system

Abstract

We provide the system. [Solution] A means for acquiring user data and saving that data as a profile, A means for analyzing the aforementioned profile data and matching it with other users who have similar interests or experiences, The means of selecting candidate groups or events and providing them to users, A means of notifying users about the group or event being offered and accepting their participation, Means to support communication among participants in the aforementioned group or event, A means of collecting feedback after the completion of a participating activity and providing support for the next activity, A system that includes this.

Description

Technical Field

[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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] Elderly people approaching retirement are required to build new social connections and prevent isolation. However, previous community activities have not fully considered individual interests and experiences, making it difficult to achieve appropriate matching. Also, it has been difficult to provide a unified promotion of participation in new events and groups and a feedback function after activities. The problem to be solved by this invention is to eliminate such conventional problems and enable matching and participation support according to individual needs.

Means for Solving the Problems

[0005] The present invention includes means for acquiring user data and storing it as a profile, means for analyzing the profile data and matching it with other users who have similar interests or experiences, and means for selecting and providing candidate groups or events to the user. This system enables appropriate matching based on individual profiles. Furthermore, it includes means for notifying users about the provided groups or events and accepting their participation, means for supporting communication among participants, and means for collecting feedback after the participation activity is completed and supporting future activities, thereby promoting consistent communication and the building of continuous relationships.

[0006] "User data" refers to information about an individual user, including profile information, interests, experiences, and work history.

[0007] A "profile" is a collection of information that summarizes a user's basic information, interests, and experiences, and forms the basis of user matching.

[0008] "Analysis" is the process of identifying commonalities and similarities among collected user data and optimizing their combinations.

[0009] "Matching" is the process of connecting users who share common interests and experiences.

[0010] A "group" is a collection of multiple users who share common hobbies or goals and engage in activities together.

[0011] An "event" refers to a gathering or activity held for a specific purpose that users can participate in.

[0012] "Notifications" refer to communication methods used to inform users of information via their devices, and include push notifications.

[0013] "Participation" refers to a user becoming involved in a selected group or event and taking part in its activities.

[0014] "Communication" refers to dialogue and interaction among participants that deepen mutual understanding through the exchange of information.

[0015] "Feedback" is the process of collecting and providing information based on the results and impressions of participating in an activity, in order to improve future activities. [Brief explanation of the drawing]

[0016] [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]It 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

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

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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.

[0020] 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.

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

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a community matching system that helps older adults build new social connections before and after retirement. The system provides functions for data acquisition, analysis, matching, notification, participation, communication, and feedback, helping users effectively participate in events and groups that interest them.

[0038] Program processing

[0039] First, users create their profile using their device. This profile includes information such as their name, age, hobbies, interests, and past work experience. This information is sent to the server and stored in a database.

[0040] The server uses an AI algorithm to analyze the received profile data. The AI ​​generates interest tags based on the user's hobbies and interests, and identifies other users with similar characteristics. This analysis results in a list of potential matching candidates.

[0041] Next, the server searches the event database for events and groups that might be of interest to the user, based on the user's tags. Information about the found events and groups is provided to the user's device via push notifications, etc., and the user reviews this information.

[0042] When a user selects an event or group they wish to participate in, their registration information is sent to the server via their device, and the user can access the chatbot function to begin interacting with other participants in advance.

[0043] After the event ends, the server automatically sends a survey to users requesting feedback and collects user responses. This feedback is analyzed by AI and used to make suggestions and improvements for future events and activities.

[0044] For example, if a 59-year-old user enters that they are interested in "hiking" and "local volunteering," the system will recommend local hiking clubs and regularly held volunteer events that match their interests. Through this process, users can form new connections and experience enriching activities.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] Users create a profile using their device. Specifically, they enter their name, age, hobbies, interests, and past work history in an application on their device, and then press the "Save" button to send the entered information to the server.

[0048] Step 2:

[0049] The server receives profile data submitted by the user and stores it in a database. Next, an AI algorithm is used to analyze the profile data and generate and classify interest tags based on the user's interests and experiences.

[0050] Step 3:

[0051] The server uses the generated interest tags to calculate similarity with other users, identify users with common characteristics, and list them as matching candidates. Methods such as cosine similarity and Euclidean distance are used in this process.

[0052] Step 4:

[0053] The server searches the event database to identify events and groups that match the user's interest tags. It extracts information about the relevant events and groups and ranks them according to their recommendation level.

[0054] Step 5:

[0055] The server sends recommended group and event information to the device. The device then pushes the received information to the user, allowing them to view details within the app.

[0056] Step 6:

[0057] Users review recommended information, select groups and events that interest them, and register to participate from their device. The user's selection is sent to the server, and registration is complete.

[0058] Step 7:

[0059] Users can use their devices to communicate with other participants in advance by utilizing the chatbot function of the groups and events they have registered for.

[0060] Step 8:

[0061] After the event ends, the server sends a feedback form to users and collects their evaluation of the activity through a survey. The collected feedback is analyzed by AI and stored to help improve future events and increase user satisfaction.

[0062] (Example 1)

[0063] 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."

[0064] In modern society, there is a problem in that it is difficult for older adults to build new social connections after retirement. In particular, finding appropriate events or groups based on individual hobbies and interests is not easy due to the sheer volume of information and the difficulty in judging suitability. As a result, older adults may feel isolated, which can lead to a decline in their quality of life (QOL).

[0065] 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.

[0066] In this invention, the server includes means for acquiring user information from a terminal and storing the information as a profile in a database; means for analyzing the profile information on the server, generating user interest tags using artificial intelligence technology, and identifying other users with similar characteristics; and means for the server to search the information database for candidate activities and groups based on the interest tags and recommend them to the user. This makes it possible for users to easily find events and groups that suit their hobbies and interests and to effectively build new social connections.

[0067] "User information" refers to data necessary to form a personal profile provided by a user, and includes a variety of information such as name, age, hobbies, interests, and past work history.

[0068] A "terminal" refers to an electronic device used by a user for inputting, receiving, or communicating information, and includes devices such as smartphones and computers.

[0069] A "server" refers to a computer system that processes data received from users and provides various services to users through a network.

[0070] A "profile" is a dataset constructed by integrating individual user information, and it refers to a collection of information including the user's hobbies, interests, and past behavior.

[0071] "Artificial intelligence technology" refers to technologies that use computer programs to learn and reason like humans, supporting decision-making, and includes techniques such as machine learning and natural language processing.

[0072] "Interest tags" are keywords and topics extracted from a user's profile information, and refer to labels that concisely indicate a user's hobbies and interests.

[0073] "Activities and groups" refer to events and groups that users can participate in, and include gatherings aimed at social interaction or the pursuit of hobbies.

[0074] An "information database" is a system in which various types of information are organized and stored, and it refers to a collection of data used to search for activities and groups that match the user's interests.

[0075] "Push notification" refers to a communication method that provides information from a server to a user's terminal in real time, and is a mechanism that enables immediate notification of event information.

[0076] "Feedback" refers to input information in which users evaluate a service or activity and provide their impressions and suggestions for improvement. This is important data that contributes to improving the quality of the service.

[0077] This community matching system was developed to help seniors build new social connections after retirement. The system consists of three main elements: users, terminals, and servers.

[0078] Users first create their profile using a device. This profile includes personal information such as the user's name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0079] The server applies an AI algorithm based on stored profile information to generate interest tags that reflect the user's interests and hobbies. This AI algorithm uses natural language processing techniques to extract relevant information from the user's input data and uses a generative AI model to identify similarities with other users.

[0080] Next, the server searches the information database based on interest tags and selects activities and groups that the user is likely to be interested in. As a result, the activities recommended to the user will match their individual preferences.

[0081] Recommended activities and groups are provided to the user's device via push notifications. Through these notifications, users can view details about the activities and select events or groups they wish to participate in.

[0082] Furthermore, when users participate in an activity they have selected, the server provides a chatbot function to support pre-activity interaction with other participants through their device. This promotes active communication among users.

[0083] After the activity ends, the server asks users for feedback in the form of a survey. This feedback is analyzed by AI and used to suggest future events and improve activities.

[0084] For example, if a 59-year-old user enters "hiking" and "local volunteering" as interests on their device, the server will recommend local hiking clubs and regularly held volunteer events that match those interests. Through this process, users can form new connections and experience enriching activities.

[0085] An example of a prompt message would be: "Create a system that recommends events based on the user's interests. For example, explain how to provide relevant event information to someone who is interested in hiking."

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] Users open a profile creation screen on their device and enter their name, age, hobbies, interests, and past work history. This information is entered into the device, confirming the user's basic information. The device then sends this information to the server, where it is stored in the database as the user's profile. The entered information is crucial data used for subsequent analysis and matching.

[0089] Step 2:

[0090] The server prepares the received user profile information for analysis. Specifically, it passes the stored data to an AI algorithm. The server uses natural language processing techniques and generative AI models to generate interest tags that represent the user's hobbies and interests. In this process, keywords are extracted from the text data and listed as tags. As output, a set of tags indicating the user's interests is generated.

[0091] Step 3:

[0092] The server searches its database for suitable activities and groups based on the generated interest tags. The input to the search is the user's tag information, and the output is a list of recommended events and groups based on this information. The server applies an AI-powered matching algorithm to select candidates that match the user's interests. Specifically, it extracts the names and details of events that match the user's tags.

[0093] Step 4:

[0094] The server sends detailed information about selected events or groups to the device. The device presents this information to the user as a push notification. This notification includes information such as the event name, content, date and time, and location. The user reviews the notification and selects events that interest them. The output includes the notification content that caught the user's attention.

[0095] Step 5:

[0096] Users register to participate in selected events or groups using their devices. The devices send their participation request information to the server. The server verifies the registration based on this information and approves the user's participation. Furthermore, the server provides users with access to a chatbot function, allowing them to communicate with other participants in advance. This initiates interaction among users.

[0097] Step 6:

[0098] Once the event ends, the server sends a survey to users requesting feedback. Users answer the survey on their devices and send it back to the server. The server uses AI to analyze the collected feedback and uses it to suggest future events and improve activities. This helps to improve the quality of the service in the future. The output provided is the analyzed feedback data.

[0099] (Application Example 1)

[0100] 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."

[0101] It is difficult for older adults to build new social connections after retirement, raising concerns about loneliness and social isolation. While building new relationships through activities based on interests and hobbies is effective, finding appropriate events and groups is not easy. Furthermore, limited interaction among participants makes pre-event communication difficult. Additionally, there is a lack of mechanisms to utilize post-event feedback for future improvements.

[0102] 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.

[0103] In this invention, the server includes means for acquiring user data and storing it as a profile, means for analyzing the profile data and matching it with other users who have similar interests or experiences, and means for selecting candidate groups or events and providing them to the user. This makes it possible for elderly people to easily participate in appropriate social activities and to deepen their interactions with other participants in advance. Furthermore, feedback after the activity can be used to improve future activities.

[0104] "User data" refers to personal information, interests, hobbies, and past work history provided by individual users.

[0105] A "profile" is a collection of information that describes the characteristics of each individual, generated based on user data.

[0106] "Analysis" is the process of analyzing user data using algorithms and programs to derive specific insights and trends.

[0107] "Matching" is the process of connecting and linking multiple users who have similar interests or experiences.

[0108] A "group or event" refers to a collection or activity in which multiple people participate and act with a common interest or purpose.

[0109] "Notification" refers to a means or process of communication used to inform users of new information or events.

[0110] "Participation" refers to the act of joining a group or event selected by a user and engaging in activities with other users.

[0111] "Communication" is an activity in which participants exchange information and messages to deepen their understanding and relationships.

[0112] "Feedback" is the collection of opinions and reactions about a particular experience or event, and this data is used later to help make improvements.

[0113] A "real-time chat function" is a communication method that allows participants to exchange messages simultaneously, and is a technology that enables rapid communication.

[0114] This invention is a community matching system designed to help elderly people build new social connections. The system begins by sending user data entered by the user via a terminal to a server to build a profile. This profile reflects the user's basic information and interests and is stored in a database. Based on this profile data, the server uses an AI algorithm to identify other users with similar interests and experiences. The analytical techniques used here include natural language processing.

[0115] Furthermore, the server searches the event database for events and groups that match the user's interests and notifies them via push notifications on their device. These notifications also include access to a real-time chat function, allowing users to communicate with other participants in advance. This functionality is enabled by a real-time communication platform such as Firebase.

[0116] After participating in an event, the server collects feedback such as applause and comments, and uses AI to recommend and improve future events. This ensures that users can always experience high-quality activities.

[0117] For example, if a user in their 70s is interested in "home gardening" and "exploring local history," the system will recommend home gardening workshops and historical tours held in the local community. The user can then use their smartphone from home to meet and connect with new friends who share similar interests.

