to user questions

[0845] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[0846] The server receives the question and analyzes it using a natural language processing engine.

[0847] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0848] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0849] The server inputs the information it obtains and the results of the emotion engine into a generative AI model to generate an appropriate answer to the question.

[0850] The generated response is automatically sent back to the user in a tone and content that reflects the user's emotional state.

[0851] 3. Sending donation messages

[0852] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0853] The server sends the generated message to all users as a push notification from the LINE official account.

[0854] A user receives a push notification and clicks a link in the message.

[0855] The device (such as the user's smartphone) opens the link and displays the donation page.

[0856] The user completes the donation process and enters the amount on the donation page.

[0857] The device will complete the donation process and display a notification that the donation is complete.

[0858] Specific examples

[0859] Example 1: Creating promotional content for match results

[0860] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[0861] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[0862] Example 2: Automatic response to user questions

[0863] A user submits a question: "When is the next game?"

[0864] The server analyzes the question and uses an emotion engine to determine the user's emotional state as "excited."

[0865] The server retrieves information from the database that "The next game is on August 15th," and uses a generation AI to generate a response that reads, "The next game is on August 15th. Please cheer us on!"

[0866] The generated response is automatically sent back to the user.

[0867] In this way, by utilizing generative AI and natural language processing technology as well as an emotion engine, this system is able to automate more appropriate public relations and donation solicitation activities according to the user's emotional state.

[0868] The processing flow will be explained below.

[0869] Data collection and content generation process steps

[0870] Step 1:

[0871] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations.

[0872] Step 2:

[0873] The generated data obtained by the server is saved in a database.

[0874] Step 3:

[0875] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[0876] Step 4:

[0877] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[0878] Step 5:

[0879] The administrator (user) approves the content and makes corrections as necessary.

[0880] Step 6:

[0881] The server automatically posts the approved content to the LINE Official Account's timeline.

[0882] Processing steps for automatic responses to user questions

[0883] Step 1:

[0884] A user sends a question via the LINE official account.

[0885] Step 2:

[0886] The server receives the question and analyzes it using a natural language processing engine.

[0887] Step 3:

[0888] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0889] Step 4:

[0890] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[0891] Step 5:

[0892] The information acquired by the server is input into a generative AI model to generate an answer appropriate to the user's emotional state.

[0893] Step 6:

[0894] The server returns the generated answer to the user.

[0895] Process steps for sending a donation message

[0896] Step 1:

[0897] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[0898] Step 2:

[0899] The server sends the generated message to all users as a push notification from the LINE official account.

[0900] Step 3:

[0901] A user receives a push notification and clicks a link in the message.

[0902] Step 4:

[0903] The device (such as the user's smartphone) opens the link and displays the donation page.

[0904] Step 5:

[0905] The user completes the donation process and enters the amount on the donation page.

[0906] Step 6:

[0907] The device will complete the donation process and display a notification that the donation is complete.

[0908] Example: Automated responses to user questions

[0909] Example 1: If the user's question is "When is the next game?"

[0910] Step 1: A user sends a question to the LINE Official Account asking, "When is the next game?"

[0911] Step 2: The server receives the question and uses its natural language processing engine to parse it as asking "When is the next game?"

[0912] Step 3: When the server parses the question, it uses an emotion engine to detect the emotion of "excitement" from the user's text.

[0913] Step 4: The server retrieves the game schedule data from the database and confirms the information: "The next game is on August 15th."

[0914] Step 5: The information obtained by the server is input into the generative AI model, which generates a response that matches the user's emotions: "The next game is on August 15th. Please cheer us on!"

[0915] Step 6: The server automatically sends the generated answer back to the user.

[0916] In this way, by combining generative AI, natural language processing, and emotion engine technologies, this system can automate appropriate public relations and donation collection activities according to the user's emotional state.

[0917] Example 2

[0918] 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."

[0919] Today, amateur sports organizations spend a lot of time and manpower on effective public relations and fundraising activities. However, there is a lack of systems to automate these activities, making it particularly difficult to respond appropriately to user emotions. Furthermore, there is a lack of mechanisms for quickly and accurately answering user questions. This makes it difficult to increase user satisfaction, and as a result, fundraising is not effective.

[0920] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to a communication network, means for receiving questions from users, means for analyzing the received questions and acquiring information related to the questions from a database, means for automatically generating appropriate answers based on the acquired information, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to donation pages, means for analyzing the emotional state of the users, and means for generating appropriate content and answers based on the analyzed emotional state. This makes it possible to automate public relations activities and donation solicitation activities and respond appropriately according to the user's emotional state, thereby increasing user satisfaction and enabling effective donation solicitation.

[0921] "Means for receiving generated data" refers to a device or software that has the function of obtaining data such as match results, player information, and event schedules from the sports organization's management terminal or other information sources.

[0922] "Means for automatically generating content" refers to a device or software that has the function of automatically generating advertising or public relations content based on received data.

[0923] A "means for posting to a communications network" is any device or software capable of automatically posting generated content to an online platform.

[0924] The "means for receiving questions from users" refers to a device or software that has the function of receiving messages or questions from users via the online platform.

[0925] The "means for analyzing a question" is a device or software that has the function of analyzing a received question using a natural language processing engine and understanding the content of the question.

[0926] The "means for retrieving information from a database" is a device or software that has the function of extracting information related to the analyzed question from a database.

[0927] The "means for automatically generating appropriate answers" refers to a device or software that has the function of automatically generating answers to user questions based on the acquired information and analysis results.

[0928] The "means for returning an answer to a user" is a device or software that has the function of automatically sending the generated answer to the user.

[0929] A "means for generating a solicitation message" is a device or software capable of automatically generating a message suitable for a solicitation campaign.

[0930] The "means for sending to a user" is a device or software that has the function of sending the generated solicitation message to a user.

[0931] A "means for directing users to a donation page" is a device or software that has the function of automatically providing a message containing a link or instructions that directs users to a web page where they can make a donation.

[0932] A "means for analyzing a user's emotional state" is a device or software that has an engine or algorithm that analyzes emotions from a user's input text or voice.

[0933] The "means for generating appropriate content and answers" refers to a device or software that has the function of automatically generating content and answers that correspond to the user's emotions based on the analyzed emotional state.

[0934] This invention relates to a system that automates the public relations and fundraising activities of amateur sports organizations and enables them to respond to user emotions. This system has the functionality to automatically generate content based on generated data and to automatically generate answers to user questions.

[0935] System Configuration

[0936] This system consists of the following components:

[0937] 1. Server: Mainly manages membership, collects data, automatically generates content using generative AI models, answers questions using natural language processing and an emotion engine, and sends automatic messages.

[0938] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[0939] 3. Platform: A medium for exchanging information between users and sports organizations via online communication tools (e.g., LINE official account).

[0940] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[0941] Detailed function description

[0942] 1. Data collection and content generation

[0943] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[0944] Store the generated data in a relational database (e.g., MySQL).

[0945] The server inputs the saved data into a generative AI model (e.g., OpenAI GPT-3) to automatically generate PR content. For example, the prompt could be, "Generate PR content about yesterday's game results. The game score was 3-2, and the game date was yesterday."

[0946] The generated content is sent to an administrator's terminal, where the administrator checks the content and makes corrections as necessary.

[0947] When the administrator clicks the approval button, the server posts the approved content to the LINE Official Account's timeline.

[0948] 2. Automated responses to user questions

[0949] The server receives the question (e.g., "When is the next game?") sent by the user via the LINE Official Account.

[0950] The server uses a natural language processing engine (e.g., spaCy) to analyze the question and extract key content.

[0951] An emotion engine (e.g., IBM Watson Tone Analyzer) is used to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[0952] The server retrieves relevant information (e.g. "date of next game") from a database using an SQL query.

[0953] Based on the acquired information and the results of sentiment analysis, a prompt is input into the generative AI model to generate a response. A possible prompt might be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[0954] The generated answer is automatically sent back to the user via the LINE Messaging API.

[0955] 3. Sending donation messages

[0956] The server automatically generates a donation message by inputting it into the AI ​​model depending on the start time of the donation campaign. For example, it uses the prompt, "Please create a message for the donation campaign starting next week. The main points are to explain the importance of donations and how to donate."

[0957] The server sends the generated message to all users as a push notification from the LINE official account.

[0958] When a user receives the push notification and clicks on the link in the message, the user's device will display a donation page.

[0959] The user completes the donation procedure on the donation page, and the device displays a notification that the donation has been completed.

[0960] This system automates public relations and fundraising activities and responds appropriately to the user's emotional state, which not only increases user satisfaction but also enables effective fundraising.

[0961] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] The server obtains generated data (e.g., match results, player information, event schedules) from the sports organization's management terminal. The server periodically issues API requests, receives data from the management terminal, and imports it in JSON format. The obtained generated data is stored in a relational database (e.g., MySQL).

[0964] Input: Data generated from the management terminal of a sports organization

[0965] Data processing: Convert JSON data into database format

[0966] Output: Generated data stored in a database

[0967] Step 2:

[0968] The server inputs PR content into a generative AI model (e.g., OpenAI GPT-3) based on the saved generated data, and generates automatic content. For example, the prompt text could be, "Generate a PR text about the results of yesterday's game. The game score was 3-2, and the game date was yesterday." The generated content is temporarily saved in JSON format.

[0969] Input: Generated data stored in a database

[0970] Data processing: Input to generative AI models

[0971] Output: Generated promotional content

[0972] Step 3:

[0973] The generated PR content is sent to the administrator's terminal. The administrator (user) uses the management interface to check the generated content and make corrections as necessary. Once corrections are complete, the administrator clicks the approval button.

[0974] Input: Generated PR content, admin modifications

[0975] Data processing: Correction and approval through the management interface

[0976] Output: Approved content

[0977] Step 4:

[0978] The server posts the approved content to the LINE Official Account's timeline automatically using the LINE API.

[0979] Input: Approved Content

[0980] Data processing: Posting using LINE API

[0981] Output: Content posted to the LINE Official Account's timeline

[0982] Step 5:

[0983] A user sends a question (e.g., "When is the next game?") via the LINE Official Account. The server receives the message using the LINE Messaging API and obtains the text data.

[0984] Input: Question message from user

[0985] Data processing: Receiving text data

[0986] Output: User question data

[0987] Step 6:

[0988] The server uses a natural language processing engine (e.g., spaCy) to analyze the question, and based on the extracted intent and keywords, retrieves relevant information (e.g., "next game schedule") from a database using an SQL query.

[0989] Input: User question data

[0990] Data processing: Question analysis using natural language processing, database search using SQL queries

[0991] Output: Related information (e.g., next game date)

[0992] Step 7:

[0993] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state. Based on the analyzed emotional state (e.g., joy, anger, sadness, etc.), a generative AI model is used to generate a response with an appropriate tone and content. An example of a prompt could be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[0994] Input: relevant information, user's emotional state

[0995] Data processing: Generating answers using a generative AI model

[0996] Output: Generated answer

[0997] Step 8:

[0998] The generated response is sent back to the user via the LINE Messaging API. The server sends the generated response as a message.

[0999] Input: Generated answer

[1000] Data processing: Sending messages using the LINE API

[1001] Output: The answer message sent to the user

[1002] Step 9:

[1003] The server inputs the donation solicitation message into the AI ​​model according to the start date of the donation solicitation campaign and automatically generates the message. For example, it uses the prompt "Please create a message for the donation campaign starting next week. The main points should be to explain the importance of donations and how to donate." The generated message is sent to all users as a push notification to the LINE official account.

[1004] Input: Donation message template

[1005] Data processing: Message generation using generative AI models

[1006] Output: Generated donation message

[1007] Step 10:

[1008] When a user receives a push notification and clicks the link in the message, the user's device will display a donation page. The user enters the donation amount and provides the necessary information on the donation page. The device will then complete the donation process and display a notification that the donation has been completed.

