system

Abstract

We provide the system. [Solution] A means of collecting information related to a specific target from the internet using generative artificial intelligence, A means of storing the collected information in a database and providing an interface that users can access, A means of generating personalized support suggestions by analyzing the user's past activity history and interests, A means of providing events and content that promote interaction among multiple users with common interests, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Activities for supporting a specific target are scattered in various places, making it difficult for fans to comprehensively grasp the necessary information. Also, the opportunities for fans to communicate with each other are limited, and it is not easy to find a method for effective support activities. Furthermore, there is a lack of a mechanism for making the support activities creative by receiving support proposals tailored to individual fans.

Means for Solving the Problems

[0005] This invention provides an information gathering system utilizing generative artificial intelligence, enabling centralized information management by providing means for comprehensively collecting information related to a specific target from the internet. Furthermore, by storing the collected information in a database and providing a user-accessible interface, fans can easily access the latest information. In addition, by providing means for analyzing users' past activity history and interests and generating personalized support suggestions, the system offers original support methods to users. Moreover, by including means for providing events and content to promote interaction among multiple users with shared interests, the system can strengthen connections among fans.

[0006] "Generative artificial intelligence" is a type of artificial intelligence technology that has the ability to learn from data and generate new information and ideas.

[0007] "Information related to a specific subject" refers to data including news, event information, and social media posts concerning a particular artist, athlete, or other person who is supported by a specific individual.

[0008] "Collecting from the internet" refers to the process of automatically detecting and extracting information that is publicly available online using web crawlers and scraping techniques.

[0009] "Storing information in a database" refers to a method of structuring and saving collected information so that it can be easily searched and accessed later.

[0010] A "user-accessible interface" is a screen or application designed to allow users to easily access and interact with the information and content they need.

[0011] "Past activity history" refers to records of the user's past support activities, events they have participated in, and content they have shown interest in.

[0012] "Generating personalized support suggestions" refers to the process of using AI to propose the most suitable support methods and activities based on the user's unique preferences and behaviors.

[0013] "Interaction between multiple users with common interests" refers to interactions that encourage users who support the same object of support to communicate and share information with each other. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system that effectively supports fan support activities by utilizing generative artificial intelligence. The entire system consists of three main parts: information collection and analysis, information provision to users, and promotion of interaction among fans.

[0036] First, the server uses a web crawler to collect information related to a specific target on the internet. This process retrieves data from multiple sources, such as official websites, news sites, and social networking platforms. The collected information is stored in a structured database, ready for later searching and access.

[0037] Next, the user's device connects to the server to access the latest information. The device sends the user's past activity history and interest data to the server, which then generates support suggestions. The generating AI analyzes the user's data and suggests the most suitable ways to support, events to participate in, and recommended merchandise. These suggestions are displayed on the user dashboard, allowing the user to easily select and perform the suggested activities.

[0038] Furthermore, the server analyzes common hobbies and interests among users and connects like-minded individuals who support the same things. The terminal notifies users of invitations to online forums, group chats, and events, encouraging participation and stimulating interaction among fans.

[0039] For example, if a user is a fan of a popular artist, the server collects the latest concert and new song release information related to that artist and notifies the user's device. The user can then use this information to decide whether to attend the next concert, interact with other fans online, or purchase limited edition merchandise.

[0040] In this way, this system uses generative artificial intelligence to comprehensively support information management and suggestions for cheering activities, as well as communication among fans.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server launches a web crawler and searches the internet for information based on defined keywords. It automatically retrieves web pages, news articles, social media posts, and other information related to the specified target.

[0044] Step 2:

[0045] The server analyzes the retrieved information and extracts the necessary metadata (e.g., title, URL, date, category). This data is then stored in a database in a structured format.

[0046] Step 3:

[0047] The user's device accesses the server to retrieve the latest information of interest to the user. The device displays this information on a dashboard, making it easy for the user to view.

[0048] Step 4:

[0049] The device sends the user's past behavioral history, browsing history, and information of interest to the server. This allows the server to accumulate data based on the user's interests.

[0050] Step 5:

[0051] The server applies machine learning algorithms to analyze accumulated user data, revealing trends based on user interests and preferences.

[0052] Step 6:

[0053] The server uses a generation AI to create suggestions for support methods and events tailored to the user. These suggestions are customized to the user's specific interests.

[0054] Step 7:

[0055] The terminal receives suggestions from the server and provides them to the user. Based on this information, the user can consider trying new ways of cheering or participating in related events.

[0056] Step 8:

[0057] The server analyzes users with shared interests and generates appropriate fan groups and community events.

[0058] Step 9:

[0059] The device notifies users of event and group chat invitations, encouraging them to participate. Users can then deepen their interactions with other fans.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Many users find it difficult to quickly and efficiently gather information related to a specific subject and to engage in optimal support activities based on their individual preferences and interests. Furthermore, effectively facilitating interaction with other users who share the same hobbies is also a challenging task.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for collecting information from a public network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display device accessible to the user, and means for analyzing the user's past behavioral history and interests to generate personalized activity suggestions. This enables users to quickly access necessary information, receive support activity suggestions optimized to their individual preferences, and effectively interact with other users who share the same hobbies.

[0065] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and automatically generates information and suggestions tailored to specific purposes.

[0066] A "public network" is an information network accessible to the general public, and the internet is an example of this.

[0067] "Collecting information" refers to the process of searching for and obtaining data related to a specific subject, and includes technologies such as web crawling.

[0068] A "storage device" is a hardware device or software system used to store and save data.

[0069] A "display device" is an interface that allows users to visually confirm digital data, and includes computer monitors and smartphone screens.

[0070] "Behavioral history" refers to a record of a user's past activities and choices, and is data used for information analysis.

[0071] "Activity suggestions" refer to guidance on activities or events recommended to users based on their specific interests and concerns.

[0072] "Promoting interaction" refers to actions that support and activate communication and collaborative activities among multiple users.

[0073] In implementing this invention, the server collects information related to a specific target via a public network using generative artificial intelligence. Specifically, it obtains data from information sources using web crawling technology and analyzes the data using libraries such as Beautiful Soup or Scrapy. The server then structures the collected data and stores it in a memory device.

[0074] The device accesses the server and provides information to the user. The device sends this data to the server to analyze the user's behavioral history and interests. The server uses a generative AI model to generate activity suggestions tailored to the user. This process is expected to utilize generative AI technology (e.g., language models).

[0075] For example, one possible prompt that could be generated is, "Please generate the best message of encouragement for attending the next concert." Based on this prompt, the AI ​​provides customized suggestions to the user.

[0076] Furthermore, the server also plays a role in facilitating interaction among users. Based on data extracted from users with similar interests, it provides information about online forums and social events to the terminal. The terminal then notifies users of this information and encourages them to participate. For example, prompts such as "Please provide information on how to participate in the online fan meeting" can be entered into the AI.

[0077] In this way, the invention supports the flow of information and improves the user experience, comprehensively supporting support activities that meet the needs of users in specific areas of interest and activity.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server launches a web crawler to collect information related to a specific target from the public network. Specifically, it utilizes libraries such as Beautiful Soup and Scrapy to extract text data from the information source. The input requires keywords related to the specific target, and the output is the extracted raw text data. This data is used in subsequent processing steps.

[0081] Step 2:

[0082] The server analyzes and structures the collected information. Using the collected text data as input, it employs Natural Language Processing (NLP) techniques to transform the data into a meaningful format. This process eliminates data duplication and prioritizes the organization of highly relevant information. The output is structured data stored in a database.

[0083] Step 3:

[0084] The device communicates with the server to transmit the user's behavioral history and interest information. Inputs include the user's past activity history data, browsing history, and purchase records. Outputs are prompts that are input into a generating AI model. These prompts are designed to generate activity suggestions based on the user's interests.

[0085] Step 4:

[0086] The server uses a generative AI model to create activity suggestions based on the user's interests. Taking prompts as input, the AI ​​model analyzes a large amount of data and generates personalized encouragement messages and event suggestions. The output provides specific suggestions, such as recommended events and information on interacting with other fans.

[0087] Step 5:

[0088] The terminal displays the received suggestions on the user dashboard and notifies the user. It uses the suggestions received from the server as input. As output, the information is visually organized, allowing the user to easily view and select suggestions. New event information and online activities are also notified to the user.

[0089] Step 6:

[0090] The server identifies other users with similar interests and suggests relevant interaction events and online forums to facilitate communication among users. It uses user interest information and suggested content as input. As output, participation guides are created and provided to users via their terminals. This enables active interaction among fans.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] The sheer volume of information on the internet creates a problem where users cannot efficiently obtain reliable information related to their areas of interest. Furthermore, the difficulty in accessing information about new related activities and products means that many users miss out on materials and events that interest them. Additionally, there is a lack of interaction among users with similar interests, limiting opportunities for sharing information and engaging in discussions.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for collecting information related to a specific area of ​​interest from the internet using generative artificial intelligence, means for selecting and notifying users of new content and product information according to their preferences, and means for providing activities and materials that facilitate interaction among a large number of users with common interests. As a result, users can receive the latest information related to their individually customized areas of interest and efficiently share information and engage in discussions with other users who share the same interests.

[0096] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to extract specific information from large amounts of data on the internet and generate suggestions and notifications based on the user's interests.

[0097] A "specific area of ​​interest" refers to a particular theme or genre that is the subject of information collection and analysis, and is an area related to the user's interests.

[0098] "Information gathering" is the process of obtaining data that meets specified criteria from various sources on the internet and preparing it for analysis.

[0099] "User preferences" refer to the tendencies and tastes that individual users show interest in, based on their past behavioral history and choices.

[0100] "New content and product information" refers to information about recently announced events, materials, or products that provide new insights into the user's areas of interest.

[0101] "Notifications" refer to information and alerts provided by a system to users, and are a means of delivering information to users quickly and efficiently.

[0102] "Interaction between users" refers to activities in which multiple users with common interests share information and engage in discussions with each other.

[0103] "Activities and materials" refer to events that users can participate in or use, as well as content for enjoyment and learning.

[0104] This invention realizes a system for providing information and facilitating communication using generative artificial intelligence. The server continuously collects information on specific areas of interest from the internet using generative artificial intelligence and stores this data in a database. This information includes news articles, information from official websites, and posts on social media.

[0105] The user's device connects to the server to access this information. The user's past activity history and interest data, transmitted from the device, are analyzed by the server's generated artificial intelligence. Based on this analysis, the server dynamically selects new content and product information tailored to the user's preferences and notifies the user. This process can utilize Python's Beautiful Soup or Scrapy for data collection and Pandas or Scikit-learn for data analysis. Furthermore, APIs can be built using Flask or Django to display the analysis results to the user.

[0106] For example, if a user is interested in a particular artist, the server will collect information on related new song releases and live events and immediately notify the user. This ensures that users never miss out on the latest information. Furthermore, users can discuss and share information with other users who share similar interests through online forums and chat functions. These features encourage more active interaction among users.

[0107] Examples of input prompts for a generative AI model are as follows:

[0108] "User interests: Providing information on new music releases and live events related to artists."

[0109] This system simultaneously achieves increased efficiency in information retrieval and facilitates communication among users.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server collects data from various sources on the internet. Using Python's Beautiful Soup and Scrapy, it gathers information related to the target area of ​​interest from news sites and social media platforms. Input is a URL or query condition, and output is the retrieved content data. This data is structured and stored in a database.