[0118] An example of a prompt to pass to a generative AI model is, "What kind of community events should be suggested to seniors seeking new social activities after retirement?" Through such prompts, the system can make more accurate recommendations.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] Users use their devices to enter their profile information. This data includes information such as name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0122] Step 2:

[0123] The server uses AI algorithms to analyze data based on profile information stored in the database. This process utilizes natural language processing to analyze the user's hobbies and interests and generates tags related to their areas of interest. As a result of the analysis, other users with similar interests are identified and listed as matching candidates.

[0124] Step 3:

[0125] The server searches the event database and group information based on the generated interest tags and selects events and groups suitable for the user. At this time, it evaluates the degree of matching between the interest tags and the event information to select the most suitable candidates. The selected information, including details, is sent to the device as a push notification.

[0126] Step 4:

[0127] Users view the details of the notified event or group on their device and decide whether to participate. Once they choose to participate, their decision is sent to the server via their device, completing the registration process.

[0128] Step 5:

[0129] The server utilizes real-time chat functionality to support communication among event and group participants. Users can exchange messages with other participants in real time, sharing information and building relationships.

[0130] Step 6:

[0131] After an event or group activity concludes, the server automatically sends out a survey requesting feedback from users. The user feedback is collected by the server and analyzed by AI. This analysis is then used to improve and recommend future events.

[0132] 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.

[0133] This invention is a community matching system that incorporates an emotion engine into the process from user data acquisition to feedback, in order to help elderly people before and after retirement build new social connections and prevent isolation. The emotion engine analyzes the user's emotions and incorporates them into profile data, with the aim of matching them with the most suitable groups and events based on the results.

[0134] Program processing

[0135] First, the user creates a basic profile through the device. Once the basic information is entered, the device activates voice input and facial recognition camera, instructing the emotion engine to analyze the user's emotions. The analyzed emotion information is added to the profile and sent to the server.

[0136] The server stores the received profile data in a database and begins analysis using AI algorithms and an emotion engine. It generates tags containing emotion data from the profile and lists other users suitable for similar emotional states and activities as matching candidates.

[0137] Next, the server searches the event database and selects events and groups that match the user's emotional state. Based on this emotional information, it recommends cultural workshops if relaxation is desired, and sports events if energy is needed.

[0138] Next, the server sends filtered event information to the device. The device then presents the event information to the user via push notification, allowing them to view specific activity details within the app. The user selects an event they are interested in and registers to participate.

[0139] During the event, real-time sentiment analysis of the user is performed through the device, and communication support functions with other participants are utilized to support conversations that are appropriate to the user's emotional state.

[0140] After the event ends, the server requests feedback from users. It sends a feedback form that combines data from the emotion engine, and uses the feedback to recommend future events and improve the service.

[0141] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion engine will recommend a relaxing book club and suggest participation. In this way, the system supports the building of comfortable and meaningful relationships by providing activities tailored to the user's emotional state.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user creates a profile through the device. Specifically, they enter their name, age, hobbies, and interests into the application's form and click the "Next" button. After the profile is created, the device activates the voice input function and camera and prompts the user to express their emotions.

[0145] Step 2:

[0146] The device transmits data acquired through voice input and the camera to the emotion engine. The emotion engine analyzes voice tone and facial expression data to identify the user's primary emotional state. For example, it can determine states such as stress, joy, and excitement.

[0147] Step 3:

[0148] Once the user's emotional information is identified, the device sends the results to the server, where they are stored in a database along with the profile data.

[0149] Step 4:

[0150] The server begins analyzing the stored profile data. An AI algorithm generates tags based on the user's interests, experiences, and emotional data, and identifies other users with similar tags. This then lists potential matches based on emotions and interests.

[0151] Step 5:

[0152] The server consults the event database to select the event or group best suited to the user's emotional state. During selection, it considers the user's emotional state, choosing a mindfulness workshop if relaxation is needed, or a hiking event for those feeling more active.

[0153] Step 6:

[0154] The server sends information about selected groups and events to the terminal. Based on the received information, the terminal displays the details of the event or group on the user's screen and notifies them using the notification function.

[0155] Step 7:

[0156] Users check notifications on their devices, select events or groups that interest them, and register to participate. Once registration is complete, the device sends the participation information to the server and displays a confirmation message to the user.

[0157] Step 8:

[0158] While users are participating, the device performs real-time sentiment analysis and provides feedback and advice to stimulate communication among users.

[0159] Step 9:

[0160] After the activity ends, the server sends a feedback form to the user. It generates feedback using data from the emotion engine and collects responses from the user. The server analyzes this feedback and uses it to guide future activities and improve the service.

[0161] (Example 2)

[0162] 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".

[0163] Modern seniors and retirees often face feelings of isolation and weakened social connections, making it difficult for them to find new social links. Addressing these challenges and providing appropriate activities and groups tailored to individual emotional states is crucial for improving their social well-being.

[0164] 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.

[0165] In this invention, the server includes means for acquiring user information and storing said information as a data structure, means for analyzing the data structure using an emotion analysis device and matching it with other participants who have similar emotional states or behaviors, and means for selecting candidate groups or activities and providing them to the user. This makes it possible to provide optimal social interaction opportunities that match the emotional state of each individual user.

[0166] "User information" refers to basic data about individual participants that is entered into the system, including name, age, hobbies, and emotional state.

[0167] A "data structure" refers to a collection of information used to store and manage user information and analysis results, and is a format that allows computers to process data efficiently.

[0168] An "emotion analysis device" refers to a processing system or algorithm used to analyze voice and facial expression data acquired from a user and determine their emotional state.

[0169] "Matching" refers to the process of selecting appropriate other participants and activities based on the user's emotional state, interests, and activity patterns.

[0170] A "group or activity" refers to an event or group of participants proposed by the system for the purpose of social interaction, providing a space for users to build new relationships through their participation.

[0171] "Electronic notification function" refers to a technology that instantly delivers information via a terminal, and is a means of quickly and efficiently informing users of the information being provided.

[0172] A "generative AI model" is a model that has an algorithm that learns from large amounts of data to recognize and analyze complex patterns, and is particularly used for analysis based on user emotions and interests.

[0173] This invention is a system designed to help elderly people and retirees build new social connections and prevent isolation. The system aims to suggest appropriate social interaction activities based on the user's emotional state.

[0174] The user creates a basic profile using the device. The device displays screens for entering information such as name, age, hobbies, and areas of interest. Once the user's basic information is entered, the device activates voice input and facial recognition camera functions.

[0175] The emotion analysis device collects emotional data from the user's voice and facial expressions via the terminal and uses a generative AI model to analyze complex emotional patterns. The analysis results are added to the user's profile and sent to the server.

[0176] The server stores the received profile data in a database and performs analysis using AI algorithms and sentiment analysis devices. Based on the sentiment data included in the profile, it selects other participants with similar emotional states and activities as matching candidates. It also searches the event database to identify groups and activities that match the user's emotional state.

[0177] The selected information is sent to the terminal, and the user immediately receives event information via electronic notification. Users register to participate in activities that interest them, and during the activity, the interaction is optimized through real-time sentiment analysis. After the activity ends, the server collects feedback from the user and uses it to make suggestions for the next event.

[0178] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion analysis device will recommend and suggest participation in a relaxing book club. This helps in building comfortable and meaningful relationships.

[0179] An example of a prompt message might be, "Design an AI service that suggests the optimal event based on the analysis results."

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user operates the device to enter basic profile information. This information includes name, age, hobbies, and areas of interest. This data is temporarily stored as a data structure within the device.

[0183] Step 2:

[0184] The device activates its voice input function and facial recognition camera. The user's voice and facial data are input to the emotion analysis device. By collecting this data, the user's emotions are detected in real time, and a generative AI model analyzes this data to output the emotional state.

[0185] Step 3:

[0186] The device adds the analyzed emotional state to the user's profile and sends the profile data to the server. The input here is the result of the emotional analysis, and the output is the transmission of the profile data to the server.

[0187] Step 4:

[0188] The server stores the received profile data in a database. This data includes the user's basic information and emotional state. Based on the stored data, an AI algorithm and an emotion analysis device are used to analyze the user's state and output other participants with similar emotional states or activity patterns.

[0189] Step 5:

[0190] The server searches the event database to select groups and activities that match the user's emotional state. The server receives emotional data as input, outputs events that match relaxation or vitality, and sends the selection results to the terminal.

[0191] Step 6:

[0192] The terminal receives event information sent from the server and presents it to the user using an electronic notification function. The user reviews the event information and selects activities of interest. The input is the event information, and the output is the user's selections.

[0193] Step 7:

[0194] While the user participates in the event, the device analyzes the user's emotions in real time and optimizes the interaction based on the results. In this process, emotion analysis data is used as input, and the optimized interaction method is output.

[0195] Step 8:

[0196] After the event ends, the server requests feedback. Users input their feedback via their terminals and send it to the server. This feedback is used to make suggestions for future events and improve the service. The input is feedback information, and the output is adjustments to the next recommended event.

[0197] (Application Example 2)

[0198] 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".

[0199] Retired seniors often experience a weakening of social connections and feelings of isolation. In this situation, there is a need to support them in effectively participating in new communities and building comfortable and meaningful relationships through activities that align with their emotional state. However, conventional systems struggle to match individuals with appropriate emotional states, making it difficult to provide personalized experiences that meet user needs.

[0200] 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.

[0201] In this invention, the server includes means for acquiring user data and storing said data as a profile, means for analyzing the profile data and emotional information and matching it with other users who have similar interests or experiences and emotional states, and means for selecting candidate groups or events and providing them to the user. This makes it possible to encourage optimal community participation while taking into account the user's emotional state and to form social connections that meet the user's individual needs.

[0202] "User data" refers to detailed information about a user, including basic information, activity history, and emotional information.

[0203] A "profile" is a dataset created based on user data that shows the characteristics and attributes of a user.

[0204] "Emotional information" refers to data obtained by analyzing the user's emotions and psychological state.

[0205] "Matching" is the process of connecting users who have similar interests, experiences, or emotional states.

[0206] A "group or event" refers to a collective activity or event intended for user participation.

[0207] "Real-time sentiment analysis" refers to a technology that instantly analyzes a user's emotional state during a group activity.

[0208] "Feedback" is the process of collecting users' feelings and opinions about an activity.

[0209] "Push notifications" refer to a method of sending information directly to a user's device to notify them.

[0210] "Communication support" refers to a function designed to facilitate smooth communication between users.

[0211] The system for implementing this invention is built using a terminal such as a smartphone and a cloud-based server. The user first installs a dedicated application on their smartphone and creates a basic profile. During this process, emotional information can be input using a camera equipped with voice input and facial recognition capabilities. The terminal utilizes software libraries such as Google® Cloud Speech-to-Text API and OpenCV for voice recognition and image processing.

[0212] The acquired user data is sent to the server as profile data and sentiment information. The server stores this data in a database such as MySQL® and analyzes it using a generative AI model with TENSORFLOW®. Based on the analysis results, other users, groups, and events that match the user's interests and sentiment state are matched.

[0213] The server sends event information tailored to the user's needs to their device via push notifications. During the event, the server performs real-time sentiment analysis and supports smooth conversations through its communication support function.

[0214] For example, if the system analyzes that a user is experiencing stress while creating their profile, the emotion engine will recommend an online book club as a relaxing activity and notify the user of the participation invitation. In this way, the system provides users with experiences tailored to their individual needs and helps them build social connections.

[0215] An example of a prompt message would be: "This user recently left their job and is experiencing stress. What kind of relaxation event should we suggest?"

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user launches a smartphone application and creates a basic profile. Inputs include the user's name, age, interests, and past experiences. This information is then saved as profile data.

[0219] Step 2:

[0220] To collect user emotion data, the device's camera and microphone are activated. The user's facial expressions and voice tone are captured as input, and this data is analyzed by an emotion analysis engine as output. The analysis results are added to the profile as emotion information.

[0221] Step 3:

[0222] The device sends profile data and emotion information to the server. The server receives this data as input and stores it in its database. As output, a detailed user profile is completed.

[0223] Step 4:

[0224] The server launches a generative AI model using TensorFlow to analyze profile data. As input, it generates tags based on interests, experiences, and emotional states, using stored user data. As output, it lists suitable candidates for other users and events for the user.

[0225] Step 5:

[0226] The server selects the group or event that best matches the user's sentiment information. It uses the generated tags as input and searches the event database. The output determines the most suitable group or event information for the user.

[0227] Step 6:

[0228] The server sends the selected event information to the terminal and sends a push notification. The selected event information is used as input, and the event notification is displayed on the user's terminal as output.