[1009] Input: Push notification link, user input information

[1010] Data processing: Displaying donation pages and carrying out donation procedures

[1011] Output: Notification of donation completion

[1012] In this way, the system can effectively promote and solicit donations throughout the entire process.

[1013] (Application example 2)

[1014] 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."

[1015] Amateur sports organizations are required to streamline their public relations and donation-raising activities while also providing appropriate responses based on user emotions. Conventional systems lack sufficient emotional responses, which can lead to a poor user experience. In particular, accurately capturing the enthusiasm and emotions of sports fans and providing appropriate public relations and donation-raising messages is crucial for expanding support for the organization.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1017] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means including an emotion analysis engine for analyzing the user's emotions and adjusting the content and answer sentences based on the analyzed emotions. This enables public relations activities and donation solicitations to be conducted in accordance with the user's emotions, thereby effectively spreading support for sports organizations.

[1018] "Generated Data" refers to information generated by sports organizations, such as match results, player information, and event schedules.

[1019] "Content" refers to promotional text and images automatically generated by a generative AI model based on generated data.

[1020] "Platform" refers to an intermediary system, such as an online communication tool or social networking site, that allows users to exchange information with sports organizations.

[1021] A "question" refers to an inquiry that a user inputs to the system.

[1022] "Emotion analysis engine" refers to an engine for analyzing a user's emotional state from input text or voice.

[1023] A "donation solicitation message" refers to a message created by a sports organization to call on users for donations.

[1024] A "generative AI model" refers to an artificial intelligence model that performs natural language processing, image generation, etc. based on input data.

[1025] A "natural language processing engine" refers to an engine that analyzes text data entered by a user, understands its meaning, and generates an appropriate response.

[1026] MODE FOR CARRYING OUT THE INVENTION

[1027] The present invention is a system for automating public relations and donation collection activities for amateur sports organizations, and in particular, a system that uses an emotion analysis engine to realize appropriate responses according to the user's emotions. To achieve this purpose, the system includes the following components and processing steps:

[1028] 1. System Configuration

[1029] This system mainly consists of a server, a user terminal, and a platform.

[1030] server:

[1031] The server is the main component that receives generated data, automatically generates content, analyzes questions and generates answers, generates and sends donation messages, and analyzes user sentiment. Specifically, the following hardware and software are used:

[1032] Hardware: High-performance server unit

[1033] Software: Generative AI models, natural language processing engines, sentiment analysis engines (e.g., Hugging Face Transformers)

[1034] User device:

[1035] A user terminal is the device that a user uses to enter questions, receive answers, and access the donation page, typically a smartphone or personal computer.

[1036] Platform:

[1037] The platform is a system that includes online communication tools and social networking sites, and serves as a medium for the smooth exchange of information between users and sports organizations.

[1038] 2. Program Processing

[1039] The server performs the following process:

[1040] 1. Receiving generated data and generating content

[1041] The server periodically acquires generated data such as match results, player information, and event schedules from the sports organization's management terminal, and then uses this data to apply a generative AI model to automatically generate promotional content.

[1042] 2. Question Analysis and Answer Generation

[1043] When a user submits a question through the platform, the server receives the question and analyzes it using a natural language processing engine. Based on the analysis results, the sentiment analysis engine identifies the user's emotional state, retrieves relevant information from the database, and generates an appropriate answer that is tailored to the user's emotions.

[1044] 3. Generate and send a donation message

[1045] The server generates a donation message based on the start time of the donation campaign and sends it as a push notification to all users, who can then access the donation page.

[1046] 3. Examples and prompts

[1047] Example 1: Creating promotional content for match results

[1048] The server receives the match result data and uses a generative AI model to generate a sentence such as, "Yesterday's match was a close one, but we won 3-2." The generated sentence is then reviewed and approved by an administrator and automatically posted to the platform's timeline.

[1049] Example 2: Automatic response to user questions

[1050] When a user sends a question such as "When is the next game?", the server analyzes the question and uses an emotion analysis engine to determine the user's emotional state as "excited." The server then retrieves the information "The next game is on August 15th" from the database and uses a generative AI model to generate an automatic response saying "The next game is on August 15th. Please cheer us on!"

[1051] Prompt Sentence Examples

[1052] "Generate a news article from the following data: [generated data]"

[1053] "Generate a friendly, positive message about the next game: The next game is on August 15."

[1054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1055] Step 1:

[1056] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations. This input data is sent to the server via API and stored in a database.

[1057] Step 2:

[1058] The server inputs the generated data in the database into a generative AI model to automatically generate PR content. Specifically, it uses the prompt "Generate a news article from the following data: [generated data]" and outputs content in the form of a news article. This output is temporarily saved and awaits confirmation by an administrator.

[1059] Step 3:

[1060] The server sends the generated content to an administrator terminal, who reviews the content and makes any necessary corrections. The server then receives the approved content and automatically posts it to the platform's timeline. The output of this step is the published news content.

[1061] Step 4:

[1062] When a user submits a question through the platform, the server receives the question. The question is sent to the server in text format and input into a natural language processing engine for analysis. The engine analyzes the meaning of the question and identifies the intent of the question as its output.

[1063] Step 5:

[1064] The server identifies the user's emotional state based on the analysis results using a sentiment analysis engine, which extracts emotions from the user's text and outputs emotional states such as "excitement," "joy," and "anger."

[1065] Step 6:

[1066] The server retrieves relevant information from the database based on the results of the sentiment analysis. For example, if the question is "When is the next game?", it retrieves the date and time of the next game. This output information is used in the next step.

[1067] Step 7:

[1068] The server inputs the acquired information and the results of the sentiment analysis engine into a generative AI model to generate an appropriate response. It uses a prompt such as "Generate a friendly, positive message about the next game: The next game is on August 15." and outputs an appropriate response for the user.

[1069] Step 8:

[1070] The generated answer is automatically sent back to the user. A reply message is sent to the user's device via the platform's chat function. The output of this step is the appropriate answer delivered to the user.

[1071] Step 9:

[1072] The server generates a donation message based on the start date of the donation campaign. Using a generative AI model, it outputs a message calling for donations: "We need your support! Please donate to our team to keep us going."

[1073] Step 10:

[1074] The server sends the generated donation message to all users as a platform push notification. When a user receives the notification and clicks on the link in the message, the user's device is redirected to the donation page. The output of this step is a user directed to the donation page.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] [Third embodiment]

[1079] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1080] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1081] 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).

[1082] 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.

[1083] 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.

[1084] 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).

[1085] 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.

[1086] 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.

[1087] 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.

[1088] 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.

[1089] 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.

[1090] 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."

[1091] This invention is a system for automating the public relations and fundraising activities of amateur sports organizations. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[1092] System Configuration

[1093] This system mainly consists of the following components:

[1094] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing, and automatic message sending.

[1095] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1096] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[1097] Program Overview

[1098] The program processing of this system will be explained in natural language below.

[1099] 1. Data collection and content generation

[1100] The server periodically obtains data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1101] Based on the data acquired by the server, a generative AI model is used to automatically generate promotional content (news articles, event information, etc.).

[1102] The generated content is first reviewed by an administrator (a sports organization staff member) and then posted to the LINE official account timeline after approval.

[1103] 2. Automated responses to user questions

[1104] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1105] The server analyzes the received question using a natural language processing engine to understand the intent of the question.

[1106] Based on the analysis results, the server retrieves relevant information from the database and uses generation AI to generate an appropriate response.

[1107] The generated answer is automatically sent back to the user.

[1108] 3. Sending donation messages

[1109] The server periodically generates messages to collect donations (e.g., seasonal campaigns, collections before specific events, etc.).

[1110] The generated message is sent to all users as a push notification.

[1111] When the user clicks on the link to the donation page based on the message received, the terminal displays the donation page and the user can make a donation.

[1112] Specific examples

[1113] Example 1: Creating promotional content for match results

[1114] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[1115] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[1116] Example 2: Automatic response to user questions

[1117] A user submits a question: "When is the next game?"

[1118] The server analyzes the question, retrieves information from the database such as "The next game is on August 15th," and generates a sentence using a generative AI.

[1119] The generated response is automatically sent back to the user.

[1120] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[1121] The processing flow will be explained below.

[1122] Data collection and content generation process steps

[1123] Step 1:

[1124] The server acquires generated data such as match results, player information, and event schedules from the management terminal of the sports organization.

[1125] Step 2:

[1126] The generated data obtained by the server is saved in a database.

[1127] Step 3:

[1128] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1129] Step 4:

[1130] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1131] Step 5:

[1132] The administrator (user) approves the content and makes corrections as necessary.

[1133] Step 6:

[1134] The server automatically posts the approved content to the LINE Official Account's timeline.

[1135] Processing steps for automatic responses to user questions

[1136] Step 1:

[1137] A user sends a question via the LINE official account.

[1138] Step 2:

[1139] The server receives the question and analyzes it using a natural language processing engine.

[1140] Step 3:

[1141] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1142] Step 4:

[1143] The information acquired by the server is input into a generative AI model to generate an appropriate answer to the question.

[1144] Step 5:

[1145] The server returns the generated answer to the user.

[1146] Process steps for sending a donation message

[1147] Step 1:

[1148] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1149] Step 2:

[1150] The server sends the generated message to all users as a push notification from the LINE official account.

[1151] Step 3:

[1152] A user receives a push notification and clicks a link in the message.

[1153] Step 4:

[1154] The device (such as the user's smartphone) opens the link and displays the donation page.

[1155] Step 5:

[1156] The user completes the donation process and enters the amount on the donation page.

[1157] Step 6:

[1158] The device will complete the donation process and display a notification that the donation is complete.

[1159] These processing steps allow amateur sports organizations to efficiently and effectively automate their public relations and fundraising activities.

[1160] Example 1

[1161] 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."

[1162] Improving the efficiency of public relations and fundraising activities for amateur sports organizations is an important issue for many organizations. However, conventional methods require manual content creation, responding to user inquiries, and creating donation solicitation messages. These tasks are burdensome due to labor shortages and time constraints, making efficient management difficult. In addition, maintaining the quality and consistency of content is difficult, limiting the effectiveness of public relations and fundraising activities.

[1163] 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.

[1164] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to an external service, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means for an administrator to approve the content generation process. This automates public relations and donation solicitation activities, reducing the workload and improving quality.

[1165] "Generated data" refers to data received by the system, such as sporting events, player information, and event schedules.

[1166] "Content" refers to public relations information such as news articles and event information created using a generative AI model based on generated data.

[1167] "External Services" refers to online communication tools and social media platforms that allow users to interact with the website.

[1168] "User" refers to any person or entity that submits a question, receives content, or makes a donation through the System.

[1169] A "generative AI model" is a model that uses artificial intelligence technology to generate sentences in natural language from input data.

[1170] A "natural language processing engine" refers to technology that analyzes questions from users and understands their intent and content.

[1171] A "database" is a collection of data that a system uses to store information and search and retrieve it as needed.

[1172] A "donation solicitation message" is a message generated to encourage users to make donations.

[1173] "Push notifications" are notifications sent directly to users' devices, providing them with immediate access to important information and messages.

[1174] A "prompt" is a command or instruction given to a generative AI model to make it generate specific sentences based on input data.

[1175] The present invention is a system for automating public relations and fundraising activities for amateur sports organizations. This system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[1176] System Configuration

[1177] This system mainly consists of the following components:

[1178] 1. Server: Collects data, automatically generates content using generative AI models, answers questions using natural language processing, and sends automatic messages.

[1179] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1180] 3. External services: Online communication tools that act as a medium for exchanging information between users and sports organizations.

[1181] Specific hardware and software

[1182] Hardware:

[1183] Database Server: Used to store data.

[1184] High-performance GPU server: Used to run generative AI models.

[1185] software:

[1186] Data Collection API: Used to retrieve data from sports organization management tools.

[1187] Generative AI models (e.g., OpenAI's GPT-4): Used for content generation.

[1188] Natural language processing engine (e.g. BERT): Used to analyze user questions.

[1189] External service APIs (e.g., LINE API): Used to post generated content and communicate with users.