[0113] Step 2:

[0114] The server analyzes the information stored in the database. Based on the user's past behavior history and interest data, a generative AI model filters and prioritizes the information. The input is the user's history data and collected data, and the output is a list of the information most relevant to the user. This process uses data analysis techniques such as Pandas and Scikit-learn.

[0115] Step 3:

[0116] The terminal connects to the server and receives the analyzed information. The server sends a list of prioritized content, which is then notified to the user via an application on the terminal. The input is the analysis results from the server, and the output is the information notification displayed in the user interface.

[0117] Step 4:

[0118] Users make decisions based on the information they receive. For example, if they are notified of a new song release, they might decide to listen to the artist's new song or attend a related event. This process involves making decisions based on the user's preferences.

[0119] Step 5:

[0120] The server analyzes users with shared interests and provides opportunities for interaction. It facilitates interaction by sending notifications for user-to-user chat functions and forum participation. Input is user interest data, and output is invitation notifications for interaction events and group chats.

[0121] An example of prompt input to the generating AI model is, "User interests: Provide information on new song releases and live events related to the artist." This allows users to continuously receive the latest information on content they are interested in.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention provides a system that more effectively supports users' cheering activities by fusing generative artificial intelligence and an emotion engine. This system has the functions of information collection and management, generation of individual cheering suggestions, dynamic adjustment based on recognition of user emotions, and promotion of interaction among fans.

[0124] First, the server uses artificial intelligence to collect the latest information related to a specific target from the internet. This information is diverse, including news articles, social media posts, and updates to official websites. The collected data is stored in a database and can be accessed from the user's device. The device then visually displays the information that the user is interested in on its interface.

[0125] Next, the user's device uses its built-in camera and sensors to collect emotional data from the user's facial expressions, voice, and actions. This information is sent to a server, where an emotion engine recognizes the user's emotions at that moment. The server then considers this emotional data and generates optimal support suggestions based on the user's past activity history and interests.

[0126] Support suggestions are designed to boost user motivation and create a sense of comfort. For example, if a user needs encouragement, the emotion engine suggests uplifting content, music, and participatory events. Conversely, if a user is seeking relaxation, it recommends calming content and community activities.

[0127] Furthermore, the server groups users with similar emotional tendencies and provides content and events that connect fans with shared interests. The device notifies users of these group activities and events, facilitating emotion-based communication.

[0128] For example, if a user feels sad about the activities of their favorite artist, that emotion will be recognized, and the server will provide comforting messages and opportunities to connect with other fans of the same artist. In this way, by incorporating an emotion engine, the system realizes a more advanced form of support for fan activities that is more attentive to the individual needs of each user.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The server uses generative artificial intelligence to collect information related to specific targets from the internet. For example, it periodically scans and retrieves news articles, updates to official websites, and social media posts.

[0132] Step 2:

[0133] The server analyzes the acquired information, adds metadata, and stores it in a database. This makes the information easily searchable and accessible later.

[0134] Step 3:

[0135] The device accesses the database and filters relevant information based on the user's past interests, displaying it on the dashboard.

[0136] Step 4:

[0137] The user's device uses sensors such as cameras and microphones to collect emotional data from the user's facial expressions, voice tone, and operation patterns.

[0138] Step 5:

[0139] The device sends emotional data to the server in real time. The server analyzes this data using an emotion engine to identify the user's current emotions.

[0140] Step 6:

[0141] The server considers the user's emotional state and past activity history to generate personalized support suggestions. For example, if a user is feeling down, it will suggest encouraging messages and related events.

[0142] Step 7:

[0143] The device receives suggestions from the server and displays them in a format suitable for the user. Based on this, the user can then engage in new support activities.

[0144] Step 8:

[0145] The server identifies and groups users with similar feelings and interests, and sets up common events and chat rooms for them.

[0146] Step 9:

[0147] The device notifies the user of opportunities to interact with other users who share similar feelings. Users can use this to deepen their bonds with other fans.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In a world overflowing with information on topics of interest to users, providing appropriate support suggestions based on each user's emotions and interests is challenging. Finding suitable spaces and content for interaction is also not easy. Furthermore, it is necessary to immediately grasp the user's situation and respond flexibly accordingly. This necessitates promoting optimal information sharing and interaction for each individual user.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for collecting data related to a specific target from a communication network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display method accessible to the user, and means for detecting the user's facial expressions, voice, and operating status and recognizing their emotional state. This enables dynamic and appropriate support suggestions and information provision according to the emotional state of each user, and facilitates information sharing and interaction that is in line with the user's interests and emotions.

[0153] "Generative artificial intelligence" is a technology that generates new information and suggestions in computer systems by analyzing and learning from large amounts of data.

[0154] A "communication network" is a wide-area information transmission infrastructure for sending and receiving data via the internet and other networks.

[0155] A "storage device" is a hardware component that can store data and information for a long period of time.

[0156] A "user" is an individual or organization that operates an information technology system and receives its services.

[0157] "Display method" refers to the means and techniques for visually presenting data and information to users.

[0158] "Operation history" refers to a record of a series of actions or events performed by a user on the system.

[0159] "Emotional state" refers to data that indicates the user's emotional response and psychological state.

[0160] A "support suggestion" is a set of recommended actions and content provided based on the user's condition and interests.

[0161] "Activities that promote connectivity" refer to events and programs designed to increase opportunities for multiple users to exchange information and cooperate with each other.

[0162] This invention is a system that utilizes generative artificial intelligence (AI) to provide information and facilitate interaction tailored to the user's emotional state.

[0163] The server uses a generative AI model to collect data related to specific targets from communication networks, which are a wide-area information transmission infrastructure. Specific data collection is carried out through RSS feeds from news sites, APIs from social media platforms, and information retrieval from official websites. The collected data is stored in a memory device, where duplicate data and noise are filtered out, and the necessary information is organized.

[0164] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. This allows it to determine the user's emotional state in real time and transmit it to a server. An emotion engine analyzes this data to recognize the user's emotional state.

[0165] Based on the recognized emotional state, the server references the user's past activity history and interests, and creates personalized support suggestions through a generative AI model. These suggestions are then provided on the user's device through a display method accessible to the user. For example, if the user is feeling stressed, relaxation-friendly music, nature videos, and information on relaxing events they can participate in may be recommended.

[0166] Furthermore, the server recognizes multiple users with similar emotional tendencies and provides features such as interaction events and group chats to facilitate connections between users with common interests. This allows users to have a more fulfilling experience through active interaction.

[0167] As an example of a prompt, you can input "What content should be provided if the user's emotional state is 'joyful'?" to see how the system generates specific support suggestions. This system enables more detailed support than before by accurately recognizing the user's emotional state and providing information and experiences that match it.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server uses a generative AI model to collect data related to a specific target from the communication network. Inputs include URLs and query parameters obtained through RSS feeds on news sites and APIs on social media. Outputs are the collected raw text data and metadata. The server filters this data to remove duplication and noise, and organizes the data.

[0171] Step 2:

[0172] The server stores the organized information in storage. The input is the text data filtered in step 1. The output is a set of information stored in a searchable format. The server does this using a database management system and indexes it for easier access by users later.

[0173] Step 3:

[0174] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. It receives the user's biometric information and behavioral logs as input. The output is emotion data based on this information. The device performs real-time processing and applies machine learning algorithms to estimate the emotional state.

[0175] Step 4:

[0176] The device sends estimated emotion data to the server. The input is the emotion data obtained in step 3. The output is the emotion data and its associated metadata. The server analyzes this data using an emotion engine to recognize the user's accurate emotional state.

[0177] Step 5:

[0178] The server references the recognized emotional state and the user's past operation history and interests, and creates support suggestions through a generative AI model. The inputs are emotional data, past operation history, and interest information. The output is personalized support suggestions. The server processes these using the generative AI model to generate specific content tailored to the user's needs.

[0179] Step 6:

[0180] The terminal presents the user with support suggestions received from the server. The input is the support suggestions generated in step 5. The output is the content presented through the user's display or notification system. The terminal displays these suggestions in a visually clear manner so that the user can easily view and act upon them.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] In modern content distribution services, it is difficult to grasp user satisfaction in real time and dynamically adjust content based on those emotions. Furthermore, methods for smoothly facilitating interaction among users are limited. These challenges should be addressed by systems that can appropriately analyze user emotions and provide the necessary support.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for collecting information related to a specific object from an information network, means for storing the collected information in an information storage means and making it accessible to the user via an interactive display device, and means for detecting the user's facial expressions and voice and analyzing their emotional state to dynamically adjust support suggestions in real time. This makes it possible to improve the user's viewing satisfaction, customize content according to individual emotions, and effectively promote interaction among users.

[0186] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate information and make optimal suggestions according to a specific purpose.

[0187] An "information network" is a foundation for acquiring distinctive data via communication networks such as the internet and utilizing it for various services.

[0188] "Information storage means" refers to a function that organizes the diverse information collected and keeps it in a state where it can be quickly searched and used as needed.

[0189] An "interactive display device" is a device that allows users to visually confirm information and interact with that information.

[0190] "Detecting the user's facial expressions and voice" is a process that uses cameras and microphones to extract features that allow for the inference of the user's emotional state.

[0191] "Analyzing emotional state" involves estimating the user's mental tendencies and mood from detected facial expressions and voice data, and clarifying that state.

[0192] "Dynamically adjusting support suggestions in real time" refers to the process of appropriately modifying suggestions to optimize them for the user's emotions at that moment, thereby providing more appropriate feedback.

[0193] The system for realizing this invention mainly consists of a generative artificial intelligence, an emotion engine, an information gathering module, a display device, and the like.

[0194] First, the server collects information related to a specific target through the information network. This includes the latest news, social media posts, and updates to the official website. This information is stored in an information storage system and can be viewed by the user via an interactive display device.

[0195] While a user is viewing content, the device continuously monitors the user's facial expressions and voice using its built-in camera and microphone. This data is sent to a server and analyzed by an emotion engine. This process utilizes emotion recognition frameworks such as OpenPose and machine learning frameworks such as TENSORFLOW®.

[0196] Based on the analyzed emotional state, the server generates personalized support suggestions for the user. These suggestions might include recommendations for the next content to watch or the display of supplementary text information to explain what is being watched. This increases the user's viewing attention and provides a content experience that perfectly matches their emotions.

[0197] Furthermore, it's possible to identify other users who share similar emotions and provide online communication platforms to foster community engagement. For example, if a user is moved to tears by a touching film, a platform could be suggested where they can share that emotional experience through social media comments or reviews.

[0198] As a concrete example, a generative AI model may be used to generate prompt statements like the following.

[0199] "If a user is crying while watching an emotionally moving film, what kind of content suggestions or personalized messages should be presented next?"

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server collects the latest information related to a specific target from the internet via an information network. This process uses web scraping techniques to extract data from news sites and social media feeds. Given URLs or keywords as input, it crawls relevant information based on these and outputs it as text data. This allows important data to be stored in information storage systems.

[0203] Step 2:

[0204] The device monitors the user's facial expressions and voice using its built-in camera and microphone. The input consists of real-time video and audio data, which are sampled periodically. This data is analyzed by an emotion recognition algorithm to identify the user's emotional state. The output is tag information indicating the current emotional state. This analysis utilizes OpenPose and TensorFlow to obtain highly accurate emotional data in real time.

[0205] Step 3:

[0206] The server generates support suggestions based on analyzed sentiment data and utilizing previously collected information. Input includes the user's emotional state and viewing history. The server uses a generative AI model to generate prompts for suggesting optimal content and messages. Based on these prompts, it outputs recommended content and interactions within that content. This allows for a more personalized user experience.