[0229] Step 7:

[0230] Users participate in events and perform real-time sentiment analysis through their devices. Facial and voice data collected during participation are used as input and processed by a sentiment analysis engine. Communication support information is provided as output.

[0231] Step 8:

[0232] After the event ends, the server requests feedback from users. The input consists of collected opinions and feedback from users, which are then used as output for recommending future activities and improving the service.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a community matching system that helps older adults build new social connections before and after retirement. The system provides functions for data acquisition, analysis, matching, notification, participation, communication, and feedback, helping users effectively participate in events and groups that interest them.

[0250] Program processing

[0251] First, users create their profile using their device. This profile includes information such as their name, age, hobbies, interests, and past work experience. This information is sent to the server and stored in a database.

[0252] The server uses an AI algorithm to analyze the received profile data. The AI ​​generates interest tags based on the user's hobbies and interests, and identifies other users with similar characteristics. This analysis results in a list of potential matching candidates.

[0253] Next, the server searches the event database for events and groups that might be of interest to the user, based on the user's tags. Information about the found events and groups is provided to the user's device via push notifications, etc., and the user reviews this information.

[0254] When a user selects an event or group they wish to participate in, their registration information is sent to the server via their device, and the user can access the chatbot function to begin interacting with other participants in advance.

[0255] After the event ends, the server automatically sends a survey to users requesting feedback and collects user responses. This feedback is analyzed by AI and used to make suggestions and improvements for future events and activities.

[0256] For example, if a 59-year-old user enters that they are interested in "hiking" and "local volunteering," the system will recommend local hiking clubs and regularly held volunteer events that match their interests. Through this process, users can form new connections and experience enriching activities.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] Users create a profile using their device. Specifically, they enter their name, age, hobbies, interests, and past work history in an application on their device, and then press the "Save" button to send the entered information to the server.

[0260] Step 2:

[0261] The server receives profile data submitted by the user and stores it in a database. Next, an AI algorithm is used to analyze the profile data and generate and classify interest tags based on the user's interests and experiences.

[0262] Step 3:

[0263] The server uses the generated interest tags to calculate similarity with other users, identify users with common characteristics, and list them as matching candidates. Methods such as cosine similarity and Euclidean distance are used in this process.

[0264] Step 4:

[0265] The server searches the event database to identify events and groups that match the user's interest tags. It extracts information about the relevant events and groups and ranks them according to their recommendation level.

[0266] Step 5:

[0267] The server sends recommended group and event information to the device. The device then pushes the received information to the user, allowing them to view details within the app.

[0268] Step 6:

[0269] Users review recommended information, select groups and events that interest them, and register to participate from their device. The user's selection is sent to the server, and registration is complete.

[0270] Step 7:

[0271] Users can use their devices to communicate with other participants in advance by utilizing the chatbot function of the groups and events they have registered for.

[0272] Step 8:

[0273] After the event ends, the server sends a feedback form to users and collects their evaluation of the activity through a survey. The collected feedback is analyzed by AI and stored to help improve future events and increase user satisfaction.

[0274] (Example 1)

[0275] 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."

[0276] In modern society, there is a problem in that it is difficult for older adults to build new social connections after retirement. In particular, finding appropriate events or groups based on individual hobbies and interests is not easy due to the sheer volume of information and the difficulty in judging suitability. As a result, older adults may feel isolated, which can lead to a decline in their quality of life (QOL).

[0277] 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.

[0278] In this invention, the server includes means for acquiring user information from a terminal and storing the information as a profile in a database, means for analyzing the profile information in the server, generating interest tags of the user using artificial intelligence technology, and identifying other users with similar characteristics, and means for the server to search for candidate activities and groups from an information database based on the interest tags and recommend them to the user. Thereby, it becomes possible for the user to easily find events and groups suitable for their hobbies and interests and effectively build new social connections.

[0279] "User information" refers to the data necessary to form an individual profile provided by the user, and refers to various information including name, age, hobbies, activities of interest, past work history, etc.

[0280] "Terminal" refers to an electronic device used by the user for information input, reception, and communication, and refers to devices including smartphones and computers.

[0281] "Server" refers to a computer system for processing data received from a user and providing various services to the user through a network.

[0282] "Profile" refers to a dataset constructed by integrating the individual information of the user, and refers to a summary of information including the user's hobbies, interests, past behaviors, etc.

[0283] "Artificial intelligence technology" refers to technology that learns and makes inferences like a human through a computer program and supports decision-making, and includes techniques such as machine learning and natural language processing.

[0284] "Interest tag" refers to keywords or topics extracted from the user's profile information, and refers to a label for concisely indicating the user's hobbies and interests.

[0285] "Activities and groups" refers to events and groups that users can participate in, and refers to gatherings aimed at social interaction and pursuit of hobbies.

[0286] "Information database" is a system in which various information is organized and stored, and refers to a group of data used to search for activities and groups that match the user's interests.

[0287] "Push notification" is a communication means that provides information from the server to the user terminal in real time, and refers to a mechanism that enables immediate notification of event information.

[0288] "Feedback" is input information in which users evaluate services and activities and provide their feelings and points for improvement, and refers to important data that contributes to improving the quality of services.

[0289] This community matching system was developed to support elderly people in building new social connections after retirement. The system consists of three main elements: users, terminals, and servers.

[0290] Users first create their profiles using a terminal. Individual information such as the user's name, age, hobbies, activities of interest, and past work history is entered into the profile. This input information is sent by the terminal to the server and stored as a profile in the database.

[0291] The server applies an AI algorithm based on the stored profile information to generate interest tags that reflect the user's interests and hobbies. This AI algorithm uses natural language processing technology to extract relevant information from the user's input data and uses a generated AI model to identify similarities with other users.

[0292] Next, the server searches the information database based on interest tags and selects activities and groups that the user is likely to be interested in. As a result, the activities recommended to the user will match their individual preferences.

[0293] Recommended activities and groups are provided to the user's device via push notifications. Through these notifications, users can view details about the activities and select events or groups they wish to participate in.

[0294] Furthermore, when users participate in an activity they have selected, the server provides a chatbot function to support pre-activity interaction with other participants through their device. This promotes active communication among users.

[0295] After the activity ends, the server asks users for feedback in the form of a survey. This feedback is analyzed by AI and used to suggest future events and improve activities.

[0296] For example, if a 59-year-old user enters "hiking" and "local volunteering" as interests on their device, the server will recommend local hiking clubs and regularly held volunteer events that match those interests. Through this process, users can form new connections and experience enriching activities.

[0297] An example of a prompt message would be: "Create a system that recommends events based on the user's interests. For example, explain how to provide relevant event information to someone who is interested in hiking."

[0298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0299] Step 1:

[0300] The user opens the profile creation screen on the terminal and enters their name, age, hobbies, activities of interest, and past work history. Once this information is input into the terminal, the user's basic information is determined. The terminal then sends this information to the server, where it is saved as the user's profile in the database. The input information is important data for later analysis and matching.

[0301] Step 2:

[0302] The server prepares the received user profile information for analysis. Specifically, it passes the saved data to an AI algorithm. The server uses natural language processing technology and a generative AI model to generate interest tags representing the user's hobbies and interests. In this process, keywords are extracted from the text data and listed as tags. As output, a series of tags indicating the user's interests are generated.

[0303] Step 3:

[0304] Based on the generated interest tags, the server searches the database for matching activities and groups. The input to the search is the user's tag information, and the output is a list of recommended events and groups based on this. The server applies an AI-based matching algorithm to select candidates that match the user's interests. Specifically, it extracts the names and detailed information of events that match the user's tags.

[0305] Step 4:

[0306] The server sends the detailed information of the selected events and groups to the terminal. The terminal presents this information to the user as a push notification. This notification includes information such as the name, content, date and time of the event, and location. The user checks this notification and selects the events they are interested in. The output includes the notification content that catches the user's interest.

[0307] Step 5:

[0308] Users register to participate in selected events or groups using their devices. The devices send their participation request information to the server. The server verifies the registration based on this information and approves the user's participation. Furthermore, the server provides users with access to a chatbot function, allowing them to communicate with other participants in advance. This initiates interaction among users.

[0309] Step 6:

[0310] Once the event ends, the server sends a survey to users requesting feedback. Users answer the survey on their devices and send it back to the server. The server uses AI to analyze the collected feedback and uses it to suggest future events and improve activities. This helps to improve the quality of the service in the future. The output provided is the analyzed feedback data.

[0311] (Application Example 1)

[0312] 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 glasses 214 will be referred to as the "terminal."

[0313] It is difficult for older adults to build new social connections after retirement, raising concerns about loneliness and social isolation. While building new relationships through activities based on interests and hobbies is effective, finding appropriate events and groups is not easy. Furthermore, limited interaction among participants makes pre-event communication difficult. Additionally, there is a lack of mechanisms to utilize post-event feedback for future improvements.

[0314] 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.

[0315] In this invention, the server includes means for acquiring user data and storing it as a profile, means for analyzing the profile data and matching it with other users who have similar interests or experiences, and means for selecting candidate groups or events and providing them to the user. This makes it possible for elderly people to easily participate in appropriate social activities and to deepen their interactions with other participants in advance. Furthermore, feedback after the activity can be used to improve future activities.

[0316] "User data" refers to personal information, interests, hobbies, and past work history provided by individual users.

[0317] A "profile" is a collection of information that describes the characteristics of each individual, generated based on user data.

[0318] "Analysis" is the process of analyzing user data using algorithms and programs to derive specific insights and trends.

[0319] "Matching" is the process of connecting and linking multiple users who have similar interests or experiences.

[0320] A "group or event" refers to a collection or activity in which multiple people participate and act with a common interest or purpose.

[0321] "Notification" refers to a means or process of communication used to inform users of new information or events.

[0322] "Participation" refers to the act of joining a group or event selected by a user and engaging in activities with other users.

[0323] "Communication" is an activity in which participants exchange information and messages to deepen their understanding and relationships.

[0324] "Feedback" is the collection of opinions and reactions about a particular experience or event, and this data is used later to help make improvements.

[0325] A "real-time chat function" is a communication method that allows participants to exchange messages simultaneously, and is a technology that enables rapid communication.

[0326] This invention is a community matching system designed to help elderly people build new social connections. The system begins by sending user data entered by the user via a terminal to a server to build a profile. This profile reflects the user's basic information and interests and is stored in a database. Based on this profile data, the server uses an AI algorithm to identify other users with similar interests and experiences. The analytical techniques used here include natural language processing.

[0327] Furthermore, the server searches the event database for events and groups that match the user's interests and notifies them via push notifications on their device. These notifications also include access to a real-time chat function, allowing users to communicate with other participants in advance. This functionality is enabled by a real-time communication platform such as Firebase.

[0328] After participating in an event, the server collects feedback such as applause and comments, and uses AI to recommend and improve future events. This ensures that users can always experience high-quality activities.

[0329] For example, if a user in their 70s is interested in "home gardening" and "exploring local history," the system will recommend home gardening workshops and historical tours held in the local community. The user can then use their smartphone from home to meet and connect with new friends who share similar interests.

[0330] An example of a prompt to pass to a generative AI model is, "What kind of community events should be suggested to seniors seeking new social activities after retirement?" Through such prompts, the system can make more accurate recommendations.

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] Users use their devices to enter their profile information. This data includes information such as name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0334] Step 2:

[0335] The server uses AI algorithms to analyze data based on profile information stored in the database. This process utilizes natural language processing to analyze the user's hobbies and interests and generates tags related to their areas of interest. As a result of the analysis, other users with similar interests are identified and listed as matching candidates.

[0336] Step 3:

[0337] The server searches the event database and group information based on the generated interest tags and selects events and groups suitable for the user. At this time, it evaluates the degree of matching between the interest tags and the event information to select the most suitable candidates. The selected information, including details, is sent to the device as a push notification.

[0338] Step 4:

[0339] Users view the details of the notified event or group on their device and decide whether to participate. Once they choose to participate, their decision is sent to the server via their device, completing the registration process.

[0340] Step 5:

[0341] The server utilizes real-time chat functionality to support communication among event and group participants. Users can exchange messages with other participants in real time, sharing information and building relationships.

[0342] Step 6:

[0343] After an event or group activity concludes, the server automatically sends out a survey requesting feedback from users. The user feedback is collected by the server and analyzed by AI. This analysis is then used to improve and recommend future events.

[0344] 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.

[0345] This invention is a community matching system that incorporates an emotion engine into the process from user data acquisition to feedback, in order to help elderly people before and after retirement build new social connections and prevent isolation. The emotion engine analyzes the user's emotions and incorporates them into profile data, with the aim of matching them with the most suitable groups and events based on the results.

[0346] Program processing

[0347] First, the user creates a basic profile through the device. Once the basic information is entered, the device activates voice input and facial recognition camera, instructing the emotion engine to analyze the user's emotions. The analyzed emotion information is added to the profile and sent to the server.

