system

Abstract

We provide the system. [Solution] A means of collecting and organizing regional information from multiple data sources, A means of creating characteristic information for each user and managing interest information, means for generating personalized communication based on the aforementioned characteristic information, A means for transmitting the generated communication to an information terminal, A means of collecting and analyzing user evaluation information, A means of providing real-time communication to support participation in community activities, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Information regarding local community activities and volunteer activities is diverse and scattered, so there is a problem that interested residents cannot timely find appropriate activities. There is also a problem that it takes time for new residents to obtain information and opportunities to integrate into the local area. These problems hinder participation in local activities and cause smooth communication among residents not to proceed.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides means for collecting and organizing local information from multiple sources, thereby enabling centralized information management. Furthermore, by providing means for creating profiles for each user and managing their interests, it enables the provision of information based on the interests of each resident. In addition, it provides means for generating personalized notifications based on profiles and providing the latest information in a form that is easy for residents to understand by using natural language processing technology. The system configuration, which includes means for notifying user terminals and collecting and analyzing feedback, can improve the overall quality of activities in the region and promote resident involvement.

[0006] "Local information" refers to data such as events, volunteer activities, and public announcements related to a specific region.

[0007] "Information sources" refer to digital or physical sources such as websites, social media, news, and public relations materials used to obtain local information.

[0008] A "profile" refers to a collection of individual information created based on a user's interests, preferences, past participation history, etc.

[0009] "Interest information" refers to information related to community activities and events that users are likely to be particularly interested in.

[0010] "Personalized notifications" refer to informational messages that are individually customized based on the user's profile.

[0011] "Natural language processing technology" refers to the technology that enables computers to understand and appropriately generate human language.

[0012] A "user terminal" refers to a device owned by a user that is used to receive notifications and send information.

[0013] "Feedback" refers to information such as evaluations and opinions provided by users after an activity has taken place.

[0014] "Analysis" refers to the act of systematically evaluating collected data and using that information to determine future activities and improvement measures. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, a 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.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is an information system designed to facilitate local community activities and volunteer work, efficiently collecting local information and providing it to users in an appropriate format. It allows for continuous improvement based on user feedback.

[0037] The system primarily consists of a server, user terminals, and the network connecting them. The server centrally manages regional information, generates personalized notifications for each user, and sends them to the user terminals.

[0038] The server automatically collects information on events and volunteer activities from multiple sources. The information is stored in a database in a specific format, and each event is tagged to indicate which category it belongs to.

[0039] On the other hand, users generate a profile by entering their interests and preferred locations using their device during their first use. This data is sent to the server, where it is combined with other information to create the profile.

[0040] Based on this profile, the server periodically extracts relevant local event information that is likely to interest the user and generates personalized notifications. These notifications utilize natural language processing technology to present information in a way that the user can intuitively understand.

[0041] These notifications are displayed on the user's device in real time, allowing the user to access new activity information upon receiving the notification. If a user wishes to participate, they can submit a participation request to the server via their device, completing the participation process.

[0042] After the event ends, users submit feedback about their experience using their devices. This feedback information is collected on the server and used as data to improve the quality of future events.

[0043] As a concrete example, consider a local cleanup activity. The server collects information about the next cleanup activity from the local environmental organization's website and generates a notification for interested users saying, "Would you like to participate in the next local cleanup event?" When a user indicates their interest in participating, the server immediately processes the participation request, allowing the user to join the activity. In this way, centralized information management and personalized information provision to users enable efficient participation in local activities.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server automatically collects event information from local sources. Specifically, it uses web scraping and APIs to retrieve data from websites, social media, and local news, and stores it in a database.

[0047] Step 2:

[0048] The user initiates the registration process through their device. They create a personalized interest profile by entering their interests, preferred locations, and available days and times. This information is then sent to the server.

[0049] Step 3:

[0050] The server updates the user's profile based on the data it receives. The profile is tagged according to the user's interests and past participation experience, forming the basis for generating personalized notifications.

[0051] Step 4:

[0052] The server periodically extracts the most relevant events for each user based on their profile information and generates notification messages. Natural language processing techniques are used to create messages that are easy for users to understand.

[0053] Step 5:

[0054] The generated notifications are sent to the user's device and displayed to the user in real time. The user can check notifications of interest and view detailed information.