[0207] Step 4:

[0208] The user's device receives suggestions from the server and presents them to the user through an interactive display device. The input is the suggestion information received from the server, which is then visually displayed on the screen. The output is designed to capture the user's attention and provide emotionally resonant encouragement and interactive content. This process allows the user to experience real-time changes in content and further promotes interaction on the platform.

[0209] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0225] This invention provides a system that effectively supports fan support activities by utilizing generative artificial intelligence. The entire system consists of three main parts: information collection and analysis, information provision to users, and promotion of interaction among fans.

[0226] First, the server uses a web crawler to collect information related to a specific target on the internet. This process retrieves data from multiple sources, such as official websites, news sites, and social networking platforms. The collected information is stored in a structured database, ready for later searching and access.

[0227] Next, the user's device connects to the server to access the latest information. The device sends the user's past activity history and interest data to the server, which then generates support suggestions. The generating AI analyzes the user's data and suggests the most suitable ways to support, events to participate in, and recommended merchandise. These suggestions are displayed on the user dashboard, allowing the user to easily select and perform the suggested activities.

[0228] Furthermore, the server analyzes common hobbies and interests among users and connects like-minded individuals who support the same things. The terminal notifies users of invitations to online forums, group chats, and events, encouraging participation and stimulating interaction among fans.

[0229] For example, if a user is a fan of a popular artist, the server collects the latest concert and new song release information related to that artist and notifies the user's device. The user can then use this information to decide whether to attend the next concert, interact with other fans online, or purchase limited edition merchandise.

[0230] In this way, this system uses generative artificial intelligence to comprehensively support information management and suggestions for cheering activities, as well as communication among fans.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server launches a web crawler and searches the internet for information based on defined keywords. It automatically retrieves web pages, news articles, social media posts, and other information related to the specified target.

[0234] Step 2:

[0235] The server analyzes the retrieved information and extracts the necessary metadata (e.g., title, URL, date, category). This data is then stored in a database in a structured format.

[0236] Step 3:

[0237] The user's device accesses the server to retrieve the latest information of interest to the user. The device displays this information on a dashboard, making it easy for the user to view.

[0238] Step 4:

[0239] The device sends the user's past behavioral history, browsing history, and information of interest to the server. This allows the server to accumulate data based on the user's interests.

[0240] Step 5:

[0241] The server applies machine learning algorithms to analyze accumulated user data, revealing trends based on user interests and preferences.

[0242] Step 6:

[0243] The server uses a generation AI to create suggestions for support methods and events tailored to the user. These suggestions are customized to the user's specific interests.

[0244] Step 7:

[0245] The terminal receives suggestions from the server and provides them to the user. Based on this information, the user can consider trying new ways of cheering or participating in related events.

[0246] Step 8:

[0247] The server analyzes users with shared interests and generates appropriate fan groups and community events.

[0248] Step 9:

[0249] The device notifies users of event and group chat invitations, encouraging them to participate. Users can then deepen their interactions with other fans.

[0250] (Example 1)

[0251] Next, we will describe Example 1. 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."

[0252] Many users find it difficult to quickly and efficiently gather information related to a specific subject and to engage in optimal support activities based on their individual preferences and interests. Furthermore, effectively facilitating interaction with other users who share the same hobbies is also a challenging task.

[0253] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0254] In this invention, the server includes means for collecting information from a public network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display device accessible to the user, and means for analyzing the user's past behavioral history and interests to generate personalized activity suggestions. This enables users to quickly access necessary information, receive support activity suggestions optimized to their individual preferences, and effectively interact with other users who share the same hobbies.

[0255] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and automatically generates information and suggestions tailored to specific purposes.

[0256] A "public network" is an information network accessible to the general public, and the internet is an example of this.

[0257] "Collecting information" refers to the process of searching for and obtaining data related to a specific subject, and includes technologies such as web crawling.

[0258] A "storage device" is a hardware device or software system used to store and save data.

[0259] A "display device" is an interface that allows users to visually confirm digital data, and includes computer monitors and smartphone screens.

[0260] "Behavioral history" refers to a record of a user's past activities and choices, and is data used for information analysis.

[0261] "Activity suggestions" refer to guidance on activities or events recommended to users based on their specific interests and concerns.

[0262] "Promoting interaction" refers to actions that support and activate communication and collaborative activities among multiple users.

[0263] In implementing this invention, the server collects information related to a specific target via a public network using generative artificial intelligence. Specifically, it obtains data from information sources using web crawling technology and analyzes the data using libraries such as Beautiful Soup or Scrapy. The server then structures the collected data and stores it in a memory device.

[0264] The device accesses the server and provides information to the user. The device sends this data to the server to analyze the user's behavioral history and interests. The server uses a generative AI model to generate activity suggestions tailored to the user. This process is expected to utilize generative AI technology (e.g., language models).

[0265] For example, one possible prompt that could be generated is, "Please generate the best message of encouragement for attending the next concert." Based on this prompt, the AI ​​provides customized suggestions to the user.

[0266] Furthermore, the server also plays a role in facilitating interaction among users. Based on data extracted from users with similar interests, it provides information about online forums and social events to the terminal. The terminal then notifies users of this information and encourages them to participate. For example, prompts such as "Please provide information on how to participate in the online fan meeting" can be entered into the AI.

[0267] In this way, the invention supports the flow of information and improves the user experience, comprehensively supporting support activities that meet the needs of users in specific areas of interest and activity.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The server launches a web crawler to collect information related to a specific target from the public network. Specifically, it utilizes libraries such as Beautiful Soup and Scrapy to extract text data from the information source. The input requires keywords related to the specific target, and the output is the extracted raw text data. This data is used in subsequent processing steps.

[0271] Step 2:

[0272] The server analyzes and structures the collected information. Using the collected text data as input, it employs Natural Language Processing (NLP) techniques to transform the data into a meaningful format. This process eliminates data duplication and prioritizes the organization of highly relevant information. The output is structured data stored in a database.

[0273] Step 3:

[0274] The device communicates with the server to transmit the user's behavioral history and interest information. Inputs include the user's past activity history data, browsing history, and purchase records. Outputs are prompts that are input into a generating AI model. These prompts are designed to generate activity suggestions based on the user's interests.

[0275] Step 4:

[0276] The server uses a generative AI model to create activity suggestions based on the user's interests. Taking prompts as input, the AI ​​model analyzes a large amount of data and generates personalized encouragement messages and event suggestions. The output provides specific suggestions, such as recommended events and information on interacting with other fans.

[0277] Step 5:

[0278] The terminal displays the received suggestions on the user dashboard and notifies the user. It uses the suggestions received from the server as input. As output, the information is visually organized, allowing the user to easily view and select suggestions. New event information and online activities are also notified to the user.

[0279] Step 6:

[0280] The server identifies other users with similar interests and suggests relevant interaction events and online forums to facilitate communication among users. It uses user interest information and suggested content as input. As output, participation guides are created and provided to users via their terminals. This enables active interaction among fans.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0283] Due to the excessive information on the Internet, there is a problem that users cannot efficiently obtain reliable information related to their own areas of interest. In addition, it is difficult to access information on related new activities and products, so many users may miss materials and events that they are interested in. Furthermore, there is a lack of communication among users with the same interests, and the places for sharing and discussing information are limited.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for collecting information related to a specific area of interest from the Internet using a generative artificial intelligence, means for screening and notifying information on new content and products according to the user's preferences, and means for providing activities and materials that promote communication among a large number of users with common interests. Thereby, users can receive the latest information related to their individually customized areas of interest and can efficiently share information and conduct discussions with other users having the same interests.

[0286] The "generative artificial intelligence" is an artificial intelligence having the ability to extract specific information from a large amount of data on the Internet and generate proposals and notifications based on the user's interests.

[0287] The "specific area of interest" refers to a specific theme or genre that is the target of information collection and analysis, and is an area related to the user's interests.

[0288] "Information collection" is a process of obtaining data that meets specified conditions from various information sources on the Internet and preparing it for analysis.

[0289] "User preferences" refer to the tendencies and tastes that individual users show interest in, based on their past behavioral history and choices.

[0290] "New content or product information" refers to information about recently announced events, materials, or products that provide new insights into the user's areas of interest.

[0291] "Notifications" refer to information and alerts provided by a system to users, and are a means of delivering information to users quickly and efficiently.

[0292] "Interaction between users" refers to activities in which multiple users with common interests share information and engage in discussions with each other.

[0293] "Activities and materials" refer to events that users can participate in or use, as well as content for enjoyment and learning.

[0294] This invention realizes a system for providing information and facilitating communication using generative artificial intelligence. The server continuously collects information on specific areas of interest from the internet using generative artificial intelligence and stores this data in a database. This information includes news articles, information from official websites, and posts on social media.

[0295] The user's device connects to the server to access this information. The user's past activity history and interest data, transmitted from the device, are analyzed by the server's generated artificial intelligence. Based on this analysis, the server dynamically selects new content and product information tailored to the user's preferences and notifies the user. This process can utilize Python's Beautiful Soup or Scrapy for data collection and Pandas or Scikit-learn for data analysis. Furthermore, APIs can be built using Flask or Django to display the analysis results to the user.

[0296] For example, if a user is interested in a particular artist, the server will collect information on related new song releases and live events and immediately notify the user. This ensures that users never miss out on the latest information. Furthermore, users can discuss and share information with other users who share similar interests through online forums and chat functions. These features encourage more active interaction among users.

[0297] Examples of input prompts for a generative AI model are as follows:

[0298] "User interests: Providing information on new music releases and live events related to artists."

[0299] This system simultaneously achieves increased efficiency in information retrieval and facilitates communication among users.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The server collects data from various sources on the internet. Using Python's Beautiful Soup and Scrapy, it gathers information related to the target area of ​​interest from news sites and social media platforms. Input is a URL or query condition, and output is the retrieved content data. This data is structured and stored in a database.

[0303] Step 2:

[0304] The server analyzes the information stored in the database. Based on the user's past behavior history and interest data, the generative AI model filters the information and assigns priorities. The input is the user's historical data and collected data, and the output is a list of the most relevant information for the user. In this process, data analysis techniques using Pandas and Scikit-learn are employed.

[0305] Step 3:

[0306] The terminal connects to the server and receives the analyzed information. A list of prioritized content is sent from the server and notified to the user via an application on the terminal. The input is the analysis result from the server, and the output is an information notification displayed on the user interface.

[0307] Step 4:

[0308] The user decides their actions based on the received information. For example, when notified of a new song release, the user decides to listen to the artist's new song or participate in related events. This process involves judgments based on the user's preference information.

[0309] <00​​​​​​​​​​​​Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] This invention provides a system that more effectively supports users' cheering activities by fusing generative artificial intelligence and an emotion engine. This system has the functions of information collection and management, generation of individual cheering suggestions, dynamic adjustment based on recognition of user emotions, and promotion of interaction among fans.

[0314] First, the server uses artificial intelligence to collect the latest information related to a specific target from the internet. This information is diverse, including news articles, social media posts, and updates to official websites. The collected data is stored in a database and can be accessed from the user's device. The device then visually displays the information that the user is interested in on its interface.

[0315] Next, the user's device uses its built-in camera and sensors to collect emotional data from the user's facial expressions, voice, and actions. This information is sent to a server, where an emotion engine recognizes the user's emotions at that moment. The server then considers this emotional data and generates optimal support suggestions based on the user's past activity history and interests.

[0316] Support suggestions are designed to boost user motivation and create a sense of comfort. For example, if a user needs encouragement, the emotion engine suggests uplifting content, music, and participatory events. Conversely, if a user is seeking relaxation, it recommends calming content and community activities.