[0348] The server stores the received profile data in a database and begins analysis using AI algorithms and an emotion engine. It generates tags containing emotion data from the profile and lists other users suitable for similar emotional states and activities as matching candidates.

[0349] Next, the server searches the event database and selects events and groups that match the user's emotional state. Based on this emotional information, it recommends cultural workshops if relaxation is desired, and sports events if energy is needed.

[0350] Next, the server sends filtered event information to the device. The device then presents the event information to the user via push notification, allowing them to view specific activity details within the app. The user selects an event they are interested in and registers to participate.

[0351] During the event, real-time sentiment analysis of the user is performed through the device, and communication support functions with other participants are utilized to support conversations that are appropriate to the user's emotional state.

[0352] After the event ends, the server requests feedback from users. It sends a feedback form that combines data from the emotion engine, and uses the feedback to recommend future events and improve the service.

[0353] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion engine will recommend a relaxing book club and suggest participation. In this way, the system supports the building of comfortable and meaningful relationships by providing activities tailored to the user's emotional state.

[0354] The following describes the processing flow.

[0355] Step 1:

[0356] The user creates a profile through the device. Specifically, they enter their name, age, hobbies, and interests into the application's form and click the "Next" button. After the profile is created, the device activates the voice input function and camera and prompts the user to express their emotions.

[0357] Step 2:

[0358] The device transmits data acquired through voice input and the camera to the emotion engine. The emotion engine analyzes voice tone and facial expression data to identify the user's primary emotional state. For example, it can determine states such as stress, joy, and excitement.

[0359] Step 3:

[0360] Once the user's emotional information is identified, the device sends the results to the server, where they are stored in a database along with the profile data.

[0361] Step 4:

[0362] The server begins analyzing the stored profile data. An AI algorithm generates tags based on the user's interests, experiences, and emotional data, and identifies other users with similar tags. This then lists potential matches based on emotions and interests.

[0363] Step 5:

[0364] The server consults the event database to select the event or group best suited to the user's emotional state. During selection, it considers the user's emotional state, choosing a mindfulness workshop if relaxation is needed, or a hiking event for those feeling more active.

[0365] Step 6:

[0366] The server sends information about selected groups and events to the terminal. Based on the received information, the terminal displays the details of the event or group on the user's screen and notifies them using the notification function.

[0367] Step 7:

[0368] Users check notifications on their devices, select events or groups that interest them, and register to participate. Once registration is complete, the device sends the participation information to the server and displays a confirmation message to the user.

[0369] Step 8:

[0370] While users are participating, the device performs real-time sentiment analysis and provides feedback and advice to stimulate communication among users.

[0371] Step 9:

[0372] After the activity ends, the server sends a feedback form to the user. It generates feedback using data from the emotion engine and collects responses from the user. The server analyzes this feedback and uses it to guide future activities and improve the service.

[0373] (Example 2)

[0374] 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".

[0375] Modern seniors and retirees often face feelings of isolation and weakened social connections, making it difficult for them to find new social links. Addressing these challenges and providing appropriate activities and groups tailored to individual emotional states is crucial for improving their social well-being.

[0376] 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.

[0377] In this invention, the server includes means for acquiring user information and storing said information as a data structure, means for analyzing the data structure using an emotion analysis device and matching it with other participants who have similar emotional states or behaviors, and means for selecting candidate groups or activities and providing them to the user. This makes it possible to provide optimal social interaction opportunities that match the emotional state of each individual user.

[0378] "User information" refers to basic data about individual participants that is entered into the system, including name, age, hobbies, and emotional state.

[0379] A "data structure" refers to a collection of information used to store and manage user information and analysis results, and is a format that allows computers to process data efficiently.

[0380] An "emotion analysis device" refers to a processing system or algorithm used to analyze voice and facial expression data acquired from a user and determine their emotional state.

[0381] "Matching" refers to the process of selecting appropriate other participants and activities based on the user's emotional state, interests, and activity patterns.

[0382] A "group or activity" refers to an event or group of participants proposed by the system for the purpose of social interaction, providing a space for users to build new relationships through their participation.

[0383] "Electronic notification function" refers to a technology that instantly delivers information via a terminal, and is a means of quickly and efficiently informing users of the information being provided.

[0384] A "generative AI model" is a model that has an algorithm that learns from large amounts of data to recognize and analyze complex patterns, and is particularly used for analysis based on user emotions and interests.

[0385] This invention is a system designed to help elderly people and retirees build new social connections and prevent isolation. The system aims to suggest appropriate social interaction activities based on the user's emotional state.

[0386] The user creates a basic profile using the device. The device displays screens for entering information such as name, age, hobbies, and areas of interest. Once the user's basic information is entered, the device activates voice input and facial recognition camera functions.

[0387] The emotion analysis device collects emotional data from the user's voice and facial expressions via the terminal and uses a generative AI model to analyze complex emotional patterns. The analysis results are added to the user's profile and sent to the server.

[0388] The server stores the received profile data in a database and performs analysis using AI algorithms and sentiment analysis devices. Based on the sentiment data included in the profile, it selects other participants with similar emotional states and activities as matching candidates. It also searches the event database to identify groups and activities that match the user's emotional state.

[0389] The selected information is sent to the terminal, and the user immediately receives event information via electronic notification. Users register to participate in activities that interest them, and during the activity, the interaction is optimized through real-time sentiment analysis. After the activity ends, the server collects feedback from the user and uses it to make suggestions for the next event.

[0390] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion analysis device will recommend and suggest participation in a relaxing book club. This helps in building comfortable and meaningful relationships.

[0391] An example of a prompt message might be, "Design an AI service that suggests the optimal event based on the analysis results."

[0392] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0393] Step 1:

[0394] The user operates the device to enter basic profile information. This information includes name, age, hobbies, and areas of interest. This data is temporarily stored as a data structure within the device.

[0395] Step 2:

[0396] The device activates its voice input function and facial recognition camera. The user's voice and facial data are input to the emotion analysis device. By collecting this data, the user's emotions are detected in real time, and a generative AI model analyzes this data to output the emotional state.

[0397] Step 3:

[0398] The device adds the analyzed emotional state to the user's profile and sends the profile data to the server. The input here is the result of the emotional analysis, and the output is the transmission of the profile data to the server.

[0399] Step 4:

[0400] The server stores the received profile data in a database. This data includes the user's basic information and emotional state. Based on the stored data, an AI algorithm and an emotion analysis device are used to analyze the user's state and output other participants with similar emotional states or activity patterns.

[0401] Step 5:

[0402] The server searches the event database to select groups and activities that match the user's emotional state. The server receives emotional data as input, outputs events that match relaxation or vitality, and sends the selection results to the terminal.

[0403] Step 6:

[0404] The terminal receives event information sent from the server and presents it to the user using an electronic notification function. The user reviews the event information and selects activities of interest. The input is the event information, and the output is the user's selections.

[0405] Step 7:

[0406] While the user participates in the event, the device analyzes the user's emotions in real time and optimizes the interaction based on the results. In this process, emotion analysis data is used as input, and the optimized interaction method is output.

[0407] Step 8:

[0408] After the event ends, the server requests feedback. Users input their feedback via their terminals and send it to the server. This feedback is used to make suggestions for future events and improve the service. The input is feedback information, and the output is adjustments to the next recommended event.

[0409] (Application Example 2)

[0410] 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."

[0411] Retired seniors often experience a weakening of social connections and feelings of isolation. In this situation, there is a need to support them in effectively participating in new communities and building comfortable and meaningful relationships through activities that align with their emotional state. However, conventional systems struggle to match individuals with appropriate emotional states, making it difficult to provide personalized experiences that meet user needs.

[0412] 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.

[0413] In this invention, the server includes means for acquiring user data and storing said data as a profile, means for analyzing the profile data and emotional information and matching it with other users who have similar interests or experiences and emotional states, and means for selecting candidate groups or events and providing them to the user. This makes it possible to encourage optimal community participation while taking into account the user's emotional state and to form social connections that meet the user's individual needs.

[0414] "User data" refers to detailed information about a user, including basic information, activity history, and emotional information.

[0415] A "profile" is a dataset created based on user data that shows the characteristics and attributes of a user.

[0416] "Emotional information" refers to data obtained by analyzing the user's emotions and psychological state.

[0417] "Matching" is the process of connecting users who have similar interests, experiences, or emotional states.

[0418] A "group or event" refers to a collective activity or event intended for user participation.

[0419] "Real-time sentiment analysis" refers to a technology that instantly analyzes a user's emotional state during a group activity.

[0420] "Feedback" is the process of collecting users' feelings and opinions about an activity.

[0421] "Push notifications" refer to a method of sending information directly to a user's device to notify them.

[0422] "Communication support" refers to a function designed to facilitate smooth communication between users.

[0423] The system for implementing this invention is built using a terminal such as a smartphone and a cloud-based server. The user first installs a dedicated application on their smartphone and creates a basic profile. During this process, emotional information can be input using a camera equipped with voice input and facial recognition capabilities. The terminal utilizes software libraries such as Google Cloud Speech-to-Text API and OpenCV for voice recognition and image processing.

[0424] The acquired user data is sent to the server as profile data and sentiment information. The server stores this data in a database such as MySQL and analyzes it using a generative AI model based on TensorFlow. Based on the analysis results, other users, groups, and events that match the user's interests and sentiment state are matched.

[0425] The server sends event information tailored to the user's needs to their device via push notifications. During the event, the server performs real-time sentiment analysis and supports smooth conversations through its communication support function.

[0426] For example, if the system analyzes that a user is experiencing stress while creating their profile, the emotion engine will recommend an online book club as a relaxing activity and notify the user of the participation invitation. In this way, the system provides users with experiences tailored to their individual needs and helps them build social connections.

[0427] An example of a prompt message would be: "This user recently left their job and is experiencing stress. What kind of relaxation event should we suggest?"

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The user launches a smartphone application and creates a basic profile. Inputs include the user's name, age, interests, and past experiences. This information is then saved as profile data.

[0431] Step 2:

[0432] To collect user emotion data, the device's camera and microphone are activated. The user's facial expressions and voice tone are captured as input, and this data is analyzed by an emotion analysis engine as output. The analysis results are added to the profile as emotion information.

[0433] Step 3:

[0434] The device sends profile data and emotion information to the server. The server receives this data as input and stores it in its database. As output, a detailed user profile is completed.

[0435] Step 4:

[0436] The server launches a generative AI model using TensorFlow to analyze profile data. As input, it generates tags based on interests, experiences, and emotional states, using stored user data. As output, it lists suitable candidates for other users and events for the user.

[0437] Step 5:

[0438] The server selects the group or event that best matches the user's sentiment information. It uses the generated tags as input and searches the event database. The output determines the most suitable group or event information for the user.

[0439] Step 6:

[0440] The server sends the selected event information to the terminal and sends a push notification. The selected event information is used as input, and the event notification is displayed on the user's terminal as output.

[0441] Step 7:

[0442] Users participate in events and perform real-time sentiment analysis through their devices. Facial and voice data collected during participation are used as input and processed by a sentiment analysis engine. Communication support information is provided as output.

[0443] Step 8:

[0444] After the event ends, the server requests feedback from users. The input consists of collected opinions and feedback from users, which are then used as output for recommending future activities and improving the service.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention is a community matching system that helps older adults build new social connections before and after retirement. The system provides functions for data acquisition, analysis, matching, notification, participation, communication, and feedback, helping users effectively participate in events and groups that interest them.

[0462] Program processing

[0463] First, users create their profile using their device. This profile includes information such as their name, age, hobbies, interests, and past work experience. This information is sent to the server and stored in a database.

[0464] The server uses an AI algorithm to analyze the received profile data. The AI ​​generates interest tags based on the user's hobbies and interests, and identifies other users with similar characteristics. This analysis results in a list of potential matching candidates.

[0465] Next, the server searches the event database for events and groups that might be of interest to the user, based on the user's tags. Information about the found events and groups is provided to the user's device via push notifications, etc., and the user reviews this information.

[0466] When a user selects an event or group they wish to participate in, their registration information is sent to the server via their device, and the user can access the chatbot function to begin interacting with other participants in advance.

[0467] After the event ends, the server automatically sends a survey to users requesting feedback and collects user responses. This feedback is analyzed by AI and used to make suggestions and improvements for future events and activities.

[0468] For example, if a 59-year-old user enters that they are interested in "hiking" and "local volunteering," the system will recommend local hiking clubs and regularly held volunteer events that match their interests. Through this process, users can form new connections and experience enriching activities.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] Users create a profile using their device. Specifically, they enter their name, age, hobbies, interests, and past work history in an application on their device, and then press the "Save" button to send the entered information to the server.