[1190] Specific examples of data collection and content generation

[1191] The server periodically retrieves data such as match results, player information, and event schedules from the sports organization's management terminal. This data is received in JSON format using HTTP requests. The server then analyzes the retrieved data and inputs the following prompts to the generative AI model:

[1192] "Generate news articles for your fans using yesterday's game results."

[1193] The generative AI model generates content based on this prompt as follows:

[1194] "Yesterday's game was a close one, but we won 3-2."

[1195] The generated content is sent to the sports organization's administrator, who reviews and approves it. Once approved, the content is automatically posted to the LINE Official Account's timeline using the LINE API.

[1196] Examples of automated responses to user questions

[1197] The user sends a question via the official LINE account: "When is the next game?" The server analyzes the received question using a natural language processing engine and understands the intent of the question. The server retrieves the information "The next game is on August 15th" from the database and inputs the following prompt sentence into the generative AI model:

[1198] "Generate an answer using the date of the next match."

[1199] Based on this prompt, the generative AI model generates an answer such as "The next game is on August 15th," and automatically replies to the user using the LINE API.

[1200] Examples of donation solicitation messages

[1201] The server periodically generates donation messages using a generative AI model. For example, before the next game, it inputs the following prompt:

[1202] "Create a message before your next game asking for donations."

[1203] Based on this prompt, the generative AI model generates a message saying, "Please support the next game. Donate here!" The generated message is sent to all users as a push notification using the notification API. Users receive the notification and click the link to be taken to a donation page where they can enter the required information to complete their donation.

[1204] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Step 1: Data collection

[1207] The server retrieves data such as match results, player information, and event schedules from the sports organization's management terminal, using an HTTP request to retrieve data in JSON format.

[1208] Input: Data obtained from the management terminal API (JSON format).

[1209] Data processing: Analyze the received data and extract the necessary information (e.g., match results, player information).

[1210] Output: Extracted sports team data (match results, player information, event schedules).

[1211] Step 2: Content generation

[1212] The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) based on the data extracted in step 1, and generates promotional content.

[1213] Input: Extracted sports organization data, prompt (e.g., "Generate a news article for fans using yesterday's game results.").

[1214] Data processing: Generative AI models generate news articles and event information in natural language based on prompts and data.

[1215] Output: Generated promotional content (news articles, event information).

[1216] Step 3: Administrator Verification

[1217] The server sends the generated content to the sports organization's administrator for review and approval.

[1218] Input: Generated promotional content (news articles, event information).

[1219] Data processing: The administrator will be notified by email or via the management screen to request that the content be reviewed.

[1220] Output: Approval or corrective feedback from management.

[1221] Step 4: Post your content

[1222] After administrator approval, the server automatically posts the content to the LINE Official Account's timeline.

[1223] Input: Content approved by administrator.

[1224] Data processing: Uses the LINE API to post approved content to the timeline.

[1225] Output: Content posted to an external service.

[1226] Step 5: Receiving user questions

[1227] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1228] Input: A question message from the user.

[1229] Data processing: Receive questions using LINE's chat function.

[1230] Output: The query message received.

[1231] Step 6: Question Analysis

[1232] The server analyzes the received question using a natural language processing engine (e.g., BERT) to understand the intent of the question.

[1233] Input: The received question message.

[1234] Data processing: Perform text analysis to analyze keywords and context.

[1235] Output: Analysis results (question intent, keywords).

[1236] Step 7: Answer Generation

[1237] Based on the analysis results, the server retrieves relevant information from the database and generates an appropriate response using a generative AI model (e.g., GPT-4).

[1238] Input: Analysis results, relevant information from the database, and a prompt (e.g., "Generate an answer using the date of the next game.").

[1239] Data processing: Execute a database query to obtain the necessary information, and input a prompt into the generative AI model to generate an answer.

[1240] Output: The generated answer.

[1241] Step 8: Submit your response

[1242] The server automatically returns the generated answer to the user.

[1243] Input: The generated answer text.

[1244] Data processing: Use the LINE API to send the response to the user.

[1245] Output: The answer message sent to the user.

[1246] Step 9: Generate a donation message

[1247] The server periodically generates donation solicitation messages using a generative AI model.

[1248] Input: Prompt text (e.g., "Write a message to collect donations before the next game.").

[1249] Data processing: The generative AI model generates a donation solicitation message based on the prompt text.

[1250] Output: The generated donation message.

[1251] Step 10: Send the message

[1252] The server sends the generated message to all users as a push notification.

[1253] Input: The generated donation message.

[1254] Data processing: Push notifications are sent using the notification API.

[1255] Output: The push notification sent to the user device.

[1256] Step 11: Display the donation page

[1257] When the user clicks on the link to the donation page based on the message they received, the device displays the donation page.

[1258] Input: Link to donation page.

[1259] Data processing: Launch a browser and display the URL of the donation page.

[1260] Output: The donation page displayed.

[1261] Step 12: Make a donation

[1262] The user enters the required information on the donation page and completes the donation.

[1263] Input: Donation information (donation amount, payment method, etc.).

[1264] Data processing: Enter donation information into the form and click the submit button.

[1265] Output: Notification of donation completion.

[1266] In this way, each processing step involves specific operations and data processing, and the entire system is designed to operate efficiently using generative AI models and natural language processing techniques.

[1267] (Application example 1)

[1268] 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."

[1269] The present invention provides a system for automating public relations and donation solicitation activities in fields such as amateur sports organizations and food delivery. In particular, there is a need for centralized management of user question and answering, content automatic generation and posting, and donation solicitation message generation and transmission, all of which must be performed efficiently and with high accuracy. Conventional methods require these tasks to be performed manually, which requires a significant amount of time and effort, creating a need for automation to improve efficiency.

[1270] 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.

[1271] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to users, means for generating and sending a donation solicitation message to users, means for directing users to a donation page, means for automatically generating a recommendation menu based on user preference information, and means for sending the generated content to all users using push notifications. This enables unified and efficient operation of public relations activities, user support, promotional activities, and donation solicitations.

[1272] "Generation data" refers to data that is input into the system and is information that is used to generate content.

[1273] "Content" is a collection of information created based on generated data, and is material provided to users for public relations, promotion, or information purposes.

[1274] A "platform" is a foundation for users and systems to exchange information, including online communication tools and applications.

[1275] A "user" is an entity that uses the system to send and receive information, such as asking questions, viewing publicity content, and making donations.

[1276] A "natural language processing engine" is a technology that analyzes questions and input text from users and understands their meaning.

[1277] A "generative AI model" is a type of artificial intelligence used to automatically generate content based on data, and has the ability to generate responses to specific prompts.

[1278] A "donation solicitation message" is a message sent to users to encourage them to make donations, and includes information related to a specific campaign or event.

[1279] A "donation page" is a web page or screen within an application that allows users to make donations.

[1280] A "recommended menu" is a list of dishes and services that is automatically generated using a generative AI model based on the user's preference information.

[1281] "Push notification" is a feature that sends content or messages directly to a user's device, with the aim of attracting the user's attention.

[1282] A detailed embodiment of the system according to the present invention will be described below. The system automates public relations and donation collection activities, and particularly improves the efficiency of food delivery service operations. The system is composed of a server, a user terminal, and a platform.

[1283] The server implements the following main functions:

[1284] 1. Receiving generated data

[1285] 2. Automated content generation

[1286] 3. Posting Generated Content to the Platform

[1287] 4. Receiving and analyzing user questions

[1288] 5. Generate and reply to appropriate answers

[1289] 6. Creating and sending donation solicitation messages

[1290] 7. Direct users to a donation page

[1291] 8. Automatic Generation of Recommendation Menus Based on User Preferences

[1292] 9. Sending to all users using push notifications

[1293] The user terminal is used to input questions, generate content, and access the donation page. The platform uses online communication tools to mediate the exchange of information between the user and the server.

[1294] Hardware and software used

[1295] The system's server uses a database (MySQL), a generative AI model (OpenAI GPT-3), a natural language processing engine (NLTK), and a push notification service (Firebase Cloud Messaging). User devices are primarily mobile devices such as smartphones and tablets. The platform used is a common online communication tool (e.g., LINE).

[1296] Data processing and calculation

[1297] 1. Data collection and content generation

[1298] The server collects the latest menu and promotion information from the food delivery company's database. Based on this data, it uses a generative AI model to automatically generate content for users. For example, it generates a promotional message such as, "Today's recommended menu items are sushi, tempura, and ramen."

[1299] 2. Responding to user questions

[1300] When a user submits a question (e.g., "What's your recommendation today?"), the server uses a natural language processing engine to analyze the intent of the question. Based on the analysis results, it uses a generative AI model to generate an appropriate answer and sends it back to the user.

[1301] 3. Generate and send donation messages

[1302] The server periodically generates donation solicitation messages and sends them to all users using the push notification service. For example, a message generated in response to the prompt "Please create a new donation campaign message" might include something like "Would you like to donate 50 meals as part of our winter campaign?"

[1303] Specific examples

[1304] Example of generating a recommended menu: Using a generative AI model (OpenAI GPT-3) based on user preference information, a prompt such as "User preference: spicy food" is input, and "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot" is generated.

[1305] Example of response to user question: The server analyzes the question "What is your recommendation today?" and generates and returns the answer "Today, we recommend Margherita pizza and Caprese salad."

[1306] Example of a donation message prompt: "Write a message for our new donation campaign."

[1307] In this way, this system can achieve automation and efficiency in food delivery operations by utilizing generative AI models and natural language processing technology.

[1308] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1309] Step 1:

[1310] The server collects the latest menu and promotion information from food delivery companies' databases.

[1311] Input: Latest menu and promotion information available in the database

[1312] Output: Collected menu and promotion information

[1313] What happens: The server uses a REST API to access the database and retrieve the latest menu and promotion information, which is then used in subsequent processing steps.

[1314] Step 2:

[1315] Based on the collected data, the server automatically generates public relations or promotional content using a generative AI model (OpenAI GPT-3).

[1316] Input: Latest menu and promotion information

[1317] Output: Auto-generated promotional or promotional content

[1318] Specific operation: The server inputs the prompt "Please create a promotional message based on the latest food delivery menu" into the generative AI model, and generates content such as "Today's recommended menu items are sushi, tempura, and ramen."

[1319] Step 3:

[1320] The server posts the generated content to a platform (e.g., an online communication tool).

[1321] Input: Auto-generated PR or promotional content

[1322] Output: Content posted to the platform

[1323] Specific operation: The server sends the generated content to the platform's submission endpoint using the REST API so that it can be displayed to the user.

[1324] Step 4:

[1325] The user uses the device to enter a question within the app and send it to the server.

[1326] Input: A question from the user (e.g., "What's your recommendation today?")

[1327] Output: User question sent to server

[1328] Specific operation: The user enters a question in the message input field of the app and presses the send button, and the question is sent to the server.

[1329] Step 5:

[1330] The server analyzes the received question using a natural language processing engine (NLTK) to understand the intent of the question.

[1331] Input: User question sent to server

[1332] Output: Parsed question intent

[1333] Specific operation: The server uses a natural language processing engine to analyze the text of the received question and extract the intent (e.g., "I would like to know what menu items are recommended").

[1334] Step 6:

[1335] Based on the intent of the analyzed question, the server uses a generative AI model to generate an appropriate answer and returns it to the user.

[1336] Input: Parsed question intent

[1337] Output: Auto-generated answer

[1338] Specific operation: The server inputs the prompt "What's your recommendation today?" into the generative AI model, which generates the answer "Margherita pizza and Caprese salad are recommended today." It then sends this answer back to the user.

[1339] Step 7:

[1340] The server generates a donation solicitation message using a generative AI model and sends it to all users via push notification.

[1341] Input: Generate Donation Message Prompt

[1342] Output: The donation message sent to the user

[1343] Specific operation: The server inputs the prompt "Please create a message for a new donation campaign" into the generative AI model, which generates the message "Would you like to donate 50 meals as part of our winter campaign?". It then uses Firebase Cloud Messaging to send a push notification to all users.