[0317] Furthermore, the server groups users with similar emotional tendencies and provides content and events that connect fans with shared interests. The device notifies users of these group activities and events, facilitating emotion-based communication.

[0318] For example, if a user feels sad about the activities of their favorite artist, that emotion will be recognized, and the server will provide comforting messages and opportunities to connect with other fans of the same artist. In this way, by incorporating an emotion engine, the system realizes a more advanced form of support for fan activities that is more attentive to the individual needs of each user.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server uses generative artificial intelligence to collect information related to specific targets from the internet. For example, it periodically scans and retrieves news articles, updates to official websites, and social media posts.

[0322] Step 2:

[0323] The server analyzes the acquired information, adds metadata, and stores it in a database. This makes the information easily searchable and accessible later.

[0324] Step 3:

[0325] The device accesses the database and filters relevant information based on the user's past interests, displaying it on the dashboard.

[0326] Step 4:

[0327] The user's device uses sensors such as cameras and microphones to collect emotional data from the user's facial expressions, voice tone, and operation patterns.

[0328] Step 5:

[0329] The device sends emotional data to the server in real time. The server analyzes this data using an emotion engine to identify the user's current emotions.

[0330] Step 6:

[0331] The server considers the user's emotional state and past activity history to generate personalized support suggestions. For example, if a user is feeling down, it will suggest encouraging messages and related events.

[0332] Step 7:

[0333] The device receives suggestions from the server and displays them in a format suitable for the user. Based on this, the user can then engage in new support activities.

[0334] Step 8:

[0335] The server identifies and groups users with similar feelings and interests, and sets up common events and chat rooms for them.

[0336] Step 9:

[0337] The device notifies the user of opportunities to interact with other users who share similar feelings. Users can use this to deepen their bonds with other fans.

[0338] (Example 2)

[0339] Next, we will describe Example 2. 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".

[0340] In a world overflowing with information on topics of interest to users, providing appropriate support suggestions based on each user's emotions and interests is challenging. Finding suitable spaces and content for interaction is also not easy. Furthermore, it is necessary to immediately grasp the user's situation and respond flexibly accordingly. This necessitates promoting optimal information sharing and interaction for each individual user.

[0341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0342] In this invention, the server includes means for collecting data related to a specific target from a communication network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display method accessible to the user, and means for detecting the user's facial expressions, voice, and operating status and recognizing their emotional state. This enables dynamic and appropriate support suggestions and information provision according to the emotional state of each user, and facilitates information sharing and interaction that is in line with the user's interests and emotions.

[0343] "Generative artificial intelligence" is a technology that generates new information and suggestions in computer systems by analyzing and learning from large amounts of data.

[0344] A "communication network" is a wide-area information transmission infrastructure for sending and receiving data via the internet and other networks.

[0345] A "storage device" is a hardware component that can store data and information for a long period of time.

[0346] A "user" is an individual or organization that operates an information technology system and receives its services.

[0347] "Display method" refers to the means and techniques for visually presenting data and information to users.

[0348] "Operation history" refers to a record of a series of actions or events performed by a user on the system.

[0349] "Emotional state" refers to data that indicates the user's emotional response and psychological state.

[0350] A "support suggestion" is a set of recommended actions and content provided based on the user's condition and interests.

[0351] "Activities that promote connectivity" refer to events and programs designed to increase opportunities for multiple users to exchange information and cooperate with each other.

[0352] This invention is a system that utilizes generative artificial intelligence (AI) to provide information and facilitate interaction tailored to the user's emotional state.

[0353] The server uses a generative AI model to collect data related to specific targets from communication networks, which are a wide-area information transmission infrastructure. Specific data collection is carried out through RSS feeds from news sites, APIs from social media platforms, and information retrieval from official websites. The collected data is stored in a memory device, where duplicate data and noise are filtered out, and the necessary information is organized.

[0354] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. This allows it to determine the user's emotional state in real time and transmit it to a server. An emotion engine then analyzes this data to recognize the user's emotional state.

[0355] Based on the recognized emotional state, the server references the user's past activity history and interests, and creates personalized support suggestions through a generative AI model. These suggestions are then provided on the user's device through a display method accessible to the user. For example, if the user is feeling stressed, relaxation-friendly music, nature videos, and information on relaxing events they can participate in may be recommended.

[0356] Furthermore, the server recognizes multiple users with similar emotional tendencies and provides features such as interaction events and group chats to facilitate connections between users with common interests. This allows users to have a more fulfilling experience through active interaction.

[0357] As an example of a prompt, you can input "What content should be provided if the user's emotional state is 'joyful'?" to see how the system generates specific support suggestions. This system enables more detailed support than before by accurately recognizing the user's emotional state and providing information and experiences that match it.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The server uses a generative AI model to collect data related to a specific target from the communication network. Inputs include URLs and query parameters obtained through RSS feeds on news sites and APIs on social media. Outputs are the collected raw text data and metadata. The server filters this data to remove duplication and noise, and organizes the data.

[0361] Step 2:

[0362] The server stores the organized information in storage. The input is the text data filtered in step 1. The output is a set of information stored in a searchable format. The server does this using a database management system and indexes it for easier access by users later.

[0363] Step 3:

[0364] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. It receives the user's biometric information and behavioral logs as input. The output is emotion data based on this information. The device performs real-time processing and applies machine learning algorithms to estimate the emotional state.

[0365] Step 4:

[0366] The device sends estimated emotion data to the server. The input is the emotion data obtained in step 3. The output is the emotion data and its associated metadata. The server analyzes this data using an emotion engine to recognize the user's accurate emotional state.

[0367] Step 5:

[0368] The server references the recognized emotional state and the user's past operation history and interests, and creates support suggestions through a generative AI model. The inputs are emotional data, past operation history, and interest information. The output is personalized support suggestions. The server processes these using the generative AI model to generate specific content tailored to the user's needs.

[0369] Step 6:

[0370] The terminal presents the user with support suggestions received from the server. The input is the support suggestions generated in step 5. The output is the content presented through the user's display or notification system. The terminal displays these suggestions in a visually clear manner so that the user can easily view and act upon them.

[0371] (Application Example 2)

[0372] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0373] In modern content distribution services, it is difficult to grasp user satisfaction in real time and dynamically adjust content based on those emotions. Furthermore, methods for smoothly facilitating interaction among users are limited. These challenges should be addressed by systems that can appropriately analyze user emotions and provide the necessary support.

[0374] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0375] In this invention, the server includes means for collecting information related to a specific object from an information network, means for storing the collected information in an information storage means and making it accessible to the user via an interactive display device, and means for detecting the user's facial expressions and voice and analyzing their emotional state to dynamically adjust support suggestions in real time. This makes it possible to improve the user's viewing satisfaction, customize content according to individual emotions, and effectively promote interaction among users.

[0376] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate information and make optimal suggestions according to a specific purpose.

[0377] An "information network" is a foundation for acquiring distinctive data via communication networks such as the internet and utilizing it for various services.

[0378] "Information storage means" refers to a function that organizes the diverse information collected and keeps it in a state where it can be quickly searched and used as needed.

[0379] An "interactive display device" is a device that allows users to visually confirm information and interact with that information.

[0380] "Detecting the user's facial expressions and voice" is a process that uses cameras and microphones to extract features that allow for the inference of the user's emotional state.

[0381] "Analyzing emotional state" involves estimating the user's mental tendencies and mood from detected facial expressions and voice data, and clarifying that state.

[0382] "Dynamically adjusting support suggestions in real time" refers to the process of appropriately modifying suggestions to optimize them for the user's emotions at that moment, thereby providing more appropriate feedback.

[0383] The system for realizing this invention mainly consists of a generative artificial intelligence, an emotion engine, an information gathering module, a display device, and the like.

[0384] First, the server collects information related to a specific target through the information network. This includes the latest news, social media posts, and updates to the official website. This information is stored in an information storage system and can be viewed by the user via an interactive display device.

[0385] While a user is viewing content, the device continuously monitors the user's facial expressions and voice using its built-in camera and microphone. This data is sent to a server and analyzed by an emotion engine. This process utilizes emotion recognition frameworks such as OpenPose and machine learning frameworks such as TensorFlow.

[0386] Based on the analyzed emotional state, the server generates personalized support suggestions for the user. These suggestions might include recommendations for the next content to watch or the display of supplementary text information to explain what is being watched. This increases the user's viewing attention and provides a content experience that perfectly matches their emotions.

[0387] Furthermore, it's possible to identify other users who share similar emotions and provide online communication platforms to foster community engagement. For example, if a user is moved to tears by a touching film, a platform could be suggested where they can share that emotional experience through social media comments or reviews.

[0388] As a concrete example, a generative AI model may be used to generate prompt statements like the following.

[0389] "If a user is crying while watching an emotionally moving film, what kind of content suggestions or personalized messages should be presented next?"

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server collects the latest information related to a specific target from the internet via an information network. This process uses web scraping techniques to extract data from news sites and social media feeds. Given URLs or keywords as input, it crawls relevant information based on these and outputs it as text data. This allows important data to be stored in information storage systems.

[0393] Step 2:

[0394] The device monitors the user's facial expressions and voice using its built-in camera and microphone. The input consists of real-time video and audio data, which are sampled periodically. This data is analyzed by an emotion recognition algorithm to identify the user's emotional state. The output is tag information indicating the current emotional state. This analysis utilizes OpenPose and TensorFlow to obtain highly accurate emotional data in real time.

[0395] Step 3:

[0396] The server generates support suggestions based on analyzed sentiment data and utilizing previously collected information. Input includes the user's emotional state and viewing history. The server uses a generative AI model to generate prompts for suggesting optimal content and messages. Based on these prompts, it outputs recommended content and interactions within that content. This allows for a more personalized user experience.

[0397] Step 4:

[0398] The user's device receives suggestions from the server and presents them to the user through an interactive display device. The input is the suggestion information received from the server, which is then visually displayed on the screen. The output is designed to capture the user's attention and provide emotionally resonant encouragement and interactive content. This process allows the user to experience real-time changes in content and further promotes interaction on the platform.

[0399] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention provides a system that effectively supports fan support activities by utilizing generative artificial intelligence. The entire system consists of three main parts: information collection and analysis, information provision to users, and promotion of interaction among fans.

[0416] First, the server uses a web crawler to collect information related to a specific target on the internet. This process retrieves data from multiple sources, such as official websites, news sites, and social networking platforms. The collected information is stored in a structured database, ready for later searching and access.

[0417] Next, the user's device connects to the server to access the latest information. The device sends the user's past activity history and interest data to the server, which then generates support suggestions. The generating AI analyzes the user's data and suggests the most suitable ways to support, events to participate in, and recommended merchandise. These suggestions are displayed on the user dashboard, allowing the user to easily select and perform the suggested activities.

[0418] Furthermore, the server analyzes common hobbies and interests among users and connects like-minded individuals who support the same things. The terminal notifies users of invitations to online forums, group chats, and events, encouraging participation and stimulating interaction among fans.

[0419] For example, if a user is a fan of a popular artist, the server collects the latest concert and new song release information related to that artist and notifies the user's device. The user can then use this information to decide whether to attend the next concert, interact with other fans online, or purchase limited edition merchandise.

[0420] In this way, this system uses generative artificial intelligence to comprehensively support information management and suggestions for cheering activities, as well as communication among fans.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The server launches a web crawler and searches the internet for information based on defined keywords. It automatically retrieves web pages, news articles, social media posts, and other information related to the specified target.