[0055] Step 6:

[0056] When a user indicates their intention to participate, a participation request is sent from their device to the server. The server updates its database and registers them as an event participant.

[0057] Step 7:

[0058] After the event ends, the server sends a feedback form to the user's device. The user fills out and submits this form, and the server collects the feedback.

[0059] Step 8:

[0060] The server analyzes the collected feedback and extracts information to help improve future events and services. This provides data to enhance the quality of community activities.

[0061] (Example 1)

[0062] 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."

[0063] To promote participation in local community and volunteer activities and to provide information optimized for each individual, it is necessary to efficiently collect vast amounts of local information and provide information based on users' interests. However, conventional systems are insufficient in organizing and personalizing information, and do not provide an attractive participation experience for many users. Furthermore, there is a challenge in that mechanisms for effectively utilizing user feedback are not in place.

[0064] 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.

[0065] In this invention, the server includes means for acquiring and classifying local information from multiple sources, means for creating summaries for each user and managing interest information, and means for recording the user's behavior history and generating personalized participation recommendation information. This makes it possible to provide local information optimized for each user and promotes participation in local community activities.

[0066] "Local information" refers to information such as events, volunteer activities, and news related to a specific region, and is collected from a number of sources.

[0067] "Information sources" refer to media that provide local information, such as websites, social media, newsletters, and materials published by local organizations.

[0068] "Classifying" refers to the process of organizing and systematizing collected regional information based on themes and categories.

[0069] "Users" refer to people who use the system, who are interested in local activities, and who receive information from it.

[0070] "Overview" refers to the profile information created for each user, which includes information about their interests and local area.

[0071] "Means" refers to the methods and processes used to achieve an objective, and in this invention in particular, it refers to technical methods for collecting, organizing, and distributing information.

[0072] "Personalized notifications" refer to messages that are optimized based on the user's interests and preferences, and are generated to present information that is highly relevant to the user.

[0073] "Feedback" refers to opinions from users about the system and evaluations of activities they participated in, which are used to improve the system.

[0074] "Activity history" refers to data that records past activities and participation information performed by users through the system.

[0075] "Recommended participation information" refers to information that indicates local events and activities that users are recommended to participate in, based on their interests and past activity history.

[0076] The system of this invention is designed to effectively collect information on local community activities and volunteer work, and to provide users with optimized information. The system mainly consists of servers, terminals, and a network connecting them.

[0077] server:

[0078] The server utilizes web crawlers and scraping tools to collect information about local events and volunteer activities. Data analysis platforms and APIs may also be used. The collected information is stored in a database in a specific format and tagged by category using natural language processing techniques. Categories may include, for example, "environment," "education," and "culture."

[0079] The server also manages user profiles. These profiles are generated based on the activities and locations that users have shown interest in, and they form the foundation for accurate personalization.

[0080] Terminal:

[0081] The devices used by users are smartphones, personal computers, and other devices used to input and retrieve information through the user interface. Upon first use, a profile is created by inputting activities and regions of interest through the device and sending this information to the server.

[0082] The device also receives personalized notifications sent from the server, allowing users to access new activity information in real time. Furthermore, it makes it easy to register for events and submit feedback using the device.

[0083] Examples of specific cases and prompt statements:

[0084] For example, a server can collect information about upcoming cleanup activities from a local environmental organization's website and generate a notification for users asking, "Would you like to participate in the next local cleanup event?" If a user is interested in this notification and wishes to participate, they can immediately complete the participation process via their device.

[0085] An example of a prompt message might be: "Generate a notification to encourage participation in local community activities. Describe an activity that users are likely to be interested in, as follows: 'Would you like to participate in the next community cleanup event?'"

[0086] This will enable the efficient promotion of local community activities and provide a user-friendly environment for participation.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server collects local information using web crawlers and scraping tools. The data obtained from each information source is retrieved in JSON or CSV format and temporarily stored on the server. Because this input data has various formats and structures, it is pre-processed as needed and formatted into a unified data format. The output is a formatted dataset containing event dates, locations, content, categories, and other information.