[1344] Step 8:

[1345] A user receives a donation message and clicks on a link to a donation page.

[1346] Input: Donation message received as a push notification

[1347] Output: Display of donation page

[1348] What happens: When a user clicks on the link in the push notification, a browser or in-app browser opens to a donation page where the user can make a donation.

[1349] Step 9:

[1350] The server automatically generates a recommendation menu based on user preference information using a generative AI model.

[1351] Input: User preference information

[1352] Output: Recommendation menu

[1353] Specific operation: The server inputs the prompt "User preference: spicy food" into the generative AI model, and automatically generates candidates such as "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot." The generated recommended menu is then presented to the user.

[1354] 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.

[1355] This invention combines a system for automating public relations and fundraising activities for amateur sports organizations with an emotion engine that recognizes user emotions. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users. Furthermore, the emotion engine enables appropriate responses based on the user's emotional state.

[1356] System Configuration

[1357] This system mainly consists of the following components:

[1358] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing and an emotion engine, and automatic message sending.

[1359] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1360] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[1361] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[1362] Program Overview

[1363] The program processing of this system will be explained in natural language below.

[1364] 1. Data collection and content generation

[1365] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1366] The generated data obtained by the server is saved in a database.

[1367] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1368] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1369] The administrator (user) approves the content and makes corrections as necessary.

[1370] The server automatically posts the approved content to the LINE Official Account's timeline.

[1371] 2. Automated responses to user questions

[1372] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1373] The server receives the question and analyzes it using a natural language processing engine.

[1374] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[1375] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1376] The server inputs the information it obtains and the results of the emotion engine into a generative AI model to generate an appropriate answer to the question.

[1377] The generated response is automatically sent back to the user in a tone and content that reflects the user's emotional state.

[1378] 3. Sending donation messages

[1379] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1380] The server sends the generated message to all users as a push notification from the LINE official account.

[1381] A user receives a push notification and clicks a link in the message.

[1382] The device (such as the user's smartphone) opens the link and displays the donation page.

[1383] The user completes the donation process and enters the amount on the donation page.

[1384] The device will complete the donation process and display a notification that the donation is complete.

[1385] Specific examples

[1386] Example 1: Creating promotional content for match results

[1387] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[1388] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[1389] Example 2: Automatic response to user questions

[1390] A user submits a question: "When is the next game?"

[1391] The server analyzes the question and uses an emotion engine to determine the user's emotional state as "excited."

[1392] The server retrieves information from the database that "The next game is on August 15th," and uses a generation AI to generate a response that reads, "The next game is on August 15th. Please cheer us on!"

[1393] The generated response is automatically sent back to the user.

[1394] In this way, by utilizing generative AI and natural language processing technology as well as an emotion engine, this system is able to automate more appropriate public relations and donation solicitation activities according to the user's emotional state.

[1395] The processing flow will be explained below.

[1396] Data collection and content generation process steps

[1397] Step 1:

[1398] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations.

[1399] Step 2:

[1400] The generated data obtained by the server is saved in a database.

[1401] Step 3:

[1402] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1403] Step 4:

[1404] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1405] Step 5:

[1406] The administrator (user) approves the content and makes corrections as necessary.

[1407] Step 6:

[1408] The server automatically posts the approved content to the LINE Official Account's timeline.

[1409] Processing steps for automatic responses to user questions

[1410] Step 1:

[1411] A user sends a question via the LINE official account.

[1412] Step 2:

[1413] The server receives the question and analyzes it using a natural language processing engine.

[1414] Step 3:

[1415] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[1416] Step 4:

[1417] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1418] Step 5:

[1419] The information acquired by the server is input into a generative AI model to generate an answer appropriate to the user's emotional state.

[1420] Step 6:

[1421] The server returns the generated answer to the user.

[1422] Process steps for sending a donation message

[1423] Step 1:

[1424] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1425] Step 2:

[1426] The server sends the generated message to all users as a push notification from the LINE official account.

[1427] Step 3:

[1428] A user receives a push notification and clicks a link in the message.

[1429] Step 4:

[1430] The device (such as the user's smartphone) opens the link and displays the donation page.

[1431] Step 5:

[1432] The user completes the donation process and enters the amount on the donation page.

[1433] Step 6:

[1434] The device will complete the donation process and display a notification that the donation is complete.

[1435] Example: Automated responses to user questions

[1436] Example 1: If the user's question is "When is the next game?"

[1437] Step 1: A user sends a question to the LINE Official Account asking, "When is the next game?"

[1438] Step 2: The server receives the question and uses its natural language processing engine to parse it as asking "When is the next game?"

[1439] Step 3: When the server parses the question, it uses an emotion engine to detect the emotion of "excitement" from the user's text.

[1440] Step 4: The server retrieves the game schedule data from the database and confirms the information: "The next game is on August 15th."

[1441] Step 5: The information obtained by the server is input into the generative AI model, which generates a response that matches the user's emotions: "The next game is on August 15th. Please cheer us on!"

[1442] Step 6: The server automatically sends the generated answer back to the user.

[1443] In this way, by combining generative AI, natural language processing, and emotion engine technologies, this system can automate appropriate public relations and donation collection activities according to the user's emotional state.

[1444] Example 2

[1445] 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."

[1446] Today, amateur sports organizations spend a lot of time and manpower on effective public relations and fundraising activities. However, there is a lack of systems to automate these activities, making it particularly difficult to respond appropriately to user emotions. Furthermore, there is a lack of mechanisms for quickly and accurately answering user questions. This makes it difficult to increase user satisfaction, and as a result, fundraising is not effective.

[1447] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to a communication network, means for receiving questions from users, means for analyzing the received questions and acquiring information related to the questions from a database, means for automatically generating appropriate answers based on the acquired information, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to donation pages, means for analyzing the emotional state of the users, and means for generating appropriate content and answers based on the analyzed emotional state. This makes it possible to automate public relations activities and donation solicitation activities and respond appropriately according to the user's emotional state, thereby increasing user satisfaction and enabling effective donation solicitation.

[1448] "Means for receiving generated data" refers to a device or software that has the function of obtaining data such as match results, player information, and event schedules from the sports organization's management terminal or other information sources.

[1449] "Means for automatically generating content" refers to a device or software that has the function of automatically generating advertising or public relations content based on received data.

[1450] A "means for posting to a communications network" is any device or software capable of automatically posting generated content to an online platform.

[1451] The "means for receiving questions from users" refers to a device or software that has the function of receiving messages or questions from users via the online platform.

[1452] The "means for analyzing a question" is a device or software that has the function of analyzing a received question using a natural language processing engine and understanding the content of the question.

[1453] The "means for retrieving information from a database" is a device or software that has the function of extracting information related to the analyzed question from a database.

[1454] The "means for automatically generating appropriate answers" refers to a device or software that has the function of automatically generating answers to user questions based on the acquired information and analysis results.

[1455] The "means for returning an answer to a user" is a device or software that has the function of automatically sending the generated answer to the user.

[1456] A "means for generating a solicitation message" is a device or software capable of automatically generating a message suitable for a solicitation campaign.

[1457] The "means for sending to a user" is a device or software that has the function of sending the generated solicitation message to a user.

[1458] A "means for directing users to a donation page" is a device or software that has the function of automatically providing a message containing a link or instructions that directs users to a web page where they can make a donation.

[1459] A "means for analyzing a user's emotional state" is a device or software that has an engine or algorithm that analyzes emotions from a user's input text or voice.

[1460] The "means for generating appropriate content and answers" refers to a device or software that has the function of automatically generating content and answers that correspond to the user's emotions based on the analyzed emotional state.

[1461] This invention relates to a system that automates the public relations and fundraising activities of amateur sports organizations and enables them to respond to user emotions. This system has the functionality to automatically generate content based on generated data and to automatically generate answers to user questions.

[1462] System Configuration

[1463] This system consists of the following components:

[1464] 1. Server: Mainly manages membership, collects data, automatically generates content using generative AI models, answers questions using natural language processing and an emotion engine, and sends automatic messages.

[1465] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1466] 3. Platform: A medium for exchanging information between users and sports organizations via online communication tools (e.g., LINE official account).

[1467] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[1468] Detailed function description

[1469] 1. Data collection and content generation

[1470] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1471] Store the generated data in a relational database (e.g., MySQL).

[1472] The server inputs the saved data into a generative AI model (e.g., OpenAI GPT-3) to automatically generate PR content. For example, the prompt could be, "Generate PR content about yesterday's game results. The game score was 3-2, and the game date was yesterday."

[1473] The generated content is sent to an administrator's terminal, where the administrator checks the content and makes corrections as necessary.

[1474] When the administrator clicks the approval button, the server posts the approved content to the LINE Official Account's timeline.

[1475] 2. Automated responses to user questions

[1476] The server receives the question (e.g., "When is the next game?") sent by the user via the LINE Official Account.

[1477] The server uses a natural language processing engine (e.g., spaCy) to analyze the question and extract key content.

[1478] An emotion engine (e.g., IBM Watson Tone Analyzer) is used to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[1479] The server retrieves relevant information (e.g. "date of next game") from a database using an SQL query.

[1480] Based on the acquired information and the results of sentiment analysis, a prompt is input into the generative AI model to generate a response. A possible prompt might be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[1481] The generated answer is automatically sent back to the user via the LINE Messaging API.

[1482] 3. Sending donation messages

[1483] The server automatically generates a donation message by inputting it into the AI ​​model depending on the start time of the donation campaign. For example, it uses the prompt, "Please create a message for the donation campaign starting next week. The main points are to explain the importance of donations and how to donate."

[1484] The server sends the generated message to all users as a push notification from the LINE official account.

[1485] When a user receives the push notification and clicks on the link in the message, the user's device will display a donation page.

[1486] The user completes the donation procedure on the donation page, and the device displays a notification that the donation has been completed.

[1487] This system automates public relations and fundraising activities and responds appropriately to the user's emotional state, which not only increases user satisfaction but also enables effective fundraising.

[1488] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1489] Step 1:

[1490] The server obtains generated data (e.g., match results, player information, event schedules) from the sports organization's management terminal. The server periodically issues API requests, receives data from the management terminal, and imports it in JSON format. The obtained generated data is stored in a relational database (e.g., MySQL).

[1491] Input: Data generated from the management terminal of a sports organization

[1492] Data processing: Convert JSON data into database format

[1493] Output: Generated data stored in a database

[1494] Step 2:

[1495] The server inputs PR content into a generative AI model (e.g., OpenAI GPT-3) based on the saved generated data, and generates automatic content. For example, the prompt text could be, "Generate a PR text about the results of yesterday's game. The game score was 3-2, and the game date was yesterday." The generated content is temporarily saved in JSON format.

[1496] Input: Generated data stored in a database

[1497] Data processing: Input to generative AI models

[1498] Output: Generated promotional content

[1499] Step 3:

[1500] The generated PR content is sent to the administrator's terminal. The administrator (user) uses the management interface to check the generated content and make corrections as necessary. Once corrections are complete, the administrator clicks the approval button.

[1501] Input: Generated PR content, admin modifications

[1502] Data processing: Correction and approval through the management interface

[1503] Output: Approved content

[1504] Step 4:

[1505] The server posts the approved content to the LINE Official Account's timeline automatically using the LINE API.

[1506] Input: Approved Content

[1507] Data processing: Posting using LINE API

[1508] Output: Content posted to the LINE Official Account's timeline

[1509] Step 5:

[1510] A user sends a question (e.g., "When is the next game?") via the LINE Official Account. The server receives the message using the LINE Messaging API and obtains the text data.

[1511] Input: Question message from user

[1512] Data processing: Receiving text data

[1513] Output: User question data

[1514] Step 6:

[1515] The server uses a natural language processing engine (e.g., spaCy) to analyze the question, and based on the extracted intent and keywords, retrieves relevant information (e.g., "next game schedule") from a database using an SQL query.