[0424] Step 2:

[0425] The server analyzes the retrieved information and extracts the necessary metadata (e.g., title, URL, date, category). This data is then stored in a database in a structured format.

[0426] Step 3:

[0427] The user's device accesses the server to retrieve the latest information of interest to the user. The device displays this information on a dashboard, making it easy for the user to view.

[0428] Step 4:

[0429] The device sends the user's past behavioral history, browsing history, and information of interest to the server. This allows the server to accumulate data based on the user's interests.

[0430] Step 5:

[0431] The server applies machine learning algorithms to analyze accumulated user data, revealing trends based on user interests and preferences.

[0432] Step 6:

[0433] The server uses a generation AI to create suggestions for support methods and events tailored to the user. These suggestions are customized to the user's specific interests.

[0434] Step 7:

[0435] The terminal receives suggestions from the server and provides them to the user. Based on this information, the user can consider trying new ways of cheering or participating in related events.

[0436] Step 8:

[0437] The server analyzes users with shared interests and generates appropriate fan groups and community events.

[0438] Step 9:

[0439] The device notifies users of event and group chat invitations, encouraging them to participate. Users can then deepen their interactions with other fans.

[0440] (Example 1)

[0441] Next, we will describe Example 1. 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."

[0442] Many users find it difficult to quickly and efficiently gather information related to a specific subject and to engage in optimal support activities based on their individual preferences and interests. Furthermore, effectively facilitating interaction with other users who share the same hobbies is also a challenging task.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes means for collecting information from a public network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display device accessible to the user, and means for analyzing the user's past behavioral history and interests to generate personalized activity suggestions. This enables users to quickly access necessary information, receive support activity suggestions optimized to their individual preferences, and effectively interact with other users who share the same hobbies.

[0445] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and automatically generates information and suggestions tailored to specific purposes.

[0446] A "public network" is an information network accessible to the general public, and the internet is an example of this.

[0447] "Collecting information" refers to the process of searching for and obtaining data related to a specific subject, and includes technologies such as web crawling.

[0448] A "storage device" is a hardware device or software system used to store and save data.

[0449] A "display device" is an interface that allows users to visually confirm digital data, and includes computer monitors and smartphone screens.

[0450] "Behavioral history" refers to a record of a user's past activities and choices, and is data used for information analysis.

[0451] "Activity suggestions" refer to guidance on activities or events recommended to users based on their specific interests and concerns.

[0452] "Promoting interaction" refers to actions that support and activate communication and collaborative activities among multiple users.

[0453] In implementing this invention, the server collects information related to a specific target via a public network using generative artificial intelligence. Specifically, it obtains data from information sources using web crawling technology and analyzes the data using libraries such as Beautiful Soup or Scrapy. The server then structures the collected data and stores it in a memory device.

[0454] The device accesses the server and provides information to the user. The device sends this data to the server to analyze the user's behavioral history and interests. The server uses a generative AI model to generate activity suggestions tailored to the user. This process is expected to utilize generative AI technology (e.g., language models).

[0455] For example, one possible prompt that could be generated is, "Please generate the best message of encouragement for attending the next concert." Based on this prompt, the AI ​​provides customized suggestions to the user.

[0456] Furthermore, the server also plays a role in facilitating interaction among users. Based on data extracted from users with similar interests, it provides information about online forums and social events to the terminal. The terminal then notifies users of this information and encourages them to participate. For example, prompts such as "Please provide information on how to participate in the online fan meeting" can be entered into the AI.

[0457] In this way, the invention supports the flow of information and improves the user experience, comprehensively supporting support activities that meet the needs of users in specific areas of interest and activity.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The server launches a web crawler to collect information related to a specific target from the public network. Specifically, it utilizes libraries such as Beautiful Soup and Scrapy to extract text data from the information source. The input requires keywords related to the specific target, and the output is the extracted raw text data. This data is used in subsequent processing steps.

[0461] Step 2:

[0462] The server analyzes and structures the collected information. Using the collected text data as input, it employs Natural Language Processing (NLP) techniques to transform the data into a meaningful format. This process eliminates data duplication and prioritizes the organization of highly relevant information. The output is structured data stored in a database.

[0463] Step 3:

[0464] The device communicates with the server to transmit the user's behavioral history and interest information. Inputs include the user's past activity history data, browsing history, and purchase records. Outputs are prompts that are input into a generating AI model. These prompts are designed to generate activity suggestions based on the user's interests.

[0465] Step 4:

[0466] The server uses a generative AI model to create activity suggestions based on the user's interests. Taking prompts as input, the AI ​​model analyzes a large amount of data and generates personalized encouragement messages and event suggestions. The output provides specific suggestions, such as recommended events and information on interacting with other fans.

[0467] Step 5:

[0468] The terminal displays the received suggestions on the user dashboard and notifies the user. It uses the suggestions received from the server as input. As output, the information is visually organized, allowing the user to easily view and select suggestions. New event information and online activities are also notified to the user.

[0469] Step 6:

[0470] The server identifies other users with similar interests and suggests relevant interaction events and online forums to facilitate communication among users. It uses user interest information and suggested content as input. As output, participation guides are created and provided to users via their terminals. This enables active interaction among fans.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, 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."

[0473] The sheer volume of information on the internet creates a problem where users cannot efficiently obtain reliable information related to their areas of interest. Furthermore, the difficulty in accessing information about new related activities and products means that many users miss out on materials and events that interest them. Additionally, there is a lack of interaction among users with similar interests, limiting opportunities for sharing information and engaging in discussions.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for collecting information related to a specific area of ​​interest from the internet using generative artificial intelligence, means for selecting and notifying users of new content and product information according to their preferences, and means for providing activities and materials that facilitate interaction among a large number of users with common interests. As a result, users can receive the latest information related to their individually customized areas of interest and efficiently share information and engage in discussions with other users who share the same interests.

[0476] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to extract specific information from large amounts of data on the internet and generate suggestions and notifications based on the user's interests.

[0477] A "specific area of ​​interest" refers to a particular theme or genre that is the subject of information collection and analysis, and is an area related to the user's interests.

[0478] "Information gathering" is the process of obtaining data that meets specified criteria from various sources on the internet and preparing it for analysis.

[0479] "User preferences" refer to the tendencies and tastes that individual users show interest in, based on their past behavioral history and choices.

[0480] "New content or product information" refers to information about recently announced events, materials, or products that provide new insights into the user's areas of interest.

[0481] "Notifications" refer to information and alerts provided by a system to users, and are a means of delivering information to users quickly and efficiently.

[0482] "Interaction between users" refers to activities in which multiple users with common interests share information and engage in discussions with each other.

[0483] "Activities and materials" refer to events that users can participate in or use, as well as content for enjoyment and learning.

[0484] This invention realizes a system for providing information and facilitating communication using generative artificial intelligence. The server continuously collects information on specific areas of interest from the internet using generative artificial intelligence and stores this data in a database. This information includes news articles, information from official websites, and posts on social media.

[0485] The user's device connects to the server to access this information. The user's past activity history and interest data, transmitted from the device, are analyzed by the server's generated artificial intelligence. Based on this analysis, the server dynamically selects new content and product information tailored to the user's preferences and notifies the user. This process can utilize Python's Beautiful Soup or Scrapy for data collection and Pandas or Scikit-learn for data analysis. Furthermore, APIs can be built using Flask or Django to display the analysis results to the user.

[0486] For example, if a user is interested in a particular artist, the server will collect information on related new song releases and live events and immediately notify the user. This ensures that users never miss out on the latest information. Furthermore, users can discuss and share information with other users who share similar interests through online forums and chat functions. These features encourage more active interaction among users.

[0487] Examples of input prompts for a generative AI model are as follows:

[0488] "User interests: Providing information on new music releases and live events related to artists."

[0489] This system simultaneously achieves increased efficiency in information retrieval and facilitates communication among users.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The server collects data from various sources on the internet. Using Python's Beautiful Soup and Scrapy, it gathers information related to the target area of ​​interest from news sites and social media platforms. Input is a URL or query condition, and output is the retrieved content data. This data is structured and stored in a database.

[0493] Step 2:

[0494] The server analyzes the information stored in the database. Based on the user's past behavior history and interest data, a generative AI model filters and prioritizes the information. The input is the user's history data and collected data, and the output is a list of the information most relevant to the user. This process uses data analysis techniques such as Pandas and Scikit-learn.

[0495] Step 3:

[0496] The terminal connects to the server and receives the analyzed information. The server sends a list of prioritized content, which is then notified to the user via an application on the terminal. The input is the analysis results from the server, and the output is the information notification displayed in the user interface.

[0497] Step 4:

[0498] Users make decisions based on the information they receive. For example, if they are notified of a new song release, they might decide to listen to the artist's new song or attend a related event. This process involves making decisions based on the user's preferences.

[0499] Step 5:

[0500] The server analyzes users with shared interests and provides opportunities for interaction. It facilitates interaction by sending notifications for user-to-user chat functions and forum participation. The input is user interest data, and the output is invitation notifications for interaction events and group chats.

[0501] An example of prompt input to the generating AI model is, "User interests: Provide information on new song releases and live events related to the artist." This allows users to continuously receive the latest information on content they are interested in.

[0502] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0503] This invention provides a system that more effectively supports users' cheering activities by fusing generative artificial intelligence and an emotion engine. This system has the functions of information collection and management, generation of individual cheering suggestions, dynamic adjustment based on recognition of user emotions, and promotion of interaction among fans.

[0504] First, the server uses artificial intelligence to collect the latest information related to a specific target from the internet. This information is diverse, including news articles, social media posts, and updates to official websites. The collected data is stored in a database and can be accessed from the user's device. The device then visually displays the information that the user is interested in on its interface.

[0505] Next, the user's device uses its built-in camera and sensors to collect emotional data from the user's facial expressions, voice, and actions. This information is sent to a server, where an emotion engine recognizes the user's emotions at that moment. The server then considers this emotional data and generates optimal support suggestions based on the user's past activity history and interests.

[0506] Support suggestions are designed to boost user motivation and create a sense of comfort. For example, if a user needs encouragement, the emotion engine suggests uplifting content, music, and participatory events. Conversely, if a user is seeking relaxation, it recommends calming content and community activities.

[0507] Furthermore, the server groups users with similar emotional tendencies and provides content and events that connect fans with shared interests. The device notifies users of these group activities and events, facilitating emotion-based communication.

[0508] For example, if a user feels sad about the activities of their favorite artist, that emotion will be recognized, and the server will provide comforting messages and opportunities to connect with other fans of the same artist. In this way, by incorporating an emotion engine, the system realizes a more advanced form of support for fan activities that is more attentive to the individual needs of each user.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The server uses generative artificial intelligence to collect information related to specific targets from the internet. For example, it periodically scans and retrieves news articles, updates to official websites, and social media posts.

[0512] Step 2:

[0513] The server analyzes the acquired information, adds metadata, and stores it in a database. This makes the information easily searchable and accessible later.

[0514] Step 3:

[0515] The device accesses the database and filters relevant information based on the user's past interests, displaying it on the dashboard.

[0516] Step 4:

[0517] The user's device uses sensors such as cameras and microphones to collect emotional data from the user's facial expressions, voice tone, and operation patterns.

[0518] Step 5:

[0519] The device sends emotional data to the server in real time. The server analyzes this data using an emotion engine to identify the user's current emotions.

[0520] Step 6:

[0521] The server considers the user's emotional state and past activity history to generate personalized support suggestions. For example, if a user is feeling down, it will suggest encouraging messages and related events.