[0472] Step 2:

[0473] The server receives profile data submitted by the user and stores it in a database. Next, an AI algorithm is used to analyze the profile data and generate and classify interest tags based on the user's interests and experiences.

[0474] Step 3:

[0475] The server uses the generated interest tags to calculate similarity with other users, identify users with common characteristics, and list them as matching candidates. Methods such as cosine similarity and Euclidean distance are used in this process.

[0476] Step 4:

[0477] The server searches the event database to identify events and groups that match the user's interest tags. It extracts information about the relevant events and groups and ranks them according to their recommendation level.

[0478] Step 5:

[0479] The server sends recommended group and event information to the device. The device then pushes the received information to the user, allowing them to view details within the app.

[0480] Step 6:

[0481] Users review recommended information, select groups and events that interest them, and register to participate from their device. The user's selection is sent to the server, and registration is complete.

[0482] Step 7:

[0483] Users can use their devices to communicate with other participants in advance by utilizing the chatbot function of the groups and events they have registered for.

[0484] Step 8:

[0485] After the event ends, the server sends a feedback form to users and collects their evaluation of the activity through a survey. The collected feedback is analyzed by AI and stored to help improve future events and increase user satisfaction.

[0486] (Example 1)

[0487] 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."

[0488] In modern society, there is a problem in that it is difficult for older adults to build new social connections after retirement. In particular, finding appropriate events or groups based on individual hobbies and interests is not easy due to the sheer volume of information and the difficulty in judging suitability. As a result, older adults may feel isolated, which can lead to a decline in their quality of life (QOL).

[0489] 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.

[0490] In this invention, the server includes means for acquiring user information from a terminal and storing the information as a profile in a database; means for analyzing the profile information on the server, generating user interest tags using artificial intelligence technology, and identifying other users with similar characteristics; and means for the server to search the information database for candidate activities and groups based on the interest tags and recommend them to the user. This makes it possible for users to easily find events and groups that suit their hobbies and interests and to effectively build new social connections.

[0491] "User information" refers to data necessary to form a personal profile provided by a user, and includes a variety of information such as name, age, hobbies, interests, and past work history.

[0492] A "terminal" refers to an electronic device used by a user for inputting, receiving, or communicating information, and includes devices such as smartphones and computers.

[0493] A "server" refers to a computer system that processes data received from users and provides various services to users through a network.

[0494] A "profile" is a dataset constructed by integrating individual user information, and it refers to a collection of information including the user's hobbies, interests, and past behavior.

[0495] "Artificial intelligence technology" refers to technologies that use computer programs to learn and reason like humans, supporting decision-making, and includes techniques such as machine learning and natural language processing.

[0496] "Interest tags" are keywords and topics extracted from a user's profile information, and refer to labels that concisely indicate a user's hobbies and interests.

[0497] "Activities and groups" refer to events and groups that users can participate in, and include gatherings aimed at social interaction or the pursuit of hobbies.

[0498] An "information database" is a system in which various types of information are organized and stored, and it refers to a collection of data used to search for activities and groups that match the user's interests.

[0499] "Push notification" refers to a communication method that provides information from a server to a user's terminal in real time, and is a mechanism that enables immediate notification of event information.

[0500] "Feedback" refers to input information in which users evaluate a service or activity and provide their impressions and suggestions for improvement. This is important data that contributes to improving the quality of the service.

[0501] This community matching system was developed to help seniors build new social connections after retirement. The system consists of three main elements: users, terminals, and servers.

[0502] Users first create their profile using a device. This profile includes personal information such as the user's name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0503] The server applies an AI algorithm based on stored profile information to generate interest tags that reflect the user's interests and hobbies. This AI algorithm uses natural language processing techniques to extract relevant information from the user's input data and uses a generative AI model to identify similarities with other users.

[0504] Next, the server searches the information database based on interest tags and selects activities and groups that the user is likely to be interested in. As a result, the activities recommended to the user will match their individual preferences.

[0505] Recommended activities and groups are provided to the user's device via push notifications. Through these notifications, users can view details about the activities and select events or groups they wish to participate in.

[0506] Furthermore, when users participate in an activity they have selected, the server provides a chatbot function to support pre-activity interaction with other participants through their device. This promotes active communication among users.

[0507] After the activity ends, the server asks users for feedback in the form of a survey. This feedback is analyzed by AI and used to suggest future events and improve activities.

[0508] For example, if a 59-year-old user enters "hiking" and "local volunteering" as interests on their device, the server will recommend local hiking clubs and regularly held volunteer events that match those interests. Through this process, users can form new connections and experience enriching activities.

[0509] An example of a prompt message would be: "Create a system that recommends events based on the user's interests. For example, explain how to provide relevant event information to someone who is interested in hiking."

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] Users open a profile creation screen on their device and enter their name, age, hobbies, interests, and past work history. This information is entered into the device, confirming the user's basic information. The device then sends this information to the server, where it is stored in the database as the user's profile. The entered information is crucial data used for subsequent analysis and matching.

[0513] Step 2:

[0514] The server prepares the received user profile information for analysis. Specifically, it passes the stored data to an AI algorithm. The server uses natural language processing techniques and generative AI models to generate interest tags that represent the user's hobbies and interests. In this process, keywords are extracted from the text data and listed as tags. As output, a set of tags indicating the user's interests is generated.

[0515] Step 3:

[0516] The server searches its database for suitable activities and groups based on the generated interest tags. The input to the search is the user's tag information, and the output is a list of recommended events and groups based on this information. The server applies an AI-powered matching algorithm to select candidates that match the user's interests. Specifically, it extracts the names and details of events that match the user's tags.

[0517] Step 4:

[0518] The server sends detailed information about selected events or groups to the device. The device presents this information to the user as a push notification. This notification includes information such as the event name, content, date and time, and location. The user reviews the notification and selects events that interest them. The output includes the notification content that caught the user's attention.

[0519] Step 5:

[0520] Users register to participate in selected events or groups using their devices. The devices send their participation request information to the server. The server verifies the registration based on this information and approves the user's participation. Furthermore, the server provides users with access to a chatbot function, allowing them to communicate with other participants in advance. This initiates interaction among users.

[0521] Step 6:

[0522] Once the event ends, the server sends a survey to users requesting feedback. Users answer the survey on their devices and send it back to the server. The server uses AI to analyze the collected feedback and uses it to suggest future events and improve activities. This helps to improve the quality of the service in the future. The output provided is the analyzed feedback data.

[0523] (Application Example 1)

[0524] 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."

[0525] It is difficult for older adults to build new social connections after retirement, raising concerns about loneliness and social isolation. While building new relationships through activities based on interests and hobbies is effective, finding appropriate events and groups is not easy. Furthermore, limited interaction among participants makes pre-event communication difficult. Additionally, there is a lack of mechanisms to utilize post-event feedback for future improvements.

[0526] 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.

[0527] In this invention, the server includes means for acquiring user data and storing it as a profile, means for analyzing the profile data and matching it with other users who have similar interests or experiences, and means for selecting candidate groups or events and providing them to the user. This makes it possible for elderly people to easily participate in appropriate social activities and to deepen their interactions with other participants in advance. Furthermore, feedback after the activity can be used to improve future activities.

[0528] "User data" refers to personal information, interests, hobbies, and past work history provided by individual users.

[0529] A "profile" is a collection of information that describes the characteristics of each individual, generated based on user data.

[0530] "Analysis" is the process of analyzing user data using algorithms and programs to derive specific insights and trends.

[0531] "Matching" is the process of connecting and linking multiple users who have similar interests or experiences.

[0532] A "group or event" refers to a collection or activity in which multiple people participate and act with a common interest or purpose.

[0533] "Notification" refers to a means or process of communication used to inform users of new information or events.

[0534] "Participation" refers to the act of joining a group or event selected by a user and engaging in activities with other users.

[0535] "Communication" is an activity in which participants exchange information and messages to deepen their understanding and relationships.

[0536] "Feedback" is the collection of opinions and reactions about a particular experience or event, and this data is used later to help make improvements.

[0537] A "real-time chat function" is a communication method that allows participants to exchange messages simultaneously, and is a technology that enables rapid communication.

[0538] This invention is a community matching system designed to help elderly people build new social connections. The system begins by sending user data entered by the user via a terminal to a server to build a profile. This profile reflects the user's basic information and interests and is stored in a database. Based on this profile data, the server uses an AI algorithm to identify other users with similar interests and experiences. The analytical techniques used here include natural language processing.

[0539] Furthermore, the server searches the event database for events and groups that match the user's interests and notifies them via push notifications on their device. These notifications also include access to a real-time chat function, allowing users to communicate with other participants in advance. This functionality is enabled by a real-time communication platform such as Firebase.

[0540] After participating in an event, the server collects feedback such as applause and comments, and uses AI to recommend and improve future events. This ensures that users can always experience high-quality activities.

[0541] For example, if a user in their 70s is interested in "home gardening" and "exploring local history," the system will recommend home gardening workshops and historical tours held in the local community. The user can then use their smartphone from home to meet and connect with new friends who share similar interests.

[0542] An example of a prompt to pass to a generative AI model is, "What kind of community events should be suggested to seniors seeking new social activities after retirement?" Through such prompts, the system can make more accurate recommendations.

[0543] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0544] Step 1:

[0545] Users use their devices to enter their profile information. This data includes information such as name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0546] Step 2:

[0547] The server uses AI algorithms to analyze data based on profile information stored in the database. This process utilizes natural language processing to analyze the user's hobbies and interests and generates tags related to their areas of interest. As a result of the analysis, other users with similar interests are identified and listed as matching candidates.

[0548] Step 3:

[0549] The server searches the event database and group information based on the generated interest tags and selects events and groups suitable for the user. At this time, it evaluates the degree of matching between the interest tags and the event information to select the most suitable candidates. The selected information, including details, is sent to the device as a push notification.

[0550] Step 4:

[0551] Users view the details of the notified event or group on their device and decide whether to participate. Once they choose to participate, their decision is sent to the server via their device, completing the registration process.

[0552] Step 5:

[0553] The server utilizes real-time chat functionality to support communication among event and group participants. Users can exchange messages with other participants in real time, sharing information and building relationships.

[0554] Step 6:

[0555] After an event or group activity concludes, the server automatically sends out a survey requesting feedback from users. The user feedback is collected by the server and analyzed by AI. This analysis is then used to improve and recommend future events.

[0556] 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.

[0557] This invention is a community matching system that incorporates an emotion engine into the process from user data acquisition to feedback, in order to help elderly people before and after retirement build new social connections and prevent isolation. The emotion engine analyzes the user's emotions and incorporates them into profile data, with the aim of matching them with the most suitable groups and events based on the results.

[0558] Program processing

[0559] First, the user creates a basic profile through the device. Once the basic information is entered, the device activates voice input and facial recognition camera, instructing the emotion engine to analyze the user's emotions. The analyzed emotion information is added to the profile and sent to the server.

[0560] The server stores the received profile data in a database and begins analysis using AI algorithms and an emotion engine. It generates tags containing emotion data from the profile and lists other users suitable for similar emotional states and activities as matching candidates.

[0561] Next, the server searches the event database and selects events and groups that match the user's emotional state. Based on this emotional information, it recommends cultural workshops if relaxation is desired, and sports events if energy is needed.

[0562] Next, the server sends filtered event information to the device. The device then presents the event information to the user via push notification, allowing them to view specific activity details within the app. The user selects an event they are interested in and registers to participate.

[0563] During the event, real-time sentiment analysis of the user is performed through the device, and communication support functions with other participants are utilized to support conversations that are appropriate to the user's emotional state.

[0564] After the event ends, the server requests feedback from users. It sends a feedback form that combines data from the emotion engine, and uses the feedback to recommend future events and improve the service.

[0565] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion engine will recommend a relaxing book club and suggest participation. In this way, the system supports the building of comfortable and meaningful relationships by providing activities tailored to the user's emotional state.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user creates a profile through the device. Specifically, they enter their name, age, hobbies, and interests into the application's form and click the "Next" button. After the profile is created, the device activates the voice input function and camera and prompts the user to express their emotions.

[0569] Step 2:

[0570] The device transmits data acquired through voice input and the camera to the emotion engine. The emotion engine analyzes voice tone and facial expression data to identify the user's primary emotional state. For example, it can determine states such as stress, joy, and excitement.

[0571] Step 3:

[0572] Once the user's emotional information is identified, the device sends the results to the server, where they are stored in a database along with the profile data.

[0573] Step 4:

[0574] The server begins analyzing the stored profile data. An AI algorithm generates tags based on the user's interests, experiences, and emotional data, and identifies other users with similar tags. This then lists potential matches based on emotions and interests.