[1516] Input: User question data

[1517] Data processing: Question analysis using natural language processing, database search using SQL queries

[1518] Output: Related information (e.g., next game date)

[1519] Step 7:

[1520] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state. Based on the analyzed emotional state (e.g., joy, anger, sadness, etc.), a generative AI model is used to generate a response with an appropriate tone and content. An example of a prompt could be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[1521] Input: relevant information, user's emotional state

[1522] Data processing: Generating answers using a generative AI model

[1523] Output: Generated answer

[1524] Step 8:

[1525] The generated response is sent back to the user via the LINE Messaging API. The server sends the generated response as a message.

[1526] Input: Generated answer

[1527] Data processing: Sending messages using the LINE API

[1528] Output: The answer message sent to the user

[1529] Step 9:

[1530] The server inputs the donation solicitation message into the AI ​​model according to the start date of the donation solicitation campaign and automatically generates the message. For example, it uses the prompt "Please create a message for the donation campaign starting next week. The main points should be to explain the importance of donations and how to donate." The generated message is sent to all users as a push notification to the LINE official account.

[1531] Input: Donation message template

[1532] Data processing: Message generation using generative AI models

[1533] Output: Generated donation message

[1534] Step 10:

[1535] When a user receives a push notification and clicks the link in the message, the user's device will display a donation page. The user enters the donation amount and provides the necessary information on the donation page. The device will then complete the donation process and display a notification that the donation has been completed.

[1536] Input: Push notification link, user input information

[1537] Data processing: Displaying donation pages and carrying out donation procedures

[1538] Output: Notification of donation completion

[1539] In this way, the system can effectively promote and solicit donations throughout the entire process.

[1540] (Application example 2)

[1541] 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."

[1542] Amateur sports organizations are required to streamline their public relations and donation-raising activities while also providing appropriate responses based on user emotions. Conventional systems lack sufficient emotional responses, which can lead to a poor user experience. In particular, accurately capturing the enthusiasm and emotions of sports fans and providing appropriate public relations and donation-raising messages is crucial for expanding support for the organization.

[1543] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1544] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means including an emotion analysis engine for analyzing the user's emotions and adjusting the content and answer sentences based on the analyzed emotions. This enables public relations activities and donation solicitations to be conducted in accordance with the user's emotions, thereby effectively spreading support for sports organizations.

[1545] "Generated Data" refers to information generated by sports organizations, such as match results, player information, and event schedules.

[1546] "Content" refers to promotional text and images automatically generated by a generative AI model based on generated data.

[1547] "Platform" refers to an intermediary system, such as an online communication tool or social networking site, that allows users to exchange information with sports organizations.

[1548] A "question" refers to an inquiry that a user inputs to the system.

[1549] "Emotion analysis engine" refers to an engine for analyzing a user's emotional state from input text or voice.

[1550] A "donation solicitation message" refers to a message created by a sports organization to call on users for donations.

[1551] A "generative AI model" refers to an artificial intelligence model that performs natural language processing, image generation, etc. based on input data.

[1552] A "natural language processing engine" refers to an engine that analyzes text data entered by a user, understands its meaning, and generates an appropriate response.

[1553] MODE FOR CARRYING OUT THE INVENTION

[1554] The present invention is a system for automating public relations and donation collection activities for amateur sports organizations, and in particular, a system that uses an emotion analysis engine to realize appropriate responses according to the user's emotions. To achieve this purpose, the system includes the following components and processing steps:

[1555] 1. System Configuration

[1556] This system mainly consists of a server, a user terminal, and a platform.

[1557] server:

[1558] The server is the main component that receives generated data, automatically generates content, analyzes questions and generates answers, generates and sends donation messages, and analyzes user sentiment. Specifically, the following hardware and software are used:

[1559] Hardware: High-performance server unit

[1560] Software: Generative AI models, natural language processing engines, sentiment analysis engines (e.g., Hugging Face Transformers)

[1561] User device:

[1562] A user terminal is the device that a user uses to enter questions, receive answers, and access the donation page, typically a smartphone or personal computer.

[1563] Platform:

[1564] The platform is a system that includes online communication tools and social networking sites, and serves as a medium for the smooth exchange of information between users and sports organizations.

[1565] 2. Program Processing

[1566] The server performs the following process:

[1567] 1. Receiving generated data and generating content

[1568] The server periodically acquires generated data such as match results, player information, and event schedules from the sports organization's management terminal, and then uses this data to apply a generative AI model to automatically generate promotional content.

[1569] 2. Question Analysis and Answer Generation

[1570] When a user submits a question through the platform, the server receives the question and analyzes it using a natural language processing engine. Based on the analysis results, the sentiment analysis engine identifies the user's emotional state, retrieves relevant information from the database, and generates an appropriate answer that is tailored to the user's emotions.

[1571] 3. Generate and send a donation message

[1572] The server generates a donation message based on the start time of the donation campaign and sends it as a push notification to all users, who can then access the donation page.

[1573] 3. Examples and prompts

[1574] Example 1: Creating promotional content for match results

[1575] The server receives the match result data and uses a generative AI model to generate a sentence such as, "Yesterday's match was a close one, but we won 3-2." The generated sentence is then reviewed and approved by an administrator and automatically posted to the platform's timeline.

[1576] Example 2: Automatic response to user questions

[1577] When a user sends a question such as "When is the next game?", the server analyzes the question and uses an emotion analysis engine to determine the user's emotional state as "excited." The server then retrieves the information "The next game is on August 15th" from the database and uses a generative AI model to generate an automatic response saying "The next game is on August 15th. Please cheer us on!"

[1578] Prompt Sentence Examples

[1579] "Generate a news article from the following data: [generated data]"

[1580] "Generate a friendly, positive message about the next game: The next game is on August 15."

[1581] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1582] Step 1:

[1583] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations. This input data is sent to the server via API and stored in a database.

[1584] Step 2:

[1585] The server inputs the generated data in the database into a generative AI model to automatically generate PR content. Specifically, it uses the prompt "Generate a news article from the following data: [generated data]" and outputs content in the form of a news article. This output is temporarily saved and awaits confirmation by an administrator.

[1586] Step 3:

[1587] The server sends the generated content to an administrator terminal, who reviews the content and makes any necessary corrections. The server then receives the approved content and automatically posts it to the platform's timeline. The output of this step is the published news content.

[1588] Step 4:

[1589] When a user submits a question through the platform, the server receives the question. The question is sent to the server in text format and input into a natural language processing engine for analysis. The engine analyzes the meaning of the question and identifies the intent of the question as its output.

[1590] Step 5:

[1591] The server identifies the user's emotional state based on the analysis results using a sentiment analysis engine, which extracts emotions from the user's text and outputs emotional states such as "excitement," "joy," and "anger."

[1592] Step 6:

[1593] The server retrieves relevant information from the database based on the results of the sentiment analysis. For example, if the question is "When is the next game?", it retrieves the date and time of the next game. This output information is used in the next step.

[1594] Step 7:

[1595] The server inputs the acquired information and the results of the sentiment analysis engine into a generative AI model to generate an appropriate response. It uses a prompt such as "Generate a friendly, positive message about the next game: The next game is on August 15." and outputs an appropriate response for the user.

[1596] Step 8:

[1597] The generated answer is automatically sent back to the user. A reply message is sent to the user's device via the platform's chat function. The output of this step is the appropriate answer delivered to the user.

[1598] Step 9:

[1599] The server generates a donation message based on the start date of the donation campaign. Using a generative AI model, it outputs a message calling for donations: "We need your support! Please donate to our team to keep us going."

[1600] Step 10:

[1601] The server sends the generated donation message to all users as a platform push notification. When a user receives the notification and clicks on the link in the message, the user's device is redirected to the donation page. The output of this step is a user directed to the donation page.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] [Fourth embodiment]

[1606] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1607] 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.

[1608] 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).

[1609] 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.

[1610] 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.

[1611] 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).

[1612] 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.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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."

[1619] This invention is a system for automating the public relations and fundraising activities of amateur sports organizations. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[1620] System Configuration

[1621] This system mainly consists of the following components:

[1622] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing, and automatic message sending.

[1623] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1624] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[1625] Program Overview

[1626] The program processing of this system will be explained in natural language below.

[1627] 1. Data collection and content generation

[1628] The server periodically obtains data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1629] Based on the data acquired by the server, a generative AI model is used to automatically generate promotional content (news articles, event information, etc.).

[1630] The generated content is first reviewed by an administrator (a sports organization staff member) and then posted to the LINE official account timeline after approval.

[1631] 2. Automated responses to user questions

[1632] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1633] The server analyzes the received question using a natural language processing engine to understand the intent of the question.

[1634] Based on the analysis results, the server retrieves relevant information from the database and uses generation AI to generate an appropriate response.

[1635] The generated answer is automatically sent back to the user.

[1636] 3. Sending donation messages

[1637] The server periodically generates messages to collect donations (e.g., seasonal campaigns, collections before specific events, etc.).

[1638] The generated message is sent to all users as a push notification.

[1639] When the user clicks on the link to the donation page based on the message received, the terminal displays the donation page and the user can make a donation.

[1640] Specific examples

[1641] Example 1: Creating promotional content for match results

[1642] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[1643] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[1644] Example 2: Automatic response to user questions

[1645] A user submits a question: "When is the next game?"

[1646] The server analyzes the question, retrieves information from the database such as "The next game is on August 15th," and generates a sentence using a generative AI.

[1647] The generated response is automatically sent back to the user.

[1648] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[1649] The processing flow will be explained below.

[1650] Data collection and content generation process steps

[1651] Step 1:

[1652] The server acquires generated data such as match results, player information, and event schedules from the management terminal of the sports organization.

[1653] Step 2:

[1654] The generated data obtained by the server is saved in a database.

[1655] Step 3:

[1656] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1657] Step 4:

[1658] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1659] Step 5:

[1660] The administrator (user) approves the content and makes corrections as necessary.

[1661] Step 6:

[1662] The server automatically posts the approved content to the LINE Official Account's timeline.

[1663] Processing steps for automatic responses to user questions

[1664] Step 1:

[1665] A user sends a question via the LINE official account.

[1666] Step 2:

[1667] The server receives the question and analyzes it using a natural language processing engine.

[1668] Step 3:

[1669] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1670] Step 4:

[1671] The information acquired by the server is input into a generative AI model to generate an appropriate answer to the question.

[1672] Step 5:

[1673] The server returns the generated answer to the user.

[1674] Process steps for sending a donation message

[1675] Step 1:

[1676] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1677] Step 2:

[1678] The server sends the generated message to all users as a push notification from the LINE official account.

[1679] Step 3:

[1680] A user receives a push notification and clicks a link in the message.

[1681] Step 4:

[1682] The device (such as the user's smartphone) opens the link and displays the donation page.

[1683] Step 5:

[1684] The user completes the donation process and enters the amount on the donation page.

[1685] Step 6:

[1686] The device will complete the donation process and display a notification that the donation is complete.

[1687] These processing steps allow amateur sports organizations to efficiently and effectively automate their public relations and fundraising activities.

[1688] Example 1

[1689] 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."

[1690] Improving the efficiency of public relations and fundraising activities for amateur sports organizations is an important issue for many organizations. However, conventional methods require manual content creation, responding to user inquiries, and creating donation solicitation messages. These tasks are burdensome due to labor shortages and time constraints, making efficient management difficult. In addition, maintaining the quality and consistency of content is difficult, limiting the effectiveness of public relations and fundraising activities.

[1691] 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.

[1692] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to an external service, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means for an administrator to approve the content generation process. This automates public relations and donation solicitation activities, reducing the workload and improving quality.

[1693] "Generated data" refers to data received by the system, such as sporting events, player information, and event schedules.

[1694] "Content" refers to public relations information such as news articles and event information created using a generative AI model based on generated data.

[1695] "External Services" refers to online communication tools and social media platforms that allow users to interact with the website.

[1696] "User" refers to any person or entity that submits a question, receives content, or makes a donation through the System.