[0522] Step 7:

[0523] The device receives suggestions from the server and displays them in a format suitable for the user. Based on this, the user can then engage in new support activities.

[0524] Step 8:

[0525] The server identifies and groups users with similar feelings and interests, and sets up common events and chat rooms for them.

[0526] Step 9:

[0527] The device notifies the user of opportunities to interact with other users who share similar feelings. Users can use this to deepen their bonds with other fans.

[0528] (Example 2)

[0529] Next, we will describe Example 2. 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."

[0530] In a world overflowing with information on topics of interest to users, providing appropriate support suggestions based on each user's emotions and interests is challenging. Finding suitable spaces and content for interaction is also not easy. Furthermore, it is necessary to immediately grasp the user's situation and respond flexibly accordingly. This necessitates promoting optimal information sharing and interaction for each individual user.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes means for collecting data related to a specific target from a communication network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display method accessible to the user, and means for detecting the user's facial expressions, voice, and operating status and recognizing their emotional state. This enables dynamic and appropriate support suggestions and information provision according to the emotional state of each user, and facilitates information sharing and interaction that is in line with the user's interests and emotions.

[0533] "Generative artificial intelligence" is a technology that generates new information and suggestions in computer systems by analyzing and learning from large amounts of data.

[0534] A "communication network" is a wide-area information transmission infrastructure for sending and receiving data via the internet and other networks.

[0535] A "storage device" is a hardware component that can store data and information for a long period of time.

[0536] A "user" is an individual or organization that operates an information technology system and receives its services.

[0537] "Display method" refers to the means and techniques for visually presenting data and information to users.

[0538] "Operation history" refers to a record of a series of actions or events performed by a user on the system.

[0539] "Emotional state" refers to data that indicates the user's emotional response and psychological state.

[0540] A "support suggestion" is a set of recommended actions and content provided based on the user's condition and interests.

[0541] "Activities that promote connectivity" refer to events and programs designed to increase opportunities for multiple users to exchange information and cooperate with each other.

[0542] This invention is a system that utilizes generative artificial intelligence (AI) to provide information and facilitate interaction tailored to the user's emotional state.

[0543] The server uses a generative AI model to collect data related to specific targets from communication networks, which are a wide-area information transmission infrastructure. Specific data collection is carried out through RSS feeds from news sites, APIs from social media platforms, and information retrieval from official websites. The collected data is stored in a memory device, where duplicate data and noise are filtered out, and the necessary information is organized.

[0544] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. This allows it to determine the user's emotional state in real time and transmit it to a server. An emotion engine then analyzes this data to recognize the user's emotional state.

[0545] Based on the recognized emotional state, the server references the user's past activity history and interests, and creates personalized support suggestions through a generative AI model. These suggestions are then provided on the user's device through a display method accessible to the user. For example, if the user is feeling stressed, relaxation-friendly music, nature videos, and information on relaxing events they can participate in may be recommended.

[0546] Furthermore, the server recognizes multiple users with similar emotional tendencies and provides features such as interaction events and group chats to facilitate connections between users with common interests. This allows users to have a more fulfilling experience through active interaction.

[0547] As an example of a prompt, you can input "What content should be provided if the user's emotional state is 'joyful'?" to see how the system generates specific support suggestions. This system enables more detailed support than before by accurately recognizing the user's emotional state and providing information and experiences that match it.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The server uses a generative AI model to collect data related to a specific target from the communication network. Inputs include URLs and query parameters obtained through RSS feeds on news sites and APIs on social media. Outputs are the collected raw text data and metadata. The server filters this data to remove duplication and noise, and organizes the data.

[0551] Step 2:

[0552] The server stores the organized information in storage. The input is the text data filtered in step 1. The output is a set of information stored in a searchable format. The server does this using a database management system and indexes it for easier access by users later.

[0553] Step 3:

[0554] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. It receives the user's biometric information and behavioral logs as input. The output is emotion data based on this information. The device performs real-time processing and applies machine learning algorithms to estimate the emotional state.

[0555] Step 4:

[0556] The device sends estimated emotion data to the server. The input is the emotion data obtained in step 3. The output is the emotion data and its associated metadata. The server analyzes this data using an emotion engine to recognize the user's accurate emotional state.

[0557] Step 5:

[0558] The server references the recognized emotional state and the user's past operation history and interests, and creates support suggestions through a generative AI model. The inputs are emotional data, past operation history, and interest information. The output is personalized support suggestions. The server processes these using the generative AI model to generate specific content tailored to the user's needs.

[0559] Step 6:

[0560] The terminal presents the user with support suggestions received from the server. The input is the support suggestions generated in step 5. The output is the content presented through the user's display or notification system. The terminal displays these suggestions in a visually clear manner so that the user can easily view and act upon them.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, 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."

[0563] In modern content distribution services, it is difficult to grasp user satisfaction in real time and dynamically adjust content based on those emotions. Furthermore, methods for smoothly facilitating interaction among users are limited. These challenges should be addressed by systems that can appropriately analyze user emotions and provide the necessary support.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for collecting information related to a specific object from an information network, means for storing the collected information in an information storage means and making it accessible to the user via an interactive display device, and means for detecting the user's facial expressions and voice and analyzing their emotional state to dynamically adjust support suggestions in real time. This makes it possible to improve the user's viewing satisfaction, customize content according to individual emotions, and effectively promote interaction among users.

[0566] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate information and make optimal suggestions according to a specific purpose.

[0567] An "information network" is a foundation for acquiring distinctive data via communication networks such as the internet and utilizing it for various services.

[0568] "Information storage means" refers to a function that organizes the diverse information collected and keeps it in a state where it can be quickly searched and used as needed.

[0569] An "interactive display device" is a device that allows users to visually confirm information and interact with that information.

[0570] "Detecting the user's facial expressions and voice" is a process that uses cameras and microphones to extract features that allow for the inference of the user's emotional state.

[0571] "Analyzing emotional state" involves estimating the user's mental tendencies and mood from detected facial expressions and voice data, and clarifying that state.

[0572] "Dynamically adjusting support suggestions in real time" refers to the process of appropriately modifying suggestions to optimize them for the user's emotions at that moment, thereby providing more appropriate feedback.

[0573] The system for realizing this invention mainly consists of a generative artificial intelligence, an emotion engine, an information gathering module, a display device, and the like.

[0574] First, the server collects information related to a specific target through the information network. This includes the latest news, social media posts, and updates to the official website. This information is stored in an information storage system and can be viewed by the user via an interactive display device.

[0575] While a user is viewing content, the device continuously monitors the user's facial expressions and voice using its built-in camera and microphone. This data is sent to a server and analyzed by an emotion engine. This process utilizes emotion recognition frameworks such as OpenPose and machine learning frameworks such as TensorFlow.

[0576] Based on the analyzed emotional state, the server generates personalized support suggestions for the user. These suggestions might include recommendations for the next content to watch or the display of supplementary text information to explain what is being watched. This increases the user's viewing attention and provides a content experience that perfectly matches their emotions.

[0577] Furthermore, it's possible to identify other users who share similar emotions and provide online communication platforms to foster community engagement. For example, if a user is moved to tears by a touching film, a platform could be suggested where they can share that emotional experience through social media comments or reviews.

[0578] As a concrete example, a generative AI model may be used to generate prompt statements like the following.

[0579] "If a user is crying while watching an emotionally moving film, what kind of content suggestions or personalized messages should be presented next?"

[0580] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0581] Step 1:

[0582] The server collects the latest information related to a specific target from the internet via an information network. This process uses web scraping techniques to extract data from news sites and social media feeds. Given URLs or keywords as input, it crawls relevant information based on these and outputs it as text data. This allows important data to be stored in information storage systems.

[0583] Step 2:

[0584] The device monitors the user's facial expressions and voice using its built-in camera and microphone. The input consists of real-time video and audio data, which are sampled periodically. This data is analyzed by an emotion recognition algorithm to identify the user's emotional state. The output is tag information indicating the current emotional state. This analysis utilizes OpenPose and TensorFlow to obtain highly accurate emotional data in real time.

[0585] Step 3:

[0586] The server generates support suggestions based on analyzed sentiment data and utilizing previously collected information. Input includes the user's emotional state and viewing history. The server uses a generative AI model to generate prompts for suggesting optimal content and messages. Based on these prompts, it outputs recommended content and interactions within that content. This allows for a more personalized user experience.

[0587] Step 4:

[0588] The user's device receives suggestions from the server and presents them to the user through an interactive display device. The input is the suggestion information received from the server, which is then visually displayed on the screen. The output is designed to capture the user's attention and provide emotionally resonant encouragement and interactive content. This process allows the user to experience real-time changes in content and further promotes interaction on the platform.

[0589] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0590] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0591] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] As shown in Figure 7, the 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.

[0595] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0596] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0597] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0598] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0599] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0600] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0601] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0602] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0603] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0604] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0605] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] This invention provides a system that effectively supports fan support activities by utilizing generative artificial intelligence. The entire system consists of three main parts: information collection and analysis, information provision to users, and promotion of interaction among fans.

[0607] First, the server uses a web crawler to collect information related to a specific target on the internet. This process retrieves data from multiple sources, such as official websites, news sites, and social networking platforms. The collected information is stored in a structured database, ready for later searching and access.

[0608] Next, the user's device connects to the server to access the latest information. The device sends the user's past activity history and interest data to the server, which then generates support suggestions. The generating AI analyzes the user's data and suggests the most suitable ways to support, events to participate in, and recommended merchandise. These suggestions are displayed on the user dashboard, allowing the user to easily select and perform the suggested activities.

[0609] Furthermore, the server analyzes common hobbies and interests among users and connects like-minded individuals who support the same things. The terminal notifies users of invitations to online forums, group chats, and events, encouraging participation and stimulating interaction among fans.

[0610] For example, if a user is a fan of a popular artist, the server collects the latest concert and new song release information related to that artist and notifies the user's device. The user can then use this information to decide whether to attend the next concert, interact with other fans online, or purchase limited edition merchandise.

[0611] In this way, this system uses generative artificial intelligence to comprehensively support information management and suggestions for cheering activities, as well as communication among fans.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The server launches a web crawler and searches the internet for information based on defined keywords. It automatically retrieves web pages, news articles, social media posts, and other information related to the specified target.

[0615] Step 2:

[0616] The server analyzes the retrieved information and extracts the necessary metadata (e.g., title, URL, date, category). This data is then stored in a database in a structured format.

[0617] Step 3:

[0618] The user's device accesses the server to retrieve the latest information of interest to the user. The device displays this information on a dashboard, making it easy for the user to view.

[0619] Step 4:

[0620] The device sends the user's past behavioral history, browsing history, and information of interest to the server. This allows the server to accumulate data based on the user's interests.

[0621] Step 5:

[0622] The server applies machine learning algorithms to analyze accumulated user data, revealing trends based on user interests and preferences.

[0623] Step 6:

[0624] The server uses a generation AI to create suggestions for support methods and events tailored to the user. These suggestions are customized to the user's specific interests.

[0625] Step 7:

[0626] The terminal receives suggestions from the server and provides them to the user. Based on this information, the user can consider trying new ways of cheering or participating in related events.

[0627] Step 8:

[0628] The server analyzes users with shared interests and generates appropriate fan groups and community events.

[0629] Step 9:

[0630] The device notifies users of event and group chat invitations, encouraging them to participate. Users can then deepen their interactions with other fans.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Many users find it difficult to quickly and efficiently gather information related to a specific subject and to engage in optimal support activities based on their individual preferences and interests. Furthermore, effectively facilitating interaction with other users who share the same hobbies is also a challenging task.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for collecting information from a public network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display device accessible to the user, and means for analyzing the user's past behavioral history and interests to generate personalized activity suggestions. This enables users to quickly access necessary information, receive support activity suggestions optimized to their individual preferences, and effectively interact with other users who share the same hobbies.