[0575] Step 5:

[0576] The server consults the event database to select the event or group best suited to the user's emotional state. During selection, it considers the user's emotional state, choosing a mindfulness workshop if relaxation is needed, or a hiking event for those feeling more active.

[0577] Step 6:

[0578] The server sends information about selected groups and events to the terminal. Based on the received information, the terminal displays the details of the event or group on the user's screen and notifies them using the notification function.

[0579] Step 7:

[0580] Users check notifications on their devices, select events or groups that interest them, and register to participate. Once registration is complete, the device sends the participation information to the server and displays a confirmation message to the user.

[0581] Step 8:

[0582] While users are participating, the device performs real-time sentiment analysis and provides feedback and advice to stimulate communication among users.

[0583] Step 9:

[0584] After the activity ends, the server sends a feedback form to the user. It generates feedback using data from the emotion engine and collects responses from the user. The server analyzes this feedback and uses it to guide future activities and improve the service.

[0585] (Example 2)

[0586] 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."

[0587] Modern seniors and retirees often face feelings of isolation and weakened social connections, making it difficult for them to find new social links. Addressing these challenges and providing appropriate activities and groups tailored to individual emotional states is crucial for improving their social well-being.

[0588] 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.

[0589] In this invention, the server includes means for acquiring user information and storing said information as a data structure, means for analyzing the data structure using an emotion analysis device and matching it with other participants who have similar emotional states or behaviors, and means for selecting candidate groups or activities and providing them to the user. This makes it possible to provide optimal social interaction opportunities that match the emotional state of each individual user.

[0590] "User information" refers to basic data about individual participants that is entered into the system, including name, age, hobbies, and emotional state.

[0591] A "data structure" refers to a collection of information used to store and manage user information and analysis results, and is a format that allows computers to process data efficiently.

[0592] An "emotion analysis device" refers to a processing system or algorithm used to analyze voice and facial expression data acquired from a user and determine their emotional state.

[0593] "Matching" refers to the process of selecting appropriate other participants and activities based on the user's emotional state, interests, and activity patterns.

[0594] A "group or activity" refers to an event or group of participants proposed by the system for the purpose of social interaction, providing a space for users to build new relationships through their participation.

[0595] "Electronic notification function" refers to a technology that instantly delivers information via a terminal, and is a means of quickly and efficiently informing users of the information being provided.

[0596] A "generative AI model" is a model that has an algorithm that learns from large amounts of data to recognize and analyze complex patterns, and is particularly used for analysis based on user emotions and interests.

[0597] This invention is a system designed to help elderly people and retirees build new social connections and prevent isolation. The system aims to suggest appropriate social interaction activities based on the user's emotional state.

[0598] The user creates a basic profile using the device. The device displays screens for entering information such as name, age, hobbies, and areas of interest. Once the user's basic information is entered, the device activates voice input and facial recognition camera functions.

[0599] The emotion analysis device collects emotional data from the user's voice and facial expressions via the terminal and uses a generative AI model to analyze complex emotional patterns. The analysis results are added to the user's profile and sent to the server.

[0600] The server stores the received profile data in a database and performs analysis using AI algorithms and sentiment analysis devices. Based on the sentiment data included in the profile, it selects other participants with similar emotional states and activities as matching candidates. It also searches the event database to identify groups and activities that match the user's emotional state.

[0601] The selected information is sent to the terminal, and the user immediately receives event information via electronic notification. Users register to participate in activities that interest them, and during the activity, the interaction is optimized through real-time sentiment analysis. After the activity ends, the server collects feedback from the user and uses it to make suggestions for the next event.

[0602] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion analysis device will recommend and suggest participation in a relaxing book club. This helps in building comfortable and meaningful relationships.

[0603] An example of a prompt message might be, "Design an AI service that suggests the optimal event based on the analysis results."

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] The user operates the device to enter basic profile information. This information includes name, age, hobbies, and areas of interest. This data is temporarily stored as a data structure within the device.

[0607] Step 2:

[0608] The device activates its voice input function and facial recognition camera. The user's voice and facial data are input to the emotion analysis device. By collecting this data, the user's emotions are detected in real time, and a generative AI model analyzes this data to output the emotional state.

[0609] Step 3:

[0610] The device adds the analyzed emotional state to the user's profile and sends the profile data to the server. The input here is the result of the emotional analysis, and the output is the transmission of the profile data to the server.

[0611] Step 4:

[0612] The server stores the received profile data in a database. This data includes the user's basic information and emotional state. Based on the stored data, an AI algorithm and an emotion analysis device are used to analyze the user's state and output other participants with similar emotional states or activity patterns.

[0613] Step 5:

[0614] The server searches the event database to select groups and activities that match the user's emotional state. The server receives emotional data as input, outputs events that match relaxation or vitality, and sends the selection results to the terminal.

[0615] Step 6:

[0616] The terminal receives event information sent from the server and presents it to the user using an electronic notification function. The user reviews the event information and selects activities of interest. The input is the event information, and the output is the user's selections.

[0617] Step 7:

[0618] While the user participates in the event, the device analyzes the user's emotions in real time and optimizes the interaction based on the results. In this process, emotion analysis data is used as input, and the optimized interaction method is output.

[0619] Step 8:

[0620] After the event ends, the server requests feedback. Users input their feedback via their terminals and send it to the server. This feedback is used to make suggestions for future events and improve the service. The input is feedback information, and the output is adjustments to the next recommended event.

[0621] (Application Example 2)

[0622] 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."

[0623] Retired seniors often experience a weakening of social connections and feelings of isolation. In this situation, there is a need to support them in effectively participating in new communities and building comfortable and meaningful relationships through activities that align with their emotional state. However, conventional systems struggle to match individuals with appropriate emotional states, making it difficult to provide personalized experiences that meet user needs.

[0624] 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.

[0625] In this invention, the server includes means for acquiring user data and storing said data as a profile, means for analyzing the profile data and emotional information and matching it with other users who have similar interests or experiences and emotional states, and means for selecting candidate groups or events and providing them to the user. This makes it possible to encourage optimal community participation while taking into account the user's emotional state and to form social connections that meet the user's individual needs.

[0626] "User data" refers to detailed information about a user, including basic information, activity history, and emotional information.

[0627] A "profile" is a dataset created based on user data that shows the characteristics and attributes of a user.

[0628] "Emotional information" refers to data obtained by analyzing the user's emotions and psychological state.

[0629] "Matching" is the process of connecting users who have similar interests, experiences, or emotional states.

[0630] A "group or event" refers to a collective activity or event intended for user participation.

[0631] "Real-time sentiment analysis" refers to a technology that instantly analyzes a user's emotional state during a group activity.

[0632] "Feedback" is the process of collecting users' feelings and opinions about an activity.

[0633] "Push notifications" refer to a method of sending information directly to a user's device to notify them.

[0634] "Communication support" refers to a function designed to facilitate smooth communication between users.

[0635] The system for implementing this invention is built using a terminal such as a smartphone and a cloud-based server. The user first installs a dedicated application on their smartphone and creates a basic profile. During this process, emotional information can be input using a camera equipped with voice input and facial recognition capabilities. The terminal utilizes software libraries such as Google Cloud Speech-to-Text API and OpenCV for voice recognition and image processing.

[0636] The acquired user data is sent to the server as profile data and sentiment information. The server stores this data in a database such as MySQL and analyzes it using a generative AI model based on TensorFlow. Based on the analysis results, other users, groups, and events that match the user's interests and sentiment state are matched.

[0637] The server sends event information tailored to the user's needs to their device via push notifications. During the event, the server performs real-time sentiment analysis and supports smooth conversations through its communication support function.

[0638] For example, if the system analyzes that a user is experiencing stress while creating their profile, the emotion engine will recommend an online book club as a relaxing activity and notify the user of the participation invitation. In this way, the system provides users with experiences tailored to their individual needs and helps them build social connections.

[0639] An example of a prompt message would be: "This user recently left their job and is experiencing stress. What kind of relaxation event should we suggest?"

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The user launches a smartphone application and creates a basic profile. Inputs include the user's name, age, interests, and past experiences. This information is then saved as profile data.

[0643] Step 2:

[0644] To collect user emotion data, the device's camera and microphone are activated. The user's facial expressions and voice tone are captured as input, and this data is analyzed by an emotion analysis engine as output. The analysis results are added to the profile as emotion information.

[0645] Step 3:

[0646] The device sends profile data and emotion information to the server. The server receives this data as input and stores it in its database. As output, a detailed user profile is completed.

[0647] Step 4:

[0648] The server launches a generative AI model using TensorFlow to analyze profile data. As input, it generates tags based on interests, experiences, and emotional states, using stored user data. As output, it lists suitable candidates for other users and events for the user.

[0649] Step 5:

[0650] The server selects the group or event that best matches the user's sentiment information. It uses the generated tags as input and searches the event database. The output determines the most suitable group or event information for the user.

[0651] Step 6:

[0652] The server sends the selected event information to the terminal and sends a push notification. The selected event information is used as input, and the event notification is displayed on the user's terminal as output.

[0653] Step 7:

[0654] Users participate in events and perform real-time sentiment analysis through their devices. Facial and voice data collected during participation are used as input and processed by a sentiment analysis engine. Communication support information is provided as output.

[0655] Step 8:

[0656] After the event ends, the server requests feedback from users. The input consists of collected opinions and feedback from users, which are then used as output for recommending future activities and improving the service.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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).

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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".

[0674] This invention is a community matching system that helps older adults build new social connections before and after retirement. The system provides functions for data acquisition, analysis, matching, notification, participation, communication, and feedback, helping users effectively participate in events and groups that interest them.

[0675] Program processing

[0676] First, users create their profile using their device. This profile includes information such as their name, age, hobbies, interests, and past work experience. This information is sent to the server and stored in a database.

[0677] The server uses an AI algorithm to analyze the received profile data. The AI ​​generates interest tags based on the user's hobbies and interests, and identifies other users with similar characteristics. This analysis results in a list of potential matching candidates.

[0678] Next, the server searches the event database for events and groups that might be of interest to the user, based on the user's tags. Information about the found events and groups is provided to the user's device via push notifications, etc., and the user reviews this information.

[0679] When a user selects an event or group they wish to participate in, their registration information is sent to the server via their device, and the user can access the chatbot function to begin interacting with other participants in advance.

[0680] After the event ends, the server automatically sends a survey to users requesting feedback and collects user responses. This feedback is analyzed by AI and used to make suggestions and improvements for future events and activities.

[0681] For example, if a 59-year-old user enters that they are interested in "hiking" and "local volunteering," the system will recommend local hiking clubs and regularly held volunteer events that match their interests. Through this process, users can form new connections and experience enriching activities.

[0682] The following describes the processing flow.

[0683] Step 1:

[0684] Users create a profile using their device. Specifically, they enter their name, age, hobbies, interests, and past work history in an application on their device, and then press the "Save" button to send the entered information to the server.

[0685] Step 2:

[0686] The server receives profile data submitted by the user and stores it in a database. Next, an AI algorithm is used to analyze the profile data and generate and classify interest tags based on the user's interests and experiences.

[0687] Step 3:

[0688] The server uses the generated interest tags to calculate similarity with other users, identify users with common characteristics, and list them as matching candidates. Methods such as cosine similarity and Euclidean distance are used in this process.

[0689] Step 4:

[0690] The server searches the event database to identify events and groups that match the user's interest tags. It extracts information about the relevant events and groups and ranks them according to their recommendation level.

[0691] Step 5:

[0692] The server sends recommended group and event information to the device. The device then pushes the received information to the user, allowing them to view details within the app.

[0693] Step 6:

[0694] Users review recommended information, select groups and events that interest them, and register to participate from their device. The user's selection is sent to the server, and registration is complete.

[0695] Step 7:

[0696] Users can use their devices to communicate with other participants in advance by utilizing the chatbot function of the groups and events they have registered for.

[0697] Step 8:

[0698] After the event ends, the server sends a feedback form to users and collects their evaluation of the activity through a survey. The collected feedback is analyzed by AI and stored to help improve future events and increase user satisfaction.

[0699] (Example 1)

[0700] 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".

[0701] In modern society, there is a problem in that it is difficult for older adults to build new social connections after retirement. In particular, finding appropriate events or groups based on individual hobbies and interests is not easy due to the sheer volume of information and the difficulty in judging suitability. As a result, older adults may feel isolated, which can lead to a decline in their quality of life (QOL).

[0702] 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.

[0703] In this invention, the server includes means for acquiring user information from a terminal and storing the information as a profile in a database; means for analyzing the profile information on the server, generating user interest tags using artificial intelligence technology, and identifying other users with similar characteristics; and means for the server to search the information database for candidate activities and groups based on the interest tags and recommend them to the user. This makes it possible for users to easily find events and groups that suit their hobbies and interests and to effectively build new social connections.