[1697] A "generative AI model" is a model that uses artificial intelligence technology to generate sentences in natural language from input data.

[1698] A "natural language processing engine" refers to technology that analyzes questions from users and understands their intent and content.

[1699] A "database" is a collection of data that a system uses to store information and search and retrieve it as needed.

[1700] A "donation solicitation message" is a message generated to encourage users to make donations.

[1701] "Push notifications" are notifications sent directly to users' devices, providing them with immediate access to important information and messages.

[1702] A "prompt" is a command or instruction given to a generative AI model to make it generate specific sentences based on input data.

[1703] The present invention is a system for automating public relations and fundraising activities for amateur sports organizations. This system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users.

[1704] System Configuration

[1705] This system mainly consists of the following components:

[1706] 1. Server: Collects data, automatically generates content using generative AI models, answers questions using natural language processing, and sends automatic messages.

[1707] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1708] 3. External services: Online communication tools that act as a medium for exchanging information between users and sports organizations.

[1709] Specific hardware and software

[1710] Hardware:

[1711] Database Server: Used to store data.

[1712] High-performance GPU server: Used to run generative AI models.

[1713] software:

[1714] Data Collection API: Used to retrieve data from sports organization management tools.

[1715] Generative AI models (e.g., OpenAI's GPT-4): Used for content generation.

[1716] Natural language processing engine (e.g. BERT): Used to analyze user questions.

[1717] External service APIs (e.g., LINE API): Used to post generated content and communicate with users.

[1718] Specific examples of data collection and content generation

[1719] The server periodically retrieves data such as match results, player information, and event schedules from the sports organization's management terminal. This data is received in JSON format using HTTP requests. The server then analyzes the retrieved data and inputs the following prompts to the generative AI model:

[1720] "Generate news articles for your fans using yesterday's game results."

[1721] The generative AI model generates content based on this prompt as follows:

[1722] "Yesterday's game was a close one, but we won 3-2."

[1723] The generated content is sent to the sports organization's administrator, who reviews and approves it. Once approved, the content is automatically posted to the LINE Official Account's timeline using the LINE API.

[1724] Examples of automated responses to user questions

[1725] The user sends a question via the official LINE account: "When is the next game?" The server analyzes the received question using a natural language processing engine and understands the intent of the question. The server retrieves the information "The next game is on August 15th" from the database and inputs the following prompt sentence into the generative AI model:

[1726] "Generate an answer using the date of the next match."

[1727] Based on this prompt, the generative AI model generates an answer such as "The next game is on August 15th," and automatically replies to the user using the LINE API.

[1728] Examples of donation solicitation messages

[1729] The server periodically generates donation messages using a generative AI model. For example, before the next game, it inputs the following prompt:

[1730] "Create a message before your next game asking for donations."

[1731] Based on this prompt, the generative AI model generates a message saying, "Please support the next game. Donate here!" The generated message is sent to all users as a push notification using the notification API. Users receive the notification and click the link to be taken to a donation page where they can enter the required information to complete their donation.

[1732] In this way, this system makes full use of generative AI and natural language processing technology to efficiently automate public relations and donation solicitation activities for sports organizations.

[1733] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1734] Step 1: Data collection

[1735] The server retrieves data such as match results, player information, and event schedules from the sports organization's management terminal, using an HTTP request to retrieve data in JSON format.

[1736] Input: Data obtained from the management terminal API (JSON format).

[1737] Data processing: Analyze the received data and extract the necessary information (e.g., match results, player information).

[1738] Output: Extracted sports team data (match results, player information, event schedules).

[1739] Step 2: Content generation

[1740] The server inputs a prompt sentence into a generative AI model (e.g., OpenAI's GPT-4) based on the data extracted in step 1, and generates promotional content.

[1741] Input: Extracted sports organization data, prompt (e.g., "Generate a news article for fans using yesterday's game results.").

[1742] Data processing: Generative AI models generate news articles and event information in natural language based on prompts and data.

[1743] Output: Generated promotional content (news articles, event information).

[1744] Step 3: Administrator Verification

[1745] The server sends the generated content to the sports organization's administrator for review and approval.

[1746] Input: Generated promotional content (news articles, event information).

[1747] Data processing: The administrator will be notified by email or via the management screen to request that the content be reviewed.

[1748] Output: Approval or corrective feedback from management.

[1749] Step 4: Post your content

[1750] After administrator approval, the server automatically posts the content to the LINE Official Account's timeline.

[1751] Input: Content approved by administrator.

[1752] Data processing: Uses the LINE API to post approved content to the timeline.

[1753] Output: Content posted to an external service.

[1754] Step 5: Receiving user questions

[1755] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1756] Input: A question message from the user.

[1757] Data processing: Receive questions using LINE's chat function.

[1758] Output: The query message received.

[1759] Step 6: Question Analysis

[1760] The server analyzes the received question using a natural language processing engine (e.g., BERT) to understand the intent of the question.

[1761] Input: The received question message.

[1762] Data processing: Perform text analysis to analyze keywords and context.

[1763] Output: Analysis results (question intent, keywords).

[1764] Step 7: Answer Generation

[1765] Based on the analysis results, the server retrieves relevant information from the database and generates an appropriate response using a generative AI model (e.g., GPT-4).

[1766] Input: Analysis results, relevant information from the database, and a prompt (e.g., "Generate an answer using the date of the next game.").

[1767] Data processing: Execute a database query to obtain the necessary information, and input a prompt into the generative AI model to generate an answer.

[1768] Output: The generated answer.

[1769] Step 8: Submit your response

[1770] The server automatically returns the generated answer to the user.

[1771] Input: The generated answer text.

[1772] Data processing: Use the LINE API to send the response to the user.

[1773] Output: The answer message sent to the user.

[1774] Step 9: Generate a donation message

[1775] The server periodically generates donation solicitation messages using a generative AI model.

[1776] Input: Prompt text (e.g., "Write a message to collect donations before the next game.").

[1777] Data processing: The generative AI model generates a donation solicitation message based on the prompt text.

[1778] Output: The generated donation message.

[1779] Step 10: Send the message

[1780] The server sends the generated message to all users as a push notification.

[1781] Input: The generated donation message.

[1782] Data processing: Push notifications are sent using the notification API.

[1783] Output: The push notification sent to the user device.

[1784] Step 11: Display the donation page

[1785] When the user clicks on the link to the donation page based on the message they received, the device displays the donation page.

[1786] Input: Link to donation page.

[1787] Data processing: Launch a browser and display the URL of the donation page.

[1788] Output: The donation page displayed.

[1789] Step 12: Make a donation

[1790] The user enters the required information on the donation page and completes the donation.

[1791] Input: Donation information (donation amount, payment method, etc.).

[1792] Data processing: Enter donation information into the form and click the submit button.

[1793] Output: Notification of donation completion.

[1794] In this way, each processing step involves specific operations and data processing, and the entire system is designed to operate efficiently using generative AI models and natural language processing techniques.

[1795] (Application example 1)

[1796] 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."

[1797] The present invention provides a system for automating public relations and donation solicitation activities in fields such as amateur sports organizations and food delivery. In particular, there is a need for centralized management of user question and answering, content automatic generation and posting, and donation solicitation message generation and transmission, all of which must be performed efficiently and with high accuracy. Conventional methods require these tasks to be performed manually, which requires a significant amount of time and effort, creating a need for automation to improve efficiency.

[1798] 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.

[1799] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to users, means for generating and sending a donation solicitation message to users, means for directing users to a donation page, means for automatically generating a recommendation menu based on user preference information, and means for sending the generated content to all users using push notifications. This enables unified and efficient operation of public relations activities, user support, promotional activities, and donation solicitations.

[1800] "Generation data" refers to data that is input into the system and is information that is used to generate content.

[1801] "Content" is a collection of information created based on generated data, and is material provided to users for public relations, promotion, or information purposes.

[1802] A "platform" is a foundation for users and systems to exchange information, including online communication tools and applications.

[1803] A "user" is an entity that uses the system to send and receive information, such as asking questions, viewing publicity content, and making donations.

[1804] A "natural language processing engine" is a technology that analyzes questions and input text from users and understands their meaning.

[1805] A "generative AI model" is a type of artificial intelligence used to automatically generate content based on data, and has the ability to generate responses to specific prompts.

[1806] A "donation solicitation message" is a message sent to users to encourage them to make donations, and includes information related to a specific campaign or event.

[1807] A "donation page" is a web page or screen within an application that allows users to make donations.

[1808] A "recommended menu" is a list of dishes and services that is automatically generated using a generative AI model based on the user's preference information.

[1809] "Push notification" is a feature that sends content or messages directly to a user's device, with the aim of attracting the user's attention.

[1810] A detailed embodiment of the system according to the present invention will be described below. The system automates public relations and donation collection activities, and particularly improves the efficiency of food delivery service operations. The system is composed of a server, a user terminal, and a platform.

[1811] The server implements the following main functions:

[1812] 1. Receiving generated data

[1813] 2. Automated content generation

[1814] 3. Posting Generated Content to the Platform

[1815] 4. Receiving and analyzing user questions

[1816] 5. Generate and reply to appropriate answers

[1817] 6. Creating and sending donation solicitation messages

[1818] 7. Direct users to a donation page

[1819] 8. Automatic Generation of Recommendation Menus Based on User Preferences

[1820] 9. Sending to all users using push notifications

[1821] The user terminal is used to input questions, generate content, and access the donation page. The platform uses online communication tools to mediate the exchange of information between the user and the server.

[1822] Hardware and software used

[1823] The system's server uses a database (MySQL), a generative AI model (OpenAI GPT-3), a natural language processing engine (NLTK), and a push notification service (Firebase Cloud Messaging). User devices are primarily mobile devices such as smartphones and tablets. The platform used is a common online communication tool (e.g., LINE).

[1824] Data processing and calculation

[1825] 1. Data collection and content generation

[1826] The server collects the latest menu and promotion information from the food delivery company's database. Based on this data, it uses a generative AI model to automatically generate content for users. For example, it generates a promotional message such as, "Today's recommended menu items are sushi, tempura, and ramen."

[1827] 2. Responding to user questions

[1828] When a user submits a question (e.g., "What's your recommendation today?"), the server uses a natural language processing engine to analyze the intent of the question. Based on the analysis results, it uses a generative AI model to generate an appropriate answer and sends it back to the user.

[1829] 3. Generate and send donation messages

[1830] The server periodically generates donation solicitation messages and sends them to all users using the push notification service. For example, a message generated in response to the prompt "Please create a new donation campaign message" might include something like "Would you like to donate 50 meals as part of our winter campaign?"

[1831] Specific examples

[1832] Example of generating a recommended menu: Using a generative AI model (OpenAI GPT-3) based on user preference information, a prompt such as "User preference: spicy food" is input, and "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot" is generated.

[1833] Example of response to user question: The server analyzes the question "What is your recommendation today?" and generates and returns the answer "Today, we recommend Margherita pizza and Caprese salad."

[1834] Example of a donation message prompt: "Write a message for our new donation campaign."

[1835] In this way, this system can achieve automation and efficiency in food delivery operations by utilizing generative AI models and natural language processing technology.

[1836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1837] Step 1:

[1838] The server collects the latest menu and promotion information from food delivery companies' databases.

[1839] Input: Latest menu and promotion information available in the database

[1840] Output: Collected menu and promotion information

[1841] What happens: The server uses a REST API to access the database and retrieve the latest menu and promotion information, which is then used in subsequent processing steps.

[1842] Step 2:

[1843] Based on the collected data, the server automatically generates public relations or promotional content using a generative AI model (OpenAI GPT-3).

[1844] Input: Latest menu and promotion information

[1845] Output: Auto-generated promotional or promotional content

[1846] Specific operation: The server inputs the prompt "Please create a promotional message based on the latest food delivery menu" into the generative AI model, and generates content such as "Today's recommended menu items are sushi, tempura, and ramen."

[1847] Step 3:

[1848] The server posts the generated content to a platform (e.g., an online communication tool).