[0090] Step 2:

[0091] The server uses natural language processing techniques to categorize the formatted regional information dataset. Specifically, it uses a text analysis algorithm to extract keywords from event summaries and then uses these keywords to determine categories (e.g., environment, education, culture). The input data consists of event information formatted in a unified format, and the output is a dataset with added category information.

[0092] Step 3:

[0093] The user accesses a form using their device to enter their interests and preferred locations. This input consists of selections of categories and locations the user is interested in. This information is sent to a server, which generates a user profile based on it and stores it in a database. The output is a personalized user profile.

[0094] Step 4:

[0095] The server references each user profile and extracts relevant local event information. It uses past participation history data and profile information stored on the server as input data. A generative AI model automatically selects events likely to interest the user and generates notification messages in natural language. The output is a personalized notification sent to the user.

[0096] Step 5:

[0097] The device receives personalized notifications sent from the server and displays them as push notifications. The user checks the notification and, if there is an event of interest, accesses the details page and clicks the "Join" button. The input at this time is the user's intention to participate, and based on this, the server performs the registration process. As output, a participation completion notification is sent to the user.

[0098] Step 6:

[0099] After the event ends, users submit feedback via their devices. This feedback includes user opinions on event satisfaction and areas for improvement. The server aggregates the received feedback and performs data analysis to inform notifications for future local events. The input data is user feedback, and the output provides insights that can help improve future events and encourage participation.

[0100] (Application Example 1)

[0101] 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."

[0102] This invention aims to solve the problem of cumbersome information gathering and participation processes in local communities and volunteer activities. Conventional systems have difficulty efficiently obtaining information that residents need to actively participate in community activities, and furthermore, feedback after participation is not adequately utilized for improvement in the future.

[0103] 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.

[0104] In this invention, the server includes means for collecting and organizing local information from multiple data sources, means for creating characteristic information for each user and managing interest information, and means for providing real-time communication to support participation in local activities. This makes it possible for residents to easily obtain information on activities they wish to participate in and easily send feedback.

[0105] "Local information" refers to data about events and activities within a local community that is available to residents.

[0106] "Data sources" is a general term for various media and platforms that provide local information, and refers to the sources from which digitized information is obtained.

[0107] "Characteristic information" refers to individual profile information created based on a user's interests, preferences, and past behavioral history.

[0108] "Communication" refers to the notification of information that is generated taking into account the user's characteristics and is directed at an individual target.

[0109] An "information terminal" is an electronic device used to receive communications and transmit them to the user, and includes smartphones and personal computers.

[0110] "Evaluation information" refers to feedback provided by users after an activity, and the data necessary for improving that activity.

[0111] "Analysis" refers to the process of analyzing collected evaluation information to improve the quality of future activities.

[0112] "Providing real-time communication" refers to a method of promoting participation in activities by immediately providing users with information they are interested in.

[0113] To implement this invention, a server for efficiently collecting and organizing local digital information and a terminal for receiving information and interacting with users are required. This system is equipped with a program that accesses multiple data sources providing local information and performs web scraping using Python scripts. As a result, the server automatically acquires event information and stores it as structured data in a database system.

[0114] The server utilizes the Flask framework to generate and update user profile information, managing information of user interest as characteristic data. Based on the collected data and user profiles, it uses AWS® Lambda and natural language processing technology to generate and send personalized communications in real time.

[0115] User terminals are primarily information terminals such as smartphones, which receive communications sent from the server via the Twilio API and provide notifications to users. These notifications support actions such as expressing interest in participating or sending feedback, encouraging participation in activities through the user interface.

[0116] User feedback is analyzed on the server using the SciKit-learn library and stored as information to improve future activities. A concrete example is a local cleanup activity. In this case, the user receives a notification asking, "Would you like to participate in the next local cleanup event?", and by expressing their interest, the participation process is automatically completed.

[0117] An example of a prompt using a generative AI model is: "Please explain the techniques for a program that retrieves local event information and generates customized notifications based on the user profile."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server collects local information from data sources using web scraping techniques. Specifically, it uses Python to retrieve local event and activity information from websites. The raw data obtained is then organized and transformed using the Pandas library and stored in a database. The input is HTML data from websites, and the output is organized structured data.

[0121] Step 2:

[0122] Users register their interests and preferred locations using their device. This generates a user profile. The user's interests and preferences are entered into the device as input and sent to the server. The output is the updated user profile information, which is processed using the Flask framework.