[0704] "User information" refers to data necessary to form a personal profile provided by a user, and includes a variety of information such as name, age, hobbies, interests, and past work history.

[0705] A "terminal" refers to an electronic device used by a user for inputting, receiving, or communicating information, and includes devices such as smartphones and computers.

[0706] A "server" refers to a computer system that processes data received from users and provides various services to users through a network.

[0707] A "profile" is a dataset constructed by integrating individual user information, and it refers to a collection of information including the user's hobbies, interests, and past behavior.

[0708] "Artificial intelligence technology" refers to technologies that use computer programs to learn and reason like humans, supporting decision-making, and includes techniques such as machine learning and natural language processing.

[0709] "Interest tags" are keywords and topics extracted from a user's profile information, and refer to labels that concisely indicate a user's hobbies and interests.

[0710] "Activities and groups" refer to events and groups that users can participate in, and include gatherings aimed at social interaction or the pursuit of hobbies.

[0711] An "information database" is a system in which various types of information are organized and stored, and it refers to a collection of data used to search for activities and groups that match the user's interests.

[0712] "Push notification" refers to a communication method that provides information from a server to a user's terminal in real time, and is a mechanism that enables immediate notification of event information.

[0713] "Feedback" refers to input information in which users evaluate a service or activity and provide their impressions and suggestions for improvement. This is important data that contributes to improving the quality of the service.

[0714] This community matching system was developed to help seniors build new social connections after retirement. The system consists of three main elements: users, terminals, and servers.

[0715] Users first create their profile using a device. This profile includes personal information such as the user's name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0716] The server applies an AI algorithm based on stored profile information to generate interest tags that reflect the user's interests and hobbies. This AI algorithm uses natural language processing techniques to extract relevant information from the user's input data and uses a generative AI model to identify similarities with other users.

[0717] Next, the server searches the information database based on interest tags and selects activities and groups that the user is likely to be interested in. As a result, the activities recommended to the user will match their individual preferences.

[0718] Recommended activities and groups are provided to the user's device via push notifications. Through these notifications, users can view details about the activities and select events or groups they wish to participate in.

[0719] Furthermore, when users participate in an activity they have selected, the server provides a chatbot function to support pre-activity interaction with other participants through their device. This promotes active communication among users.

[0720] After the activity ends, the server asks users for feedback in the form of a survey. This feedback is analyzed by AI and used to suggest future events and improve activities.

[0721] For example, if a 59-year-old user enters "hiking" and "local volunteering" as interests on their device, the server will recommend local hiking clubs and regularly held volunteer events that match those interests. Through this process, users can form new connections and experience enriching activities.

[0722] An example of a prompt message would be: "Create a system that recommends events based on the user's interests. For example, explain how to provide relevant event information to someone who is interested in hiking."

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] Users open a profile creation screen on their device and enter their name, age, hobbies, interests, and past work history. This information is entered into the device, confirming the user's basic information. The device then sends this information to the server, where it is stored in the database as the user's profile. The entered information is crucial data used for subsequent analysis and matching.

[0726] Step 2:

[0727] The server prepares the received user profile information for analysis. Specifically, it passes the stored data to an AI algorithm. The server uses natural language processing techniques and generative AI models to generate interest tags that represent the user's hobbies and interests. In this process, keywords are extracted from the text data and listed as tags. As output, a set of tags indicating the user's interests is generated.

[0728] Step 3:

[0729] The server searches its database for suitable activities and groups based on the generated interest tags. The input to the search is the user's tag information, and the output is a list of recommended events and groups based on this information. The server applies an AI-powered matching algorithm to select candidates that match the user's interests. Specifically, it extracts the names and details of events that match the user's tags.

[0730] Step 4:

[0731] The server sends detailed information about selected events or groups to the device. The device presents this information to the user as a push notification. This notification includes information such as the event name, content, date and time, and location. The user reviews the notification and selects events that interest them. The output includes the notification content that caught the user's attention.

[0732] Step 5:

[0733] Users register to participate in selected events or groups using their devices. The devices send their participation request information to the server. The server verifies the registration based on this information and approves the user's participation. Furthermore, the server provides users with access to a chatbot function, allowing them to communicate with other participants in advance. This initiates interaction among users.

[0734] Step 6:

[0735] Once the event ends, the server sends a survey to users requesting feedback. Users answer the survey on their devices and send it back to the server. The server uses AI to analyze the collected feedback and uses it to suggest future events and improve activities. This helps to improve the quality of the service in the future. The output provided is the analyzed feedback data.

[0736] (Application Example 1)

[0737] 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".

[0738] It is difficult for older adults to build new social connections after retirement, raising concerns about loneliness and social isolation. While building new relationships through activities based on interests and hobbies is effective, finding appropriate events and groups is not easy. Furthermore, limited interaction among participants makes pre-event communication difficult. Additionally, there is a lack of mechanisms to utilize post-event feedback for future improvements.

[0739] 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.

[0740] In this invention, the server includes means for acquiring user data and storing it as a profile, means for analyzing the profile data and matching it with other users who have similar interests or experiences, and means for selecting candidate groups or events and providing them to the user. This makes it possible for elderly people to easily participate in appropriate social activities and to deepen their interactions with other participants in advance. Furthermore, feedback after the activity can be used to improve future activities.

[0741] "User data" refers to personal information, interests, hobbies, and past work history provided by individual users.

[0742] A "profile" is a collection of information that describes the characteristics of each individual, generated based on user data.

[0743] "Analysis" is the process of analyzing user data using algorithms and programs to derive specific insights and trends.

[0744] "Matching" is the process of connecting and linking multiple users who have similar interests or experiences.

[0745] A "group or event" refers to a collection or activity in which multiple people participate and act with a common interest or purpose.

[0746] "Notification" refers to a means or process of communication used to inform users of new information or events.

[0747] "Participation" refers to the act of joining a group or event selected by a user and engaging in activities with other users.

[0748] "Communication" is an activity in which participants exchange information and messages to deepen their understanding and relationships.

[0749] "Feedback" is the collection of opinions and reactions about a particular experience or event, and this data is used later to help make improvements.

[0750] A "real-time chat function" is a communication method that allows participants to exchange messages simultaneously, and is a technology that enables rapid communication.

[0751] This invention is a community matching system designed to help elderly people build new social connections. The system begins by sending user data entered by the user via a terminal to a server to build a profile. This profile reflects the user's basic information and interests and is stored in a database. Based on this profile data, the server uses an AI algorithm to identify other users with similar interests and experiences. The analytical techniques used here include natural language processing.

[0752] Furthermore, the server searches the event database for events and groups that match the user's interests and notifies them via push notifications on their device. These notifications also include access to a real-time chat function, allowing users to communicate with other participants in advance. This functionality is enabled by a real-time communication platform such as Firebase.

[0753] After participating in an event, the server collects feedback such as applause and comments, and uses AI to recommend and improve future events. This ensures that users can always experience high-quality activities.

[0754] For example, if a user in their 70s is interested in "home gardening" and "exploring local history," the system will recommend home gardening workshops and historical tours held in the local community. The user can then use their smartphone from home to meet and connect with new friends who share similar interests.

[0755] An example of a prompt to pass to a generative AI model is, "What kind of community events should be suggested to seniors seeking new social activities after retirement?" Through such prompts, the system can make more accurate recommendations.

[0756] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0757] Step 1:

[0758] Users use their devices to enter their profile information. This data includes information such as name, age, hobbies, interests, and past work history. This information is sent from the device to the server and stored in the database as a profile.

[0759] Step 2:

[0760] The server uses AI algorithms to analyze data based on profile information stored in the database. This process utilizes natural language processing to analyze the user's hobbies and interests and generates tags related to their areas of interest. As a result of the analysis, other users with similar interests are identified and listed as matching candidates.

[0761] Step 3:

[0762] The server searches the event database and group information based on the generated interest tags and selects events and groups suitable for the user. At this time, it evaluates the degree of matching between the interest tags and the event information to select the most suitable candidates. The selected information, including details, is sent to the device as a push notification.

[0763] Step 4:

[0764] Users view the details of the notified event or group on their device and decide whether to participate. Once they choose to participate, their decision is sent to the server via their device, completing the registration process.

[0765] Step 5:

[0766] The server utilizes real-time chat functionality to support communication among event and group participants. Users can exchange messages with other participants in real time, sharing information and building relationships.

[0767] Step 6:

[0768] After an event or group activity concludes, the server automatically sends out a survey requesting feedback from users. The user feedback is collected by the server and analyzed by AI. This analysis is then used to improve and recommend future events.

[0769] 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.

[0770] This invention is a community matching system that incorporates an emotion engine into the process from user data acquisition to feedback, in order to help elderly people before and after retirement build new social connections and prevent isolation. The emotion engine analyzes the user's emotions and incorporates them into profile data, with the aim of matching them with the most suitable groups and events based on the results.

[0771] Program processing

[0772] First, the user creates a basic profile through the device. Once the basic information is entered, the device activates voice input and facial recognition camera, instructing the emotion engine to analyze the user's emotions. The analyzed emotion information is added to the profile and sent to the server.

[0773] The server stores the received profile data in a database and begins analysis using AI algorithms and an emotion engine. It generates tags containing emotion data from the profile and lists other users suitable for similar emotional states and activities as matching candidates.

[0774] Next, the server searches the event database and selects events and groups that match the user's emotional state. Based on this emotional information, it recommends cultural workshops if relaxation is desired, and sports events if energy is needed.

[0775] Next, the server sends filtered event information to the device. The device then presents the event information to the user via push notification, allowing them to view specific activity details within the app. The user selects an event they are interested in and registers to participate.

[0776] During the event, real-time sentiment analysis of the user is performed through the device, and communication support functions with other participants are utilized to support conversations that are appropriate to the user's emotional state.

[0777] After the event ends, the server requests feedback from users. It sends a feedback form that combines data from the emotion engine, and uses the feedback to recommend future events and improve the service.

[0778] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion engine will recommend a relaxing book club and suggest participation. In this way, the system supports the building of comfortable and meaningful relationships by providing activities tailored to the user's emotional state.

[0779] The following describes the processing flow.

[0780] Step 1:

[0781] The user creates a profile through the device. Specifically, they enter their name, age, hobbies, and interests into the application's form and click the "Next" button. After the profile is created, the device activates the voice input function and camera and prompts the user to express their emotions.

[0782] Step 2:

[0783] The device transmits data acquired through voice input and the camera to the emotion engine. The emotion engine analyzes voice tone and facial expression data to identify the user's primary emotional state. For example, it can determine states such as stress, joy, and excitement.

[0784] Step 3:

[0785] Once the user's emotional information is identified, the device sends the results to the server, where they are stored in a database along with the profile data.

[0786] Step 4:

[0787] The server begins analyzing the stored profile data. An AI algorithm generates tags based on the user's interests, experiences, and emotional data, and identifies other users with similar tags. This then lists potential matches based on emotions and interests.

[0788] Step 5:

[0789] The server consults the event database to select the event or group best suited to the user's emotional state. During selection, it considers the user's emotional state, choosing a mindfulness workshop if relaxation is needed, or a hiking event for those feeling more active.

[0790] Step 6:

[0791] The server sends information about selected groups and events to the terminal. Based on the received information, the terminal displays the details of the event or group on the user's screen and notifies them using the notification function.

[0792] Step 7:

[0793] Users check notifications on their devices, select events or groups that interest them, and register to participate. Once registration is complete, the device sends the participation information to the server and displays a confirmation message to the user.

[0794] Step 8:

[0795] While users are participating, the device performs real-time sentiment analysis and provides feedback and advice to stimulate communication among users.

[0796] Step 9:

[0797] After the activity ends, the server sends a feedback form to the user. It generates feedback using data from the emotion engine and collects responses from the user. The server analyzes this feedback and uses it to guide future activities and improve the service.

[0798] (Example 2)

[0799] 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".

[0800] Modern seniors and retirees often face feelings of isolation and weakened social connections, making it difficult for them to find new social links. Addressing these challenges and providing appropriate activities and groups tailored to individual emotional states is crucial for improving their social well-being.

[0801] 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.

[0802] In this invention, the server includes means for acquiring user information and storing said information as a data structure, means for analyzing the data structure using an emotion analysis device and matching it with other participants who have similar emotional states or behaviors, and means for selecting candidate groups or activities and providing them to the user. This makes it possible to provide optimal social interaction opportunities that match the emotional state of each individual user.

[0803] "User information" refers to basic data about individual participants that is entered into the system, including name, age, hobbies, and emotional state.

[0804] A "data structure" refers to a collection of information used to store and manage user information and analysis results, and is a format that allows computers to process data efficiently.