[1849] Input: Auto-generated PR or promotional content

[1850] Output: Content posted to the platform

[1851] Specific operation: The server sends the generated content to the platform's submission endpoint using the REST API so that it can be displayed to the user.

[1852] Step 4:

[1853] The user uses the device to enter a question within the app and send it to the server.

[1854] Input: A question from the user (e.g., "What's your recommendation today?")

[1855] Output: User question sent to server

[1856] Specific operation: The user enters a question in the message input field of the app and presses the send button, and the question is sent to the server.

[1857] Step 5:

[1858] The server analyzes the received question using a natural language processing engine (NLTK) to understand the intent of the question.

[1859] Input: User question sent to server

[1860] Output: Parsed question intent

[1861] Specific operation: The server uses a natural language processing engine to analyze the text of the received question and extract the intent (e.g., "I would like to know what menu items are recommended").

[1862] Step 6:

[1863] Based on the intent of the analyzed question, the server uses a generative AI model to generate an appropriate answer and returns it to the user.

[1864] Input: Parsed question intent

[1865] Output: Auto-generated answer

[1866] Specific operation: The server inputs the prompt "What's your recommendation today?" into the generative AI model, which generates the answer "Margherita pizza and Caprese salad are recommended today." It then sends this answer back to the user.

[1867] Step 7:

[1868] The server generates a donation solicitation message using a generative AI model and sends it to all users via push notification.

[1869] Input: Generate Donation Message Prompt

[1870] Output: The donation message sent to the user

[1871] Specific operation: The server inputs the prompt "Please create a message for a new donation campaign" into the generative AI model, which generates the message "Would you like to donate 50 meals as part of our winter campaign?". It then uses Firebase Cloud Messaging to send a push notification to all users.

[1872] Step 8:

[1873] A user receives a donation message and clicks on a link to a donation page.

[1874] Input: Donation message received as a push notification

[1875] Output: Display of donation page

[1876] What happens: When a user clicks on the link in the push notification, a browser or in-app browser opens to a donation page where the user can make a donation.

[1877] Step 9:

[1878] The server automatically generates a recommendation menu based on user preference information using a generative AI model.

[1879] Input: User preference information

[1880] Output: Recommendation menu

[1881] Specific operation: The server inputs the prompt "User preference: spicy food" into the generative AI model, and automatically generates candidates such as "Recommended menu: tandoori chicken, mapo tofu, kimchi hotpot." The generated recommended menu is then presented to the user.

[1882] 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.

[1883] This invention combines a system for automating public relations and fundraising activities for amateur sports organizations with an emotion engine that recognizes user emotions. The system includes functions for receiving generated data and automatically generating and posting content based on that data, receiving questions from users, analyzing them, and generating answers, as well as generating and sending messages for fundraising to users. Furthermore, the emotion engine enables appropriate responses based on the user's emotional state.

[1884] System Configuration

[1885] This system mainly consists of the following components:

[1886] 1. Server: Membership management, data collection, automatic content generation using generative AI models, question answering using natural language processing and an emotion engine, and automatic message sending.

[1887] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1888] 3. Platform: An online communication tool (e.g., LINE official account) that serves as a medium for exchanging information between users and sports organizations.

[1889] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[1890] Program Overview

[1891] The program processing of this system will be explained in natural language below.

[1892] 1. Data collection and content generation

[1893] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1894] The generated data obtained by the server is saved in a database.

[1895] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1896] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1897] The administrator (user) approves the content and makes corrections as necessary.

[1898] The server automatically posts the approved content to the LINE Official Account's timeline.

[1899] 2. Automated responses to user questions

[1900] A user sends a question (e.g., "When is the next game?") through the LINE Official Account.

[1901] The server receives the question and analyzes it using a natural language processing engine.

[1902] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[1903] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1904] The server inputs the information it obtains and the results of the emotion engine into a generative AI model to generate an appropriate answer to the question.

[1905] The generated response is automatically sent back to the user in a tone and content that reflects the user's emotional state.

[1906] 3. Sending donation messages

[1907] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1908] The server sends the generated message to all users as a push notification from the LINE official account.

[1909] A user receives a push notification and clicks a link in the message.

[1910] The device (such as the user's smartphone) opens the link and displays the donation page.

[1911] The user completes the donation process and enters the amount on the donation page.

[1912] The device will complete the donation process and display a notification that the donation is complete.

[1913] Specific examples

[1914] Example 1: Creating promotional content for match results

[1915] The server imports the match result data and uses a generation AI to generate the sentence, "Yesterday's match was a close one, but we won 3-2."

[1916] The generated text will be reviewed and approved by an administrator and automatically posted to the LINE Official Account's timeline.

[1917] Example 2: Automatic response to user questions

[1918] A user submits a question: "When is the next game?"

[1919] The server analyzes the question and uses an emotion engine to determine the user's emotional state as "excited."

[1920] The server retrieves information from the database that "The next game is on August 15th," and uses a generation AI to generate a response that reads, "The next game is on August 15th. Please cheer us on!"

[1921] The generated response is automatically sent back to the user.

[1922] In this way, by utilizing generative AI and natural language processing technology as well as an emotion engine, this system is able to automate more appropriate public relations and donation solicitation activities according to the user's emotional state.

[1923] The processing flow will be explained below.

[1924] Data collection and content generation process steps

[1925] Step 1:

[1926] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations.

[1927] Step 2:

[1928] The generated data obtained by the server is saved in a database.

[1929] Step 3:

[1930] The server inputs the generated data in the database into a generative AI model to automatically generate promotional content.

[1931] Step 4:

[1932] The generated content is temporarily sent to an administrator terminal, where the administrator (user) checks the content.

[1933] Step 5:

[1934] The administrator (user) approves the content and makes corrections as necessary.

[1935] Step 6:

[1936] The server automatically posts the approved content to the LINE Official Account's timeline.

[1937] Processing steps for automatic responses to user questions

[1938] Step 1:

[1939] A user sends a question via the LINE official account.

[1940] Step 2:

[1941] The server receives the question and analyzes it using a natural language processing engine.

[1942] Step 3:

[1943] Based on the analysis results, the server uses an emotion engine to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[1944] Step 4:

[1945] Based on the analysis results, the server retrieves relevant information (e.g., "next game schedule") from the database.

[1946] Step 5:

[1947] The information acquired by the server is input into a generative AI model to generate an answer appropriate to the user's emotional state.

[1948] Step 6:

[1949] The server returns the generated answer to the user.

[1950] Process steps for sending a donation message

[1951] Step 1:

[1952] The server generates a donation solicitation message according to the start time of the donation solicitation campaign.

[1953] Step 2:

[1954] The server sends the generated message to all users as a push notification from the LINE official account.

[1955] Step 3:

[1956] A user receives a push notification and clicks a link in the message.

[1957] Step 4:

[1958] The device (such as the user's smartphone) opens the link and displays the donation page.

[1959] Step 5:

[1960] The user completes the donation process and enters the amount on the donation page.

[1961] Step 6:

[1962] The device will complete the donation process and display a notification that the donation is complete.

[1963] Example: Automated responses to user questions

[1964] Example 1: If the user's question is "When is the next game?"

[1965] Step 1: A user sends a question to the LINE Official Account asking, "When is the next game?"

[1966] Step 2: The server receives the question and uses its natural language processing engine to parse it as asking "When is the next game?"

[1967] Step 3: When the server parses the question, it uses an emotion engine to detect the emotion of "excitement" from the user's text.

[1968] Step 4: The server retrieves the game schedule data from the database and confirms the information: "The next game is on August 15th."

[1969] Step 5: The information obtained by the server is input into the generative AI model, which generates a response that matches the user's emotions: "The next game is on August 15th. Please cheer us on!"

[1970] Step 6: The server automatically sends the generated answer back to the user.

[1971] In this way, by combining generative AI, natural language processing, and emotion engine technologies, this system can automate appropriate public relations and donation collection activities according to the user's emotional state.

[1972] Example 2

[1973] 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."

[1974] Today, amateur sports organizations spend a lot of time and manpower on effective public relations and fundraising activities. However, there is a lack of systems to automate these activities, making it particularly difficult to respond appropriately to user emotions. Furthermore, there is a lack of mechanisms for quickly and accurately answering user questions. This makes it difficult to increase user satisfaction, and as a result, fundraising is not effective.

[1975] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to a communication network, means for receiving questions from users, means for analyzing the received questions and acquiring information related to the questions from a database, means for automatically generating appropriate answers based on the acquired information, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to donation pages, means for analyzing the emotional state of the users, and means for generating appropriate content and answers based on the analyzed emotional state. This makes it possible to automate public relations activities and donation solicitation activities and respond appropriately according to the user's emotional state, thereby increasing user satisfaction and enabling effective donation solicitation.

[1976] "Means for receiving generated data" refers to a device or software that has the function of obtaining data such as match results, player information, and event schedules from the sports organization's management terminal or other information sources.

[1977] "Means for automatically generating content" refers to a device or software that has the function of automatically generating advertising or public relations content based on received data.

[1978] A "means for posting to a communications network" is any device or software capable of automatically posting generated content to an online platform.

[1979] The "means for receiving questions from users" refers to a device or software that has the function of receiving messages or questions from users via the online platform.

[1980] The "means for analyzing a question" is a device or software that has the function of analyzing a received question using a natural language processing engine and understanding the content of the question.

[1981] The "means for retrieving information from a database" is a device or software that has the function of extracting information related to the analyzed question from a database.

[1982] The "means for automatically generating appropriate answers" refers to a device or software that has the function of automatically generating answers to user questions based on the acquired information and analysis results.

[1983] The "means for returning an answer to a user" is a device or software that has the function of automatically sending the generated answer to the user.

[1984] A "means for generating a solicitation message" is a device or software capable of automatically generating a message suitable for a solicitation campaign.

[1985] The "means for sending to a user" is a device or software that has the function of sending the generated solicitation message to a user.

[1986] A "means for directing users to a donation page" is a device or software that has the function of automatically providing a message containing a link or instructions that directs users to a web page where they can make a donation.

[1987] A "means for analyzing a user's emotional state" is a device or software that has an engine or algorithm that analyzes emotions from a user's input text or voice.

[1988] The "means for generating appropriate content and answers" refers to a device or software that has the function of automatically generating content and answers that correspond to the user's emotions based on the analyzed emotional state.

[1989] This invention relates to a system that automates the public relations and fundraising activities of amateur sports organizations and enables them to respond to user emotions. This system has the functionality to automatically generate content based on generated data and to automatically generate answers to user questions.

[1990] System Configuration

[1991] This system consists of the following components:

[1992] 1. Server: Mainly manages membership, collects data, automatically generates content using generative AI models, answers questions using natural language processing and an emotion engine, and sends automatic messages.

[1993] 2. User terminal: Used by users to enter questions, receive answers, and access the donation page.

[1994] 3. Platform: A medium for exchanging information between users and sports organizations via online communication tools (e.g., LINE official account).

[1995] 4. Emotion Engine: An engine that analyzes the user's emotional state from their input text or voice.

[1996] Detailed function description

[1997] 1. Data collection and content generation

[1998] The server periodically obtains generated data such as the latest match results, player information, and event schedules from the sports organization's management terminal.

[1999] Store the generated data in a relational database (e.g., MySQL).

[2000] The server inputs the saved data into a generative AI model (e.g., OpenAI GPT-3) to automatically generate PR content. For example, the prompt could be, "Generate PR content about yesterday's game results. The game score was 3-2, and the game date was yesterday."

[2001] The generated content is sent to an administrator's terminal, where the administrator checks the content and makes corrections as necessary.

[2002] When the administrator clicks the approval button, the server posts the approved content to the LINE Official Account's timeline.

[2003] 2. Automated responses to user questions

[2004] The server receives the question (e.g., "When is the next game?") sent by the user via the LINE Official Account.

[2005] The server uses a natural language processing engine (e.g., spaCy) to analyze the question and extract key content.