[0123] Step 3:

[0124] The server uses AWS Lambda to periodically analyze user profiles and regional information in the database to generate personalized communications. Natural language processing techniques are used in this process to create notifications in a format that users can intuitively understand. The input is the user profile and regional information, and the output is the generated communication content.

[0125] Step 4:

[0126] The device receives notifications sent from the server via the Twilio API and displays them to the user. The user can review these notifications, express interest in participating in activities, or view more details. The input is notification data from the server, and the output is a visual notification display on the user's device.

[0127] Step 5:

[0128] Users send feedback from their devices to the server. The server then uses SciKit-learn to analyze the feedback data and extract information that can be used to improve future activities. The input is user feedback information, and the output is the analyzed insights.

[0129] 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.

[0130] This invention is a local community activity support system that combines an emotion engine that recognizes user emotions. The emotion engine extracts emotions from user input and interactions and reflects them in a profile, thereby providing a more personalized experience.

[0131] The system consists of a server, user terminals, a network, and an emotion engine. The server collects event information from local sources and stores it in a database. In addition, it utilizes the emotion engine to determine the user's emotional state and incorporate it into a profile.

[0132] Users communicate and provide feedback using their devices. The emotion engine analyzes the user's emotions from text input and voice data and sends the data to the server. The server then adds emotion tags to the user's profile based on this analysis, which are used later for generating notifications and event participation.

[0133] In generating notifications, the server creates wording that takes emotional information into account. For example, if a user is feeling stressed, notifications for refreshing events will be prioritized, allowing for personalized suggestions.

[0134] As a concrete example, when a user sends feedback after an event, the emotion engine identifies that emotion and provides the emotion data to the server along with feedback such as "fun" or "tired." The server then analyzes this data and uses it to plan future events.

[0135] In this way, by using an emotion engine, we can improve the user experience and provide a model that balances effective participation in local activities with improving the quality of those activities through feedback.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server collects event information from local sources and stores it in a database. During this process, it also records attribute information such as event type, date and time, and location, using data obtained through web scraping and APIs.

[0139] Step 2:

[0140] Users create a profile using their device. Here, users specify their preferred event categories, geographical location, and available times. The device then sends this information to the server.

[0141] Step 3:

[0142] The emotion engine analyzes the user's text input and voice data to determine their emotional state. The emotion engine generates emotion tags such as "joy," "sadness," and "stress," and sends them to the server.

[0143] Step 4:

[0144] The server adds the received emotional information to the user profile. This profile includes the user's interests and behavioral patterns, as well as their most recent emotional state.

[0145] Step 5:

[0146] The server selects event information relevant to the user based on their profile. In particular, it prioritizes events that align with the user's emotional state to generate personalized notifications.

[0147] Step 6:

[0148] The generated notification is sent to the user's device in real time. The device displays this notification to the user, encouraging them to participate in the event.

[0149] Step 7:

[0150] If a user checks the notification and wishes to participate in the event, they send a participation request from their device to the server. The server updates the participation information and registers the user as an event participant.

[0151] Step 8:

[0152] After participating in the event, the server sends a feedback form to the user's device. The device collects feedback and sentiment data from the user through this form and sends it back to the server.

[0153] Step 9:

[0154] The server analyzes the emotional data obtained along with the feedback, and uses it to improve the activities. This information is reflected in decision-making for future event planning, contributing to the overall improvement of the quality of activities.

[0155] (Example 2)

[0156] 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 will be referred to as the "terminal."

[0157] In local community activities, it is essential to understand the individual feelings and interests of each participant and effectively provide information tailored to them. Traditional information delivery methods have been limited to general information dissemination, making it difficult to address the specific needs and emotional states of users. This has resulted in challenges such as decreased motivation and satisfaction with community activities.

[0158] 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.

[0159] In this invention, the server includes means for collecting and organizing local information from multiple sources, means for analyzing user sentiment data and updating the profile, and means for generating personalized notifications based on the profile. This makes it possible to provide customized information that is tailored to each individual's emotions and interests.

[0160] "Local information" refers to information such as events, activities, and news related to a specific region, and is collected through multiple sources.

[0161] "Personal data" refers to specific information related to each user, including the user's profile, preferences, and past behavioral history.