[0636] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data and automatically generates information and suggestions tailored to specific purposes.

[0637] A "public network" is an information network accessible to the general public, and the internet is an example of this.

[0638] "Collecting information" refers to the process of searching for and obtaining data related to a specific subject, and includes technologies such as web crawling.

[0639] A "storage device" is a hardware device or software system used to store and save data.

[0640] A "display device" is an interface that allows users to visually confirm digital data, and includes computer monitors and smartphone screens.

[0641] "Behavioral history" refers to a record of a user's past activities and choices, and is data used for information analysis.

[0642] "Activity suggestions" refer to guidance on activities or events recommended to users based on their specific interests and concerns.

[0643] "Promoting interaction" refers to actions that support and activate communication and collaborative activities among multiple users.

[0644] In implementing this invention, the server collects information related to a specific target via a public network using generative artificial intelligence. Specifically, it obtains data from information sources using web crawling technology and analyzes the data using libraries such as Beautiful Soup or Scrapy. The server then structures the collected data and stores it in a memory device.

[0645] The device accesses the server and provides information to the user. The device sends this data to the server to analyze the user's behavioral history and interests. The server uses a generative AI model to generate activity suggestions tailored to the user. This process is expected to utilize generative AI technology (e.g., language models).

[0646] For example, one possible prompt that could be generated is, "Please generate the best message of encouragement for attending the next concert." Based on this prompt, the AI ​​provides customized suggestions to the user.

[0647] Furthermore, the server also plays a role in facilitating interaction among users. Based on data extracted from users with similar interests, it provides information about online forums and social events to the terminal. The terminal then notifies users of this information and encourages them to participate. For example, prompts such as "Please provide information on how to participate in the online fan meeting" can be entered into the AI.

[0648] In this way, the invention supports the flow of information and improves the user experience, comprehensively supporting support activities that meet the needs of users in specific areas of interest and activity.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server launches a web crawler to collect information related to a specific target from the public network. Specifically, it utilizes libraries such as Beautiful Soup and Scrapy to extract text data from the information source. The input requires keywords related to the specific target, and the output is the extracted raw text data. This data is used in subsequent processing steps.

[0652] Step 2:

[0653] The server analyzes and structures the collected information. Using the collected text data as input, it employs Natural Language Processing (NLP) techniques to transform the data into a meaningful format. This process eliminates data duplication and prioritizes the organization of highly relevant information. The output is structured data stored in a database.

[0654] Step 3:

[0655] The device communicates with the server to transmit the user's behavioral history and interest information. Inputs include the user's past activity history data, browsing history, and purchase records. Outputs are prompts that are input into a generating AI model. These prompts are designed to generate activity suggestions based on the user's interests.

[0656] Step 4:

[0657] The server uses a generative AI model to create activity suggestions based on the user's interests. Taking prompts as input, the AI ​​model analyzes a large amount of data and generates personalized encouragement messages and event suggestions. The output provides specific suggestions, such as recommended events and information on interacting with other fans.

[0658] Step 5:

[0659] The terminal displays the received suggestions on the user dashboard and notifies the user. It uses the suggestions received from the server as input. As output, the information is visually organized, allowing the user to easily view and select suggestions. New event information and online activities are also notified to the user.

[0660] Step 6:

[0661] The server identifies other users with similar interests and suggests relevant interaction events and online forums to facilitate communication among users. It uses user interest information and suggested content as input. As output, participation guides are created and provided to users via their terminals. This enables active interaction among fans.

[0662] (Application Example 1)

[0663] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] The sheer volume of information on the internet creates a problem where users cannot efficiently obtain reliable information related to their areas of interest. Furthermore, the difficulty in accessing information about new related activities and products means that many users miss out on materials and events that interest them. Additionally, there is a lack of interaction among users with similar interests, limiting opportunities for sharing information and engaging in discussions.

[0665] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0666] In this invention, the server includes means for collecting information related to a specific area of ​​interest from the internet using generative artificial intelligence, means for selecting and notifying users of new content and product information according to their preferences, and means for providing activities and materials that facilitate interaction among a large number of users with common interests. As a result, users can receive the latest information related to their individually customized areas of interest and efficiently share information and engage in discussions with other users who share the same interests.

[0667] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to extract specific information from large amounts of data on the internet and generate suggestions and notifications based on the user's interests.

[0668] A "specific area of ​​interest" refers to a particular theme or genre that is the subject of information collection and analysis, and is an area related to the user's interests.

[0669] "Information gathering" is the process of obtaining data that meets specified criteria from various sources on the internet and preparing it for analysis.

[0670] "User preferences" refer to the tendencies and tastes that individual users show interest in, based on their past behavioral history and choices.

[0671] "New content or product information" refers to information about recently announced events, materials, or products that provide new insights into the user's areas of interest.

[0672] "Notifications" refer to information and alerts provided by a system to users, and are a means of delivering information to users quickly and efficiently.

[0673] "Interaction between users" refers to activities in which multiple users with common interests share information and engage in discussions with each other.

[0674] "Activities and materials" refer to events that users can participate in or use, as well as content for enjoyment and learning.

[0675] This invention realizes a system for providing information and facilitating communication using generative artificial intelligence. The server continuously collects information on specific areas of interest from the internet using generative artificial intelligence and stores this data in a database. This information includes news articles, information from official websites, and posts on social media.

[0676] The user's device connects to the server to access this information. The user's past activity history and interest data, transmitted from the device, are analyzed by the server's generated artificial intelligence. Based on this analysis, the server dynamically selects new content and product information tailored to the user's preferences and notifies the user. This process can utilize Python's Beautiful Soup or Scrapy for data collection and Pandas or Scikit-learn for data analysis. Furthermore, APIs can be built using Flask or Django to display the analysis results to the user.

[0677] For example, if a user is interested in a particular artist, the server will collect information on related new song releases and live events and immediately notify the user. This ensures that users never miss out on the latest information. Furthermore, users can discuss and share information with other users who share similar interests through online forums and chat functions. These features encourage more active interaction among users.

[0678] Examples of input prompts for a generative AI model are as follows:

[0679] "User interests: Providing information on new music releases and live events related to artists."

[0680] This system simultaneously achieves increased efficiency in information retrieval and facilitates communication among users.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The server collects data from various sources on the internet. Using Python's Beautiful Soup and Scrapy, it gathers information related to the target area of ​​interest from news sites and social media platforms. Input is a URL or query condition, and output is the retrieved content data. This data is structured and stored in a database.

[0684] Step 2:

[0685] The server analyzes the information stored in the database. Based on the user's past behavior history and interest data, a generative AI model filters and prioritizes the information. The input is the user's history data and collected data, and the output is a list of the information most relevant to the user. This process uses data analysis techniques such as Pandas and Scikit-learn.

[0686] Step 3:

[0687] The terminal connects to the server and receives the analyzed information. The server sends a list of prioritized content, which is then notified to the user via an application on the terminal. The input is the analysis results from the server, and the output is the information notification displayed in the user interface.

[0688] Step 4:

[0689] Users make decisions based on the information they receive. For example, if they are notified of a new song release, they might decide to listen to the artist's new song or attend a related event. This process involves making decisions based on the user's preferences.

[0690] Step 5:

[0691] The server analyzes users with shared interests and provides opportunities for interaction. It facilitates interaction by sending notifications for user-to-user chat functions and forum participation. The input is user interest data, and the output is invitation notifications for interaction events and group chats.

[0692] An example of prompt input to the generating AI model is, "User interests: Provide information on new song releases and live events related to the artist." This allows users to continuously receive the latest information on content they are interested in.

[0693] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0694] This invention provides a system that more effectively supports users' cheering activities by fusing generative artificial intelligence and an emotion engine. This system has the functions of information collection and management, generation of individual cheering suggestions, dynamic adjustment based on recognition of user emotions, and promotion of interaction among fans.

[0695] First, the server uses artificial intelligence to collect the latest information related to a specific target from the internet. This information is diverse, including news articles, social media posts, and updates to official websites. The collected data is stored in a database and can be accessed from the user's device. The device then visually displays the information that the user is interested in on its interface.

[0696] Next, the user's device uses its built-in camera and sensors to collect emotional data from the user's facial expressions, voice, and actions. This information is sent to a server, where an emotion engine recognizes the user's emotions at that moment. The server then considers this emotional data and generates optimal support suggestions based on the user's past activity history and interests.

[0697] Support suggestions are designed to boost user motivation and create a sense of comfort. For example, if a user needs encouragement, the emotion engine suggests uplifting content, music, and participatory events. Conversely, if a user is seeking relaxation, it recommends calming content and community activities.

[0698] Furthermore, the server groups users with similar emotional tendencies and provides content and events that connect fans with shared interests. The device notifies users of these group activities and events, facilitating emotion-based communication.

[0699] For example, if a user feels sad about the activities of their favorite artist, that emotion will be recognized, and the server will provide comforting messages and opportunities to connect with other fans of the same artist. In this way, by incorporating an emotion engine, the system realizes a more advanced form of support for fan activities that is more attentive to the individual needs of each user.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] The server uses generative artificial intelligence to collect information related to specific targets from the internet. For example, it periodically scans and retrieves news articles, updates to official websites, and social media posts.

[0703] Step 2:

[0704] The server analyzes the acquired information, adds metadata, and stores it in a database. This makes the information easily searchable and accessible later.

[0705] Step 3:

[0706] The device accesses the database and filters relevant information based on the user's past interests, displaying it on the dashboard.

[0707] Step 4:

[0708] The user's device uses sensors such as cameras and microphones to collect emotional data from the user's facial expressions, voice tone, and operation patterns.

[0709] Step 5:

[0710] The device sends emotional data to the server in real time. The server analyzes this data using an emotion engine to identify the user's current emotions.

[0711] Step 6:

[0712] The server considers the user's emotional state and past activity history to generate personalized support suggestions. For example, if a user is feeling down, it will suggest encouraging messages and related events.

[0713] Step 7:

[0714] The device receives suggestions from the server and displays them in a format suitable for the user. Based on this, the user can then engage in new support activities.

[0715] Step 8:

[0716] The server identifies and groups users with similar feelings and interests, and sets up common events and chat rooms for them.

[0717] Step 9:

[0718] The device notifies the user of opportunities to interact with other users who share similar feelings. Users can use this to deepen their bonds with other fans.

[0719] (Example 2)

[0720] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0721] In a world overflowing with information on topics of interest to users, providing appropriate support suggestions based on each user's emotions and interests is challenging. Finding suitable spaces and content for interaction is also not easy. Furthermore, it is necessary to immediately grasp the user's situation and respond flexibly accordingly. This necessitates promoting optimal information sharing and interaction for each individual user.

[0722] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0723] In this invention, the server includes means for collecting data related to a specific target from a communication network using generative artificial intelligence, means for storing the collected data in a storage device and providing a display method accessible to the user, and means for detecting the user's facial expressions, voice, and operating status and recognizing their emotional state. This enables dynamic and appropriate support suggestions and information provision according to the emotional state of each user, and facilitates information sharing and interaction that is in line with the user's interests and emotions.

[0724] "Generative artificial intelligence" is a technology that generates new information and suggestions in computer systems by analyzing and learning from large amounts of data.

[0725] A "communication network" is a wide-area information transmission infrastructure for sending and receiving data via the internet and other networks.