[0805] An "emotion analysis device" refers to a processing system or algorithm used to analyze voice and facial expression data acquired from a user and determine their emotional state.

[0806] "Matching" refers to the process of selecting appropriate other participants and activities based on the user's emotional state, interests, and activity patterns.

[0807] A "group or activity" refers to an event or group of participants proposed by the system for the purpose of social interaction, providing a space for users to build new relationships through their participation.

[0808] "Electronic notification function" refers to a technology that instantly delivers information via a terminal, and is a means of quickly and efficiently informing users of the information being provided.

[0809] A "generative AI model" is a model that has an algorithm that learns from large amounts of data to recognize and analyze complex patterns, and is particularly used for analysis based on user emotions and interests.

[0810] This invention is a system designed to help elderly people and retirees build new social connections and prevent isolation. The system aims to suggest appropriate social interaction activities based on the user's emotional state.

[0811] The user creates a basic profile using the device. The device displays screens for entering information such as name, age, hobbies, and areas of interest. Once the user's basic information is entered, the device activates voice input and facial recognition camera functions.

[0812] The emotion analysis device collects emotional data from the user's voice and facial expressions via the terminal and uses a generative AI model to analyze complex emotional patterns. The analysis results are added to the user's profile and sent to the server.

[0813] The server stores the received profile data in a database and performs analysis using AI algorithms and sentiment analysis devices. Based on the sentiment data included in the profile, it selects other participants with similar emotional states and activities as matching candidates. It also searches the event database to identify groups and activities that match the user's emotional state.

[0814] The selected information is sent to the terminal, and the user immediately receives event information via electronic notification. Users register to participate in activities that interest them, and during the activity, the interaction is optimized through real-time sentiment analysis. After the activity ends, the server collects feedback from the user and uses it to make suggestions for the next event.

[0815] For example, if the system analyzes that a user is experiencing stress during profile creation, the emotion analysis device will recommend and suggest participation in a relaxing book club. This helps in building comfortable and meaningful relationships.

[0816] An example of a prompt message might be, "Design an AI service that suggests the optimal event based on the analysis results."

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1:

[0819] The user operates the device to enter basic profile information. This information includes name, age, hobbies, and areas of interest. This data is temporarily stored as a data structure within the device.

[0820] Step 2:

[0821] The device activates its voice input function and facial recognition camera. The user's voice and facial data are input to the emotion analysis device. By collecting this data, the user's emotions are detected in real time, and a generative AI model analyzes this data to output the emotional state.

[0822] Step 3:

[0823] The device adds the analyzed emotional state to the user's profile and sends the profile data to the server. The input here is the result of the emotional analysis, and the output is the transmission of the profile data to the server.

[0824] Step 4:

[0825] The server stores the received profile data in a database. This data includes the user's basic information and emotional state. Based on the stored data, an AI algorithm and an emotion analysis device are used to analyze the user's state and output other participants with similar emotional states or activity patterns.

[0826] Step 5:

[0827] The server searches the event database to select groups and activities that match the user's emotional state. The server receives emotional data as input, outputs events that match relaxation or vitality, and sends the selection results to the terminal.

[0828] Step 6:

[0829] The terminal receives event information sent from the server and presents it to the user using an electronic notification function. The user reviews the event information and selects activities of interest. The input is the event information, and the output is the user's selections.

[0830] Step 7:

[0831] While the user participates in the event, the device analyzes the user's emotions in real time and optimizes the interaction based on the results. In this process, emotion analysis data is used as input, and the optimized interaction method is output.

[0832] Step 8:

[0833] After the event ends, the server requests feedback. Users input their feedback via their terminals and send it to the server. This feedback is used to make suggestions for future events and improve the service. The input is feedback information, and the output is adjustments to the next recommended event.

[0834] (Application Example 2)

[0835] 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".

[0836] Retired seniors often experience a weakening of social connections and feelings of isolation. In this situation, there is a need to support them in effectively participating in new communities and building comfortable and meaningful relationships through activities that align with their emotional state. However, conventional systems struggle to match individuals with appropriate emotional states, making it difficult to provide personalized experiences that meet user needs.

[0837] 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.

[0838] In this invention, the server includes means for acquiring user data and storing said data as a profile, means for analyzing the profile data and emotional information and matching it with other users who have similar interests or experiences and emotional states, and means for selecting candidate groups or events and providing them to the user. This makes it possible to encourage optimal community participation while taking into account the user's emotional state and to form social connections that meet the user's individual needs.

[0839] "User data" refers to detailed information about a user, including basic information, activity history, and emotional information.

[0840] A "profile" is a dataset created based on user data that shows the characteristics and attributes of a user.

[0841] "Emotional information" refers to data obtained by analyzing the user's emotions and psychological state.

[0842] "Matching" is the process of connecting users who have similar interests, experiences, or emotional states.

[0843] A "group or event" refers to a collective activity or event intended for user participation.

[0844] "Real-time sentiment analysis" refers to a technology that instantly analyzes a user's emotional state during a group activity.

[0845] "Feedback" is the process of collecting users' feelings and opinions about an activity.

[0846] "Push notifications" refer to a method of sending information directly to a user's device to notify them.

[0847] "Communication support" refers to a function designed to facilitate smooth communication between users.

[0848] The system for implementing this invention is built using a terminal such as a smartphone and a cloud-based server. The user first installs a dedicated application on their smartphone and creates a basic profile. During this process, emotional information can be input using a camera equipped with voice input and facial recognition capabilities. The terminal utilizes software libraries such as Google Cloud Speech-to-Text API and OpenCV for voice recognition and image processing.

[0849] The acquired user data is sent to the server as profile data and sentiment information. The server stores this data in a database such as MySQL and analyzes it using a generative AI model based on TensorFlow. Based on the analysis results, other users, groups, and events that match the user's interests and sentiment state are matched.

[0850] The server sends event information tailored to the user's needs to their device via push notifications. During the event, the server performs real-time sentiment analysis and supports smooth conversations through its communication support function.

[0851] For example, if the system analyzes that a user is experiencing stress while creating their profile, the emotion engine will recommend an online book club as a relaxing activity and notify the user of the participation invitation. In this way, the system provides users with experiences tailored to their individual needs and helps them build social connections.

[0852] An example of a prompt message would be: "This user recently left their job and is experiencing stress. What kind of relaxation event should we suggest?"

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] The user launches a smartphone application and creates a basic profile. Inputs include the user's name, age, interests, and past experiences. This information is then saved as profile data.

[0856] Step 2:

[0857] To collect user emotion data, the device's camera and microphone are activated. The user's facial expressions and voice tone are captured as input, and this data is analyzed by an emotion analysis engine as output. The analysis results are added to the profile as emotion information.

[0858] Step 3:

[0859] The device sends profile data and emotion information to the server. The server receives this data as input and stores it in its database. As output, a detailed user profile is completed.

[0860] Step 4:

[0861] The server launches a generative AI model using TensorFlow to analyze profile data. As input, it generates tags based on interests, experiences, and emotional states, using stored user data. As output, it lists suitable candidates for other users and events for the user.

[0862] Step 5:

[0863] The server selects the group or event that best matches the user's sentiment information. It uses the generated tags as input and searches the event database. The output determines the most suitable group or event information for the user.

[0864] Step 6:

[0865] The server sends the selected event information to the terminal and sends a push notification. The selected event information is used as input, and the event notification is displayed on the user's terminal as output.

[0866] Step 7:

[0867] Users participate in events and perform real-time sentiment analysis through their devices. Facial and voice data collected during participation are used as input and processed by a sentiment analysis engine. Communication support information is provided as output.

[0868] Step 8:

[0869] After the event ends, the server requests feedback from users. The input consists of collected opinions and feedback from users, which are then used as output for recommending future activities and improving the service.

[0870] 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.

[0871] 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.

[0872] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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."

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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 as being incorporated by reference.

[0891] The following is further disclosed regarding the embodiments described above.

[0892] (Claim 1)

[0893] A means for acquiring user data and saving that data as a profile,

[0894] A means for analyzing the aforementioned profile data and matching it with other users who have similar interests or experiences,

[0895] The means of selecting candidate groups or events and providing them to users,

[0896] A means of notifying users about the group or event being offered and accepting their participation,

[0897] Means to support communication among participants in the aforementioned group or event,

[0898] A means of collecting feedback after the completion of a participating activity and providing support for the next activity,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, characterized in that when analyzing the user data, natural language processing is used to perform the matching based on the profile data.

[0902] (Claim 3)

[0903] The system according to claim 1, characterized in that when transmitting the information of the provided event or group, it notifies the user using push notifications.

[0904] "Example 1"

[0905] (Claim 1)

[0906] A means of obtaining user information from a terminal and saving that information as a profile in a database,

[0907] The aforementioned profile information is analyzed on a server, and artificial intelligence technology is used to generate user interest tags and identify other users with similar characteristics.

[0908] Based on the aforementioned interest tags, the server searches an information database for potential activities and groups and recommends them to the user.

[0909] A means of sending details of the recommended activities or groups to the device via push notification and accepting user registration to participate through the device,

[0910] To support digital communication among the participants in the aforementioned activity, means of providing interaction functions such as chatbots,

[0911] After the aforementioned activity is completed, user feedback will be collected, analyzed using artificial intelligence, and used to make future suggestions and improvements.

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, characterized in that when analyzing the profile information, it uses a natural language processing technique to generate interest tags using a generative AI model and identifies other users with similar characteristics.

[0915] (Claim 3)

[0916] The system according to claim 1, characterized in that when sending detailed information about the recommended activities or groups to the user, it uses push notifications to the terminal.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A means for acquiring user data and saving that data as a profile,

[0920] A means for analyzing the aforementioned profile data and matching it with other users who have similar interests or experiences,

[0921] The means of selecting candidate groups or events and providing them to users,

[0922] A means of notifying users about the group or event being offered and accepting their participation,

[0923] A means to support communication among participants in the aforementioned group or event and enable real-time dialogue,

[0924] A means of collecting feedback after the completion of a participating activity and providing support for the next activity based on said feedback,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, characterized in that when analyzing the user data, natural language processing is used to identify areas of interest based on the profile data and to perform the matching.

[0928] (Claim 3)

[0929] The system according to claim 1, characterized in that when transmitting information about the events or groups provided, it notifies the user using push notifications, and the notifications include access to a real-time chat function to facilitate interaction with other participants.

[0930] "Example 2 of combining an emotion engine"

[0931] (Claim 1)

[0932] A means for acquiring user information and storing that information as a data structure,

[0933] The aforementioned data structure is analyzed using an emotion analysis device, and means are used to match other participants who have similar emotional states or behaviors.

[0934] A means of selecting a candidate group or activity and providing it to the user,

[0935] A means of notifying users about the groups or activities offered and accepting their requests to participate,

[0936] Means for supporting information exchange among participants in the aforementioned group or activity,

[0937] A means of collecting feedback after the participation activity has ended and making suggestions for the next activity,

[0938] A means to optimize the ongoing conversation based on real-time sentiment analysis,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, characterized in that when analyzing the user information, it uses a generating AI model to perform the matching based on the data structure.

[0942] (Claim 3)

[0943] The system according to claim 1, characterized in that when transmitting the information of the provided activities or groups, it notifies the user using an electronic notification function.

[0944] "Application example 2 when combining with an emotional engine"

[0945] (Claim 1)

[0946] A means for acquiring user data and saving that data as a profile,

[0947] A means for analyzing the aforementioned profile data and emotional information and matching other users with similar interests or experiences and emotional states,

[0948] The means of selecting candidate groups or events and providing them to users,

[0949] A means of notifying users about the group or event being offered and accepting their participation,

[0950] A means to support communication based on real-time sentiment analysis among participants in the aforementioned group or event,

[0951] A means of collecting feedback, including emotional information, after the completion of a participant activity, and providing support for the next activity,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, characterized in that when analyzing the user data, it performs the matching based on the profile data using a natural language processing and sentiment analysis engine.

[0955] (Claim 3)

[0956] The system according to claim 1, characterized in that when transmitting information about the events or groups provided, it notifies the user using push notifications and provides real-time feedback. [Explanation of Symbols]

[0957] 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 for acquiring user data and saving that data as a profile, A means for analyzing the aforementioned profile data and matching it with other users who have similar interests or experiences, The means of selecting candidate groups or events and providing them to users, A means of notifying users about the group or event being offered and accepting their participation, Means to support communication among participants in the aforementioned group or event, A means of collecting feedback after the completion of a participating activity and providing support for the next activity, A system that includes this.

2. The system according to claim 1, characterized in that when analyzing the user data, natural language processing is used to perform the matching based on the profile data.

3. The system according to claim 1, characterized in that when transmitting the information of the provided event or group, it notifies the user using push notifications.

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