[0162] "Emotional data" refers to information that indicates the user's emotional state, which is analyzed by the emotion engine and reflected in the profile.

[0163] A "profile" is a data storage system set up for each user, managing user-specific information including emotional data and interest information.

[0164] "Language processing technology" refers to the technology of analyzing and processing natural language, and is used for generating notification information and interpreting user input data.

[0165] A "notification" is an informational message provided to a user, which is personalized and sent under specific conditions.

[0166] "Opinions" refer to feedback and comments collected from users, which are used to improve the system and optimize events.

[0167] The system of this invention provides personalized information to support community activities in a local area. The system consists of a data processing server, a user terminal, a network, and an emotion analysis engine.

[0168] The server collects local information from multiple sources and stores it in a database. This data collection includes common web scraping techniques and the use of APIs. The server also manages user-defined profiles and updates personal data, including sentiment data. This profile management utilizes a cloud-based database.

[0169] A terminal is a device used by users to interact with the system, such as a smartphone or personal computer. Users use the terminal to input text or voice, and this information is pre-processed locally before being sent to the server. In particular, text data is analyzed using natural language processing techniques, and sentiment data is extracted.

[0170] Users can receive more personalized information and notifications by providing their emotional state to the system. Based on the emotional information analyzed from the user's input, the server generates customized event information and notifications and sends them to the user's device.

[0171] For example, if a user submits feedback stating they were "very satisfied" after attending an event, this sentiment information is analyzed by the server and used to recommend future events. The system reflects this in the user's profile, ensuring that similar events are recommended again in the future.

[0172] Examples of prompts include the following:

[0173] "Design a system that analyzes emotional data entered by users daily and suggests local events based on the results. For example, if a user enters 'I felt stressed today,' explain how the system would suggest appropriate events (such as yoga or nature walks)."

[0174] In this way, this system enhances the user experience and promotes effective participation in community activities.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server collects local information from multiple sources. Specifically, it obtains data through web scraping and APIs. The input data consists of local event information and news. This data is then processed and stored in a database. The output is a database of organized event information.

[0178] Step 2:

[0179] The user terminal receives user input. The user inputs their emotions and opinions as text or voice. The input is data related to the user's emotions and opinions. The terminal preprocesses the data and converts it into a format that can be sent to the server. The output is data prepared for sentiment analysis.

[0180] Step 3:

[0181] The server receives the transmitted emotion data and analyzes it using the emotion engine. This analysis identifies the user's emotional state. The input is data containing emotions sent by the user. Based on the analysis, the emotional state is updated in the profile and emotion tags are added. The output of this step is the updated user profile.

[0182] Step 4:

[0183] The server generates personalized notifications based on the updated profile. Input data includes user profile information and local event information. Based on this information, natural language processing techniques are used to create event notifications tailored to the user. The output is a customized notification sent to the user.

[0184] Step 5:

[0185] The server sends the generated notification to the user's terminal. The notification contains event information based on the user's emotional state and interests. The input is the generated notification message. The output is the notification displayed on the user's terminal.

[0186] Step 6:

[0187] Users check notifications, participate in events, and then provide feedback. They re-enter their thoughts and opinions into their devices. The input is user feedback data. This information is sent back to the server and used for planning future events. The output is the feedback information that will be reflected in planning future events.

[0188] (Application Example 2)

[0189] 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".

[0190] In local community activities, there is a challenge in improving participation rates and user satisfaction due to a lack of information tailored to participants' emotions and interests. Furthermore, there is a need for a system that can grasp individual emotional states in real time and provide guidance based on that information.

[0191] 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.

[0192] In this invention, the server includes means for collecting and organizing local information from multiple sources; means for creating a profile for each individual user and controlling their interests; means for generating personalized guidance based on the profile and the user's emotional state; means for analyzing the user's emotions using an emotion recognition algorithm; means for sending suggestions to the user's terminal based on the analyzed emotional information; and means for collecting and analyzing user feedback and reflecting it in future event planning. This makes it possible to promote participation in local community activities and provide information tailored to each individual user.

[0193] "Local information" refers to information about events, news, and activities in a specific region.

[0194] "Information sources" refer to a variety of sources for obtaining local information, such as news websites, local government announcements, and community message boards.