[0726] A "storage device" is a hardware component that can store data and information for a long period of time.

[0727] A "user" is an individual or organization that operates an information technology system and receives its services.

[0728] "Display method" refers to the means and techniques for visually presenting data and information to users.

[0729] "Operation history" refers to a record of a series of actions or events performed by a user on the system.

[0730] "Emotional state" refers to data that indicates the user's emotional response and psychological state.

[0731] A "support suggestion" is a set of recommended actions and content provided based on the user's condition and interests.

[0732] "Activities that promote connectivity" refer to events and programs designed to increase opportunities for multiple users to exchange information and cooperate with each other.

[0733] This invention is a system that utilizes generative artificial intelligence (AI) to provide information and facilitate interaction tailored to the user's emotional state.

[0734] The server uses a generative AI model to collect data related to specific targets from communication networks, which are a wide-area information transmission infrastructure. Specific data collection is carried out through RSS feeds from news sites, APIs from social media platforms, and information retrieval from official websites. The collected data is stored in a memory device, where duplicate data and noise are filtered out, and the necessary information is organized.

[0735] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. This allows it to determine the user's emotional state in real time and transmit it to a server. An emotion engine then analyzes this data to recognize the user's emotional state.

[0736] Based on the recognized emotional state, the server references the user's past activity history and interests, and creates personalized support suggestions through a generative AI model. These suggestions are then provided on the user's device through a display method accessible to the user. For example, if the user is feeling stressed, relaxation-friendly music, nature videos, and information on relaxing events they can participate in may be recommended.

[0737] Furthermore, the server recognizes multiple users with similar emotional tendencies and provides features such as interaction events and group chats to facilitate connections between users with common interests. This allows users to have a more fulfilling experience through active interaction.

[0738] As an example of a prompt, you can input "What content should be provided if the user's emotional state is 'joyful'?" to see how the system generates specific support suggestions. This system enables more detailed support than before by accurately recognizing the user's emotional state and providing information and experiences that match it.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] The server uses a generative AI model to collect data related to a specific target from the communication network. Inputs include URLs and query parameters obtained through RSS feeds on news sites and APIs on social media. Outputs are the collected raw text data and metadata. The server filters this data to remove duplication and noise, and organizes the data.

[0742] Step 2:

[0743] The server stores the organized information in storage. The input is the text data filtered in step 1. The output is a set of information stored in a searchable format. The server does this using a database management system and indexes it for easier access by users later.

[0744] Step 3:

[0745] The device acquires the user's facial expressions, voice, and operation information through various sensors, cameras, and microphones. It receives the user's biometric information and behavioral logs as input. The output is emotion data based on this information. The device performs real-time processing and applies machine learning algorithms to estimate the emotional state.

[0746] Step 4:

[0747] The device sends estimated emotion data to the server. The input is the emotion data obtained in step 3. The output is the emotion data and its associated metadata. The server analyzes this data using an emotion engine to recognize the user's accurate emotional state.

[0748] Step 5:

[0749] The server references the recognized emotional state and the user's past operation history and interests, and creates support suggestions through a generative AI model. The inputs are emotional data, past operation history, and interest information. The output is personalized support suggestions. The server processes these using the generative AI model to generate specific content tailored to the user's needs.

[0750] Step 6:

[0751] The terminal presents the user with support suggestions received from the server. The input is the support suggestions generated in step 5. The output is the content presented through the user's display or notification system. The terminal displays these suggestions in a visually clear manner so that the user can easily view and act upon them.

[0752] (Application Example 2)

[0753] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] In modern content distribution services, it is difficult to grasp user satisfaction in real time and dynamically adjust content based on those emotions. Furthermore, methods for smoothly facilitating interaction among users are limited. These challenges should be addressed by systems that can appropriately analyze user emotions and provide the necessary support.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0756] In this invention, the server includes means for collecting information related to a specific object from an information network, means for storing the collected information in an information storage means and making it accessible to the user via an interactive display device, and means for detecting the user's facial expressions and voice and analyzing their emotional state to dynamically adjust support suggestions in real time. This makes it possible to improve the user's viewing satisfaction, customize content according to individual emotions, and effectively promote interaction among users.

[0757] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate information and make optimal suggestions according to a specific purpose.

[0758] An "information network" is a foundation for acquiring distinctive data via communication networks such as the internet and utilizing it for various services.

[0759] "Information storage means" refers to a function that organizes the diverse information collected and keeps it in a state where it can be quickly searched and used as needed.

[0760] An "interactive display device" is a device that allows users to visually confirm information and interact with that information.

[0761] "Detecting the user's facial expressions and voice" is a process that uses cameras and microphones to extract features that allow for the inference of the user's emotional state.

[0762] "Analyzing emotional state" involves estimating the user's mental tendencies and mood from detected facial expressions and voice data, and clarifying that state.

[0763] "Dynamically adjusting support suggestions in real time" refers to the process of appropriately modifying suggestions to optimize them for the user's emotions at that moment, thereby providing more appropriate feedback.

[0764] The system for realizing this invention mainly consists of a generative artificial intelligence, an emotion engine, an information gathering module, a display device, and the like.

[0765] First, the server collects information related to a specific target through the information network. This includes the latest news, social media posts, and updates to the official website. This information is stored in an information storage system and can be viewed by the user via an interactive display device.

[0766] While a user is viewing content, the device continuously monitors the user's facial expressions and voice using its built-in camera and microphone. This data is sent to a server and analyzed by an emotion engine. This process utilizes emotion recognition frameworks such as OpenPose and machine learning frameworks such as TensorFlow.

[0767] Based on the analyzed emotional state, the server generates personalized support suggestions for the user. These suggestions might include recommendations for the next content to watch or the display of supplementary text information to explain what is being watched. This increases the user's viewing attention and provides a content experience that perfectly matches their emotions.

[0768] Furthermore, it's possible to identify other users who share similar emotions and provide online communication platforms to foster community engagement. For example, if a user is moved to tears by a touching film, a platform could be suggested where they can share that emotional experience through social media comments or reviews.

[0769] As a concrete example, a generative AI model may be used to generate prompt statements like the following.

[0770] "If a user is crying while watching an emotionally moving film, what kind of content suggestions or personalized messages should be presented next?"

[0771] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0772] Step 1:

[0773] The server collects the latest information related to a specific target from the internet via an information network. This process uses web scraping techniques to extract data from news sites and social media feeds. Given URLs or keywords as input, it crawls relevant information based on these and outputs it as text data. This allows important data to be stored in information storage systems.

[0774] Step 2:

[0775] The device monitors the user's facial expressions and voice using its built-in camera and microphone. The input consists of real-time video and audio data, which are sampled periodically. This data is analyzed by an emotion recognition algorithm to identify the user's emotional state. The output is tag information indicating the current emotional state. This analysis utilizes OpenPose and TensorFlow to obtain highly accurate emotional data in real time.

[0776] Step 3:

[0777] The server generates support suggestions based on analyzed sentiment data and utilizing previously collected information. Input includes the user's emotional state and viewing history. The server uses a generative AI model to generate prompts for suggesting optimal content and messages. Based on these prompts, it outputs recommended content and interactions within that content. This allows for a more personalized user experience.

[0778] Step 4:

[0779] The user's device receives suggestions from the server and presents them to the user through an interactive display device. The input is the suggestion information received from the server, which is then visually displayed on the screen. The output is designed to capture the user's attention and provide emotionally resonant encouragement and interactive content. This process allows the user to experience real-time changes in content and further promotes interaction on the platform.

[0780] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0781] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0782] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0783] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0784] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0785] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0786] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0787] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0788] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0789] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0790] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0791] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0792] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0793] 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.

[0794] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0795] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0796] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0797] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0798] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0799] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0800] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0801] The following is further disclosed regarding the embodiments described above.

[0802] (Claim 1)

[0803] A means of collecting information related to a specific target from the internet using generative artificial intelligence,

[0804] A means of storing the collected information in a database and providing an interface that users can access,

[0805] A means of generating personalized support suggestions by analyzing the user's past activity history and interests,

[0806] A means of providing events and content that promote interaction among multiple users with common interests,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, which selects and notifies users of information that is likely to be of particular interest to them from the collected information.

[0810] (Claim 3)

[0811] The system according to claim 1, which dynamically adjusts the content of the support suggestions generated based on the user's support target and interests.

[0812] "Example 1"

[0813] (Claim 1)

[0814] A means of collecting information from a public network using generative artificial intelligence,

[0815] A means for storing collected data in a storage device and providing a display device that can be accessed by the user,

[0816] A means for generating personalized activity suggestions by analyzing the user's past behavioral history and interests,

[0817] A means of providing events and information to facilitate interaction among multiple users with common interests,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which selects and notifies users of information that is likely to be of particular interest to them from the collected data.

[0821] (Claim 3)

[0822] The system according to claim 1, which dynamically adjusts the content of activity suggestions generated based on the user's interests and concerns.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] A means of collecting information related to a specific area of ​​interest from the internet using generative artificial intelligence,

[0826] A means of storing the collected information in a database and providing an interface that users can access,

[0827] A means for generating personalized support suggestions by analyzing the user's past behavioral history and interests,

[0828] Means of providing activities and materials that promote interaction among a large number of users with common interests,

[0829] A means of selecting and notifying users of new content and product information according to their preferences,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which dynamically adjusts the provision of new activities or materials related to the user's areas of interest based on the collected information and analysis results.

[0833] (Claim 3)

[0834] The system according to claim 1, which enables users to interact with each other through online forums or conversation functions and to stimulate discussions about new information or materials.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] A means of collecting data related to a specific target from a communication network using generative artificial intelligence,

[0838] A means for storing collected data in a storage device and providing a display method that is accessible to users,

[0839] A means for generating personalized support suggestions by analyzing the user's past operation history and interests,

[0840] Activities and means of providing information that facilitate connections between multiple users with common interests,

[0841] A means for detecting the user's facial expressions, voice, and operation status to recognize their emotional state,

[0842] A means of dynamically adjusting support suggestions based on recognized emotional states,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, which selects and notifies users of information that is predicted to be of particular interest to them from the collected data.

[0846] (Claim 3)

[0847] The system according to claim 1, which dynamically adjusts the content of support suggestions generated based on recognized emotional states and interests.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A means of collecting information related to a specific target from an information network using generative artificial intelligence,

[0851] A means for storing collected information in an information storage means and providing an interactive display device that can be accessed by the user,

[0852] A means of generating personalized support suggestions by analyzing the user's past activity history and interests,

[0853] Activities and means of providing information that promote interaction among multiple users with common interests,

[0854] A method for dynamically adjusting support suggestions in real time by detecting the user's facial expressions and voice and analyzing their emotional state,

[0855] A means to enable real-time changes to content in order to increase viewer satisfaction,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, which selects and notifies users of information that is likely to be of particular interest to them from among the collected information.

[0859] (Claim 3)

[0860] The system according to claim 1, which dynamically adjusts the content of the support suggestions generated based on the user's support target and interests. [Explanation of symbols]

[0861] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information related to a specific target from the internet using generative artificial intelligence, A means of storing the collected information in a database and providing an interface that users can access, A means of generating personalized support suggestions by analyzing the user's past activity history and interests, A means of providing events and content that promote interaction among multiple users with common interests, A system that includes this.

2. The system according to claim 1, which selects and notifies users of information that is likely to be of particular interest to them from among the collected information.

3. The system according to claim 1, which dynamically adjusts the content of the support suggestions generated based on the user's support target and interests.

Images