[2006] An emotion engine (e.g., IBM Watson Tone Analyzer) is used to analyze the user's emotional state (e.g., joy, anger, sadness, etc.).

[2007] The server retrieves relevant information (e.g. "date of next game") from a database using an SQL query.

[2008] Based on the acquired information and the results of sentiment analysis, a prompt is input into the generative AI model to generate a response. A possible prompt might be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[2009] The generated answer is automatically sent back to the user via the LINE Messaging API.

[2010] 3. Sending donation messages

[2011] The server automatically generates a donation message by inputting it into the AI ​​model depending on the start time of the donation campaign. For example, it uses the prompt, "Please create a message for the donation campaign starting next week. The main points are to explain the importance of donations and how to donate."

[2012] The server sends the generated message to all users as a push notification from the LINE official account.

[2013] When a user receives the push notification and clicks on the link in the message, the user's device will display a donation page.

[2014] The user completes the donation procedure on the donation page, and the device displays a notification that the donation has been completed.

[2015] This system automates public relations and fundraising activities and responds appropriately to the user's emotional state, which not only increases user satisfaction but also enables effective fundraising.

[2016] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2017] Step 1:

[2018] The server obtains generated data (e.g., match results, player information, event schedules) from the sports organization's management terminal. The server periodically issues API requests, receives data from the management terminal, and imports it in JSON format. The obtained generated data is stored in a relational database (e.g., MySQL).

[2019] Input: Data generated from the management terminal of a sports organization

[2020] Data processing: Convert JSON data into database format

[2021] Output: Generated data stored in a database

[2022] Step 2:

[2023] The server inputs PR content into a generative AI model (e.g., OpenAI GPT-3) based on the saved generated data, and generates automatic content. For example, the prompt text could be, "Generate a PR text about the results of yesterday's game. The game score was 3-2, and the game date was yesterday." The generated content is temporarily saved in JSON format.

[2024] Input: Generated data stored in a database

[2025] Data processing: Input to generative AI models

[2026] Output: Generated promotional content

[2027] Step 3:

[2028] The generated PR content is sent to the administrator's terminal. The administrator (user) uses the management interface to check the generated content and make corrections as necessary. Once corrections are complete, the administrator clicks the approval button.

[2029] Input: Generated PR content, admin modifications

[2030] Data processing: Correction and approval through the management interface

[2031] Output: Approved content

[2032] Step 4:

[2033] The server posts the approved content to the LINE Official Account's timeline automatically using the LINE API.

[2034] Input: Approved Content

[2035] Data processing: Posting using LINE API

[2036] Output: Content posted to the LINE Official Account's timeline

[2037] Step 5:

[2038] A user sends a question (e.g., "When is the next game?") via the LINE Official Account. The server receives the message using the LINE Messaging API and obtains the text data.

[2039] Input: Question message from user

[2040] Data processing: Receiving text data

[2041] Output: User question data

[2042] Step 6:

[2043] The server uses a natural language processing engine (e.g., spaCy) to analyze the question, and based on the extracted intent and keywords, retrieves relevant information (e.g., "next game schedule") from a database using an SQL query.

[2044] Input: User question data

[2045] Data processing: Question analysis using natural language processing, database search using SQL queries

[2046] Output: Related information (e.g., next game date)

[2047] Step 7:

[2048] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state. Based on the analyzed emotional state (e.g., joy, anger, sadness, etc.), a generative AI model is used to generate a response with an appropriate tone and content. An example of a prompt could be, "The user is asking about the next game date and seems excited. The next game is on August 15th. Based on this, please generate a response that encourages cheering."

[2049] Input: relevant information, user's emotional state

[2050] Data processing: Generating answers using a generative AI model

[2051] Output: Generated answer

[2052] Step 8:

[2053] The generated response is sent back to the user via the LINE Messaging API. The server sends the generated response as a message.

[2054] Input: Generated answer

[2055] Data processing: Sending messages using the LINE API

[2056] Output: The answer message sent to the user

[2057] Step 9:

[2058] The server inputs the donation solicitation message into the AI ​​model according to the start date of the donation solicitation campaign and automatically generates the message. For example, it uses the prompt "Please create a message for the donation campaign starting next week. The main points should be to explain the importance of donations and how to donate." The generated message is sent to all users as a push notification to the LINE official account.

[2059] Input: Donation message template

[2060] Data processing: Message generation using generative AI models

[2061] Output: Generated donation message

[2062] Step 10:

[2063] When a user receives a push notification and clicks the link in the message, the user's device will display a donation page. The user enters the donation amount and provides the necessary information on the donation page. The device will then complete the donation process and display a notification that the donation has been completed.

[2064] Input: Push notification link, user input information

[2065] Data processing: Displaying donation pages and carrying out donation procedures

[2066] Output: Notification of donation completion

[2067] In this way, the system can effectively promote and solicit donations throughout the entire process.

[2068] (Application example 2)

[2069] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2070] Amateur sports organizations are required to streamline their public relations and donation-raising activities while also providing appropriate responses based on user emotions. Conventional systems lack sufficient emotional responses, which can lead to a poor user experience. In particular, accurately capturing the enthusiasm and emotions of sports fans and providing appropriate public relations and donation-raising messages is crucial for expanding support for the organization.

[2071] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2072] In this invention, the server includes means for receiving generated data, means for automatically generating content based on the received generated data, means for posting the generated content to the platform, means for receiving questions from users, means for analyzing the received questions and automatically generating appropriate answers, means for returning the generated answers to the users, means for generating and sending donation solicitation messages to the users, means for directing the users to a donation page, and means including an emotion analysis engine for analyzing the user's emotions and adjusting the content and answer sentences based on the analyzed emotions. This enables public relations activities and donation solicitations to be conducted in accordance with the user's emotions, thereby effectively spreading support for sports organizations.

[2073] "Generated Data" refers to information generated by sports organizations, such as match results, player information, and event schedules.

[2074] "Content" refers to promotional text and images automatically generated by a generative AI model based on generated data.

[2075] "Platform" refers to an intermediary system, such as an online communication tool or social networking site, that allows users to exchange information with sports organizations.

[2076] A "question" refers to an inquiry that a user inputs to the system.

[2077] "Emotion analysis engine" refers to an engine for analyzing a user's emotional state from input text or voice.

[2078] A "donation solicitation message" refers to a message created by a sports organization to call on users for donations.

[2079] A "generative AI model" refers to an artificial intelligence model that performs natural language processing, image generation, etc. based on input data.

[2080] A "natural language processing engine" refers to an engine that analyzes text data entered by a user, understands its meaning, and generates an appropriate response.

[2081] MODE FOR CARRYING OUT THE INVENTION

[2082] The present invention is a system for automating public relations and donation collection activities for amateur sports organizations, and in particular, a system that uses an emotion analysis engine to realize appropriate responses according to the user's emotions. To achieve this purpose, the system includes the following components and processing steps:

[2083] 1. System Configuration

[2084] This system mainly consists of a server, a user terminal, and a platform.

[2085] server:

[2086] The server is the main component that receives generated data, automatically generates content, analyzes questions and generates answers, generates and sends donation messages, and analyzes user sentiment. Specifically, the following hardware and software are used:

[2087] Hardware: High-performance server unit

[2088] Software: Generative AI models, natural language processing engines, sentiment analysis engines (e.g., Hugging Face Transformers)

[2089] User device:

[2090] A user terminal is the device that a user uses to enter questions, receive answers, and access the donation page, typically a smartphone or personal computer.

[2091] Platform:

[2092] The platform is a system that includes online communication tools and social networking sites, and serves as a medium for the smooth exchange of information between users and sports organizations.

[2093] 2. Program Processing

[2094] The server performs the following process:

[2095] 1. Receiving generated data and generating content

[2096] The server periodically acquires generated data such as match results, player information, and event schedules from the sports organization's management terminal, and then uses this data to apply a generative AI model to automatically generate promotional content.

[2097] 2. Question Analysis and Answer Generation

[2098] When a user submits a question through the platform, the server receives the question and analyzes it using a natural language processing engine. Based on the analysis results, the sentiment analysis engine identifies the user's emotional state, retrieves relevant information from the database, and generates an appropriate answer that is tailored to the user's emotions.

[2099] 3. Generate and send a donation message

[2100] The server generates a donation message based on the start time of the donation campaign and sends it as a push notification to all users, who can then access the donation page.

[2101] 3. Examples and prompts

[2102] Example 1: Creating promotional content for match results

[2103] The server receives the match result data and uses a generative AI model to generate a sentence such as, "Yesterday's match was a close one, but we won 3-2." The generated sentence is then reviewed and approved by an administrator and automatically posted to the platform's timeline.

[2104] Example 2: Automatic response to user questions

[2105] When a user sends a question such as "When is the next game?", the server analyzes the question and uses an emotion analysis engine to determine the user's emotional state as "excited." The server then retrieves the information "The next game is on August 15th" from the database and uses a generative AI model to generate an automatic response saying "The next game is on August 15th. Please cheer us on!"

[2106] Prompt Sentence Examples

[2107] "Generate a news article from the following data: [generated data]"

[2108] "Generate a friendly, positive message about the next game: The next game is on August 15."

[2109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2110] Step 1:

[2111] The server periodically acquires generated data such as match results, player information, and event schedules from the management terminals of sports organizations. This input data is sent to the server via API and stored in a database.

[2112] Step 2:

[2113] The server inputs the generated data in the database into a generative AI model to automatically generate PR content. Specifically, it uses the prompt "Generate a news article from the following data: [generated data]" and outputs content in the form of a news article. This output is temporarily saved and awaits confirmation by an administrator.

[2114] Step 3:

[2115] The server sends the generated content to an administrator terminal, who reviews the content and makes any necessary corrections. The server then receives the approved content and automatically posts it to the platform's timeline. The output of this step is the published news content.

[2116] Step 4:

[2117] When a user submits a question through the platform, the server receives the question. The question is sent to the server in text format and input into a natural language processing engine for analysis. The engine analyzes the meaning of the question and identifies the intent of the question as its output.

[2118] Step 5:

[2119] The server identifies the user's emotional state based on the analysis results using a sentiment analysis engine, which extracts emotions from the user's text and outputs emotional states such as "excitement," "joy," and "anger."

[2120] Step 6:

[2121] The server retrieves relevant information from the database based on the results of the sentiment analysis. For example, if the question is "When is the next game?", it retrieves the date and time of the next game. This output information is used in the next step.

[2122] Step 7:

[2123] The server inputs the acquired information and the results of the sentiment analysis engine into a generative AI model to generate an appropriate response. It uses a prompt such as "Generate a friendly, positive message about the next game: The next game is on August 15." and outputs an appropriate response for the user.

[2124] Step 8:

[2125] The generated answer is automatically sent back to the user. A reply message is sent to the user's device via the platform's chat function. The output of this step is the appropriate answer delivered to the user.

[2126] Step 9:

[2127] The server generates a donation message based on the start date of the donation campaign. Using a generative AI model, it outputs a message calling for donations: "We need your support! Please donate to our team to keep us going."

[2128] Step 10:

[2129] The server sends the generated donation message to all users as a platform push notification. When a user receives the notification and clicks on the link in the message, the user's device is redirected to the donation page. The output of this step is a user directed to the donation page.

[2130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2131] 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.

[2132] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2134] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2140] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2141] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2145] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2146] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable...

Claims

1. means for receiving the generated data; A means for automatically generating content based on the received generation data; a means for posting generated content to the Platform; means for receiving a query from a user; A means for analyzing received questions and automatically generating appropriate answers; means for returning the generated answer to the user; means for generating and transmitting a solicitation message to a user; A means to direct users to a donation page; A system including:

2. The system according to claim 1, characterized in that the means for automatically generating the content is implemented using a generative AI model using data such as sports team match results, player information, and event schedules.

3. 2. The system of claim 1, wherein the means for analyzing the question and automatically generating an answer uses a natural language processing engine to extract information from a corresponding database and generate the answer.

Images