[0195] A "profile" is a structure for storing information in a database, including a user's interests, concerns, emotional state, and past participation history.

[0196] "Interest information" refers to information about activities and themes that users enjoy.

[0197] "Information" refers to announcements about events and activities provided to users.

[0198] An "emotion recognition algorithm" is a program that analyzes emotions from a user's text input or voice data.

[0199] "Analysis" is a method of breaking down data to understand its meaning and trends.

[0200] "Opinions" refer to feedback and evaluations regarding events and activities provided by users.

[0201] A "proposal" is an announcement of an event or activity optimized for a specific user.

[0202] "Real-time" refers to a state where data collection, analysis, and notification occur almost simultaneously.

[0203] To implement this invention, a system comprising a server, user terminals, a network, and an emotion recognition algorithm is used. The server is responsible for collecting event information from local information sources and storing it in a database. The server also creates individual user profiles and manages information on interests. This makes it possible to generate personalized guidance and analyze the user's emotions using the emotion recognition algorithm.

[0204] The application is installed on smartphones and smart glasses and performs real-time sentiment analysis. Based on the analysis results, it sends suggestions for events best suited to the user's device. Specifically, it uses Python to run natural language processing libraries (e.g., NLTK and TextBlob) to extract sentiment. The frontend uses React Native to create the user interface and is designed to facilitate user feedback.

[0205] When users provide feedback via text or voice input after attending an event, the device sends this data to an emotion analysis algorithm, which then sends the analysis results to a server. This allows the feedback to be used to suggest future events. For example, if a user says, "I really enjoyed this event," the emotion recognition algorithm classifies this as positive, and the server prioritizes activities that evoke positive emotions when suggesting future events.

[0206] For example, if a user enters "I'm tired today," the system will detect stress and suggest relaxation events.

[0207] Example of a prompt:

[0208] User: "I'm tired today."

[0209] Emotion Engine: Recognizes the user's emotions as "stress."

[0210] App: Sends a notification saying, "We'll show you nearby events that are perfect for relieving stress!"

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The server automatically collects event information from local sources. This information includes the event name, date and time, location, and organizer information. The collected data is organized and stored in a database on the server.

[0214] Step 2:

[0215] Users input information into the device, including their interests, concerns, and emotional state. This input data is collected by the device and sent to an emotion recognition algorithm. The transmitted data is processed and reflected in each user's profile.

[0216] Step 3:

[0217] The device uses an emotion recognition algorithm to analyze emotions from user input. Specifically, it analyzes text or voice data using a natural language processing library (e.g., NLTK, TextBlob) and extracts emotion tags. These emotion tags are added to the user's profile.

[0218] Step 4:

[0219] The server generates event recommendations optimized for the user based on updated profile and sentiment information. This process compares collected local event information with the profile and selects events that are appropriate for the user's sentiment state.

[0220] Step 5:

[0221] The server sends the generated event announcement to the user's terminal. The terminal receives this and displays it as a notification to the user. The user can review the event information and decide whether to participate if they are interested.

[0222] Step 6:

[0223] After participating in an event, users enter their feedback into a device. The device then sends this feedback back to an emotion recognition algorithm for emotional analysis. The analysis results are stored in a database on the server to serve as a reference for suggesting future events.

[0224] Step 7:

[0225] The server then uses the feedback data to further improve the user profile. This step involves analyzing past participation history and sentiment feedback to enable more accurate personalization.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is an information system designed to facilitate local community activities and volunteer work, efficiently collecting local information and providing it to users in an appropriate format. It allows for continuous improvement based on user feedback.

[0243] The system primarily consists of a server, user terminals, and the network connecting them. The server centrally manages regional information, generates personalized notifications for each user, and sends them to the user terminals.

[0244] The server automatically collects information on events and volunteer activities from multiple sources. The information is stored in a database in a specific format, and each event is tagged to indicate which category it belongs to.

[0245] On the other hand, users generate a profile by entering their interests and preferred locations using their device during their first use. This data is sent to the server, where it is combined with other information to create the profile.

[0246] Based on this profile, the server periodically extracts relevant local event information that is likely to interest the user and generates personalized notifications. These notifications utilize natural language processing technology to present information in a way that the user can intuitively understand.

[0247] These notifications are displayed on the user's device in real time, allowing the user to access new activity information upon receiving the notification. If a user wishes to participate, they can submit a participation request to the server via their device, completing the participation process.

[0248] After the event ends, users submit feedback about their experience using their devices. This feedback information is collected on the server and used as data to improve the quality of future events.

[0249] As a concrete example, consider a local cleanup activity. The server collects information about the next cleanup activity from the local environmental organization's website and generates a notification for interested users saying, "Would you like to participate in the next local cleanup event?" When a user indicates their interest in participating, the server immediately processes the participation request, allowing the user to join the activity. In this way, centralized information management and personalized information provision to users enable efficient participation in local activities.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The server automatically collects event information from local sources. Specifically, it uses web scraping and APIs to retrieve data from websites, social media, and local news, and stores it in a database.

[0253] Step 2:

[0254] The user initiates the registration process through their device. They create a personalized interest profile by entering their interests, preferred locations, and available days and times. This information is then sent to the server.

[0255] Step 3:

[0256] The server updates the user's profile based on the data it receives. The profile is tagged according to the user's interests and past participation experience, forming the basis for generating personalized notifications.

[0257] Step 4:

[0258] The server periodically extracts the most relevant events for each user based on their profile information and generates notification messages. Natural language processing techniques are used to create messages that are easy for users to understand.

[0259] Step 5:

[0260] The generated notifications are sent to the user's device and displayed to the user in real time. The user can check notifications of interest and view detailed information.

[0261] Step 6:

[0262] When a user indicates their intention to participate, a participation request is sent from their device to the server. The server updates its database and registers them as an event participant.

[0263] Step 7:

[0264] After the event ends, the server sends a feedback form to the user's device. The user fills out and submits this form, and the server collects the feedback.

[0265] Step 8:

[0266] The server analyzes the collected feedback and extracts information to help improve future events and services. This provides data to enhance the quality of community activities.

[0267] (Example 1)

[0268] 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."

[0269] To promote participation in local community and volunteer activities and to provide information optimized for each individual, it is necessary to efficiently collect vast amounts of local information and provide information based on users' interests. However, conventional systems are insufficient in organizing and personalizing information, and do not provide an attractive participation experience for many users. Furthermore, there is a challenge in that mechanisms for effectively utilizing user feedback are not in place.

[0270] 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.

[0271] In this invention, the server includes means for acquiring and classifying local information from multiple sources, means for creating summaries for each user and managing interest information, and means for recording the user's behavior history and generating personalized participation recommendation information. This makes it possible to provide local information optimized for each user and promotes participation in local community activities.

[0272] "Local information" refers to information such as events, volunteer activities, and news related to a specific region, and is collected from a number of sources.

[0273] "Information sources" refer to media that provide local information, such as websites, social media, newsletters, and materials published by local organizations.

[0274] "Classifying" refers to the process of organizing and systematizing collected regional information based on themes and categories.

[0275] "Users" refer to people who use the system, who are interested in local activities, and who receive information from it.

[0276] "Overview" refers to the profile information created for each user, which includes information about their interests and local area.

[0277] "Means" refers to the methods and processes used to achieve an objective, and in this invention in particular, it refers to technical methods for collecting, organizing, and distributing information.

[0278] "Personalized notifications" refer to messages that are optimized based on the user's interests and preferences, and are generated to present information that is highly relevant to the user.

[0279] "Feedback" refers to opinions from users about the system and evaluations of activities they participated in, which are used to improve the system.

[0280] "Activity history" refers to data that records past activities and participation information performed by users through the system.

[0281] "Recommended participation information" refers to information that indicates local events and activities that users are recommended to participate in, based on their interests and past activity history.

[0282] The system of this invention is for effectively collecting information on local community activities and volunteer activities and providing optimized information to users. The system mainly consists of a server, terminals, and a network connecting them.

[0283] Server:

[0284] The server uses a web crawler and scraping tools to collect information on local events and volunteer activities. In this case, a data analysis platform and API may also be used. The collected information is stored in a database in a certain format and tagged for each category using natural language processing technology. Categories include, for example, "environment", "education", "culture", etc.

[0285] The server also manages user profiles. The profile is generated based on the activities and regions that the user has shown interest in and serves as a basis for achieving accurate personalization.

[0286] Terminal:

[0287] The terminals used by users are devices such as smartphones and personal computers, and are used to input and obtain information through the user interface. At the first use, the user inputs activities and regions of interest through the terminal and sends them to the server to create a profile.

[0288] The terminal also receives personalized notifications sent from the server. As a result, users can access new activity information in real time. Furthermore, the terminal is used to easily apply to participate in events and send feedback.

[0289] Examples of specific cases and prompt sentences:

[0290] For example, a server can collect information about upcoming cleanup activities from a local environmental organization's website and generate a notification for users asking, "Would you like to participate in the next local cleanup event?" If a user is interested in this notification and wishes to participate, they can immediately complete the participation process via their device.

[0291] An example of a prompt message might be: "Generate a notification to encourage participation in local community activities. Describe an activity that users are likely to be interested in, as follows: 'Would you like to participate in the next community cleanup event?'"

[0292] This will enable the efficient promotion of local community activities and provide a user-friendly environment for participation.

[0293] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0294] Step 1:

[0295] The server collects local information using web crawlers and scraping tools. The data obtained from each information source is retrieved in JSON or CSV format and temporarily stored on the server. Because this input data has various formats and structures, it is pre-processed as needed and formatted into a unified data format. The output is a formatted dataset containing event dates, locations, content, categories, and other information.

[0296] Step 2:

[0297] The server uses natural language processing techniques to categorize the formatted regional information dataset. Specifically, it uses a text analysis algorithm to extract keywords from event summaries and then uses these keywords to determine categories (e.g., environment, education, culture). The input data consists of event information formatted in a unified format, and the output is a dataset with added category information.

[0298] Step 3:

[0299] The user accesses a form using their device to enter their interests and preferred locations. This input consists of selections of categories and locations the user is interested in. This information is sent to a server, which generates a user profile based on it and stores it in a database. The output is a personalized user profile.

[0300] Step 4:

[0301] The server references each user profile and extracts relevant local event information. It uses past participation history data and profile information stored on the server as input data. A generative AI model automatically selects events likely to interest the user and generates notification messages in natural language. The output is a personalized notification sent to the user.

[0302] Step 5:

[0303] The device receives personalized notifications sent from the server and displays them as push notifications. The user checks the notification and, if there is an event of interest, accesses the details page and clicks the "Join" button. The input at this time is the user's intention to participate, and based on this, the server performs the registration process. As output, a participation completion notification is sent to the user.

[0304] Step 6:

[0305] After the event ends, the user sends feedback through the terminal. The feedback content includes the user's opinions on the satisfaction and improvement points of the event. The server aggregates the received feedback and performs data analysis for reflecting it in the next regional event notification. The input data is the feedback from the user, and improvement plans and insights useful for promoting participation in the next event can be obtained as output.

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] The present invention aims to solve the problem that the information collection and participation process are complicated in regional communities and volunteer activities. In the conventional system, it is difficult for residents to efficiently obtain information for actively participating in regional activities, and there is also a problem that feedback after participation is difficult to be fully utilized for the next improvement.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0310] In this invention, the server includes means for collecting and organizing regional information from a plurality of data sources, means for creating characteristic information for each user and managing interest information, and means for providing real-time communication to support participation in regional activities. Thereby, it becomes possible for residents to easily obtain activity information they want to participate in and easily send feedback.

[0311] "Regional information" refers to information on events and activities within a regional community and indicates data that can be used by residents.

[0312] "Data sources" is a general term for various media and platforms that provide local information, and refers to the sources from which digitized information is obtained.

[0313] "Characteristic information" refers to individual profile information created based on a user's interests, preferences, and past behavioral history.

[0314] "Communication" refers to the notification of information that is generated taking into account the user's characteristics and is directed at an individual target.

[0315] An "information terminal" is an electronic device used to receive communications and transmit them to the user, and includes smartphones and personal computers.

[0316] "Evaluation information" refers to feedback provided by users after an activity, and the data necessary for improving that activity.

[0317] "Analysis" refers to the process of analyzing collected evaluation information to improve the quality of future activities.

[0318] "Providing real-time communication" refers to a method of promoting participation in activities by immediately providing users with information they are interested in.

[0319] To implement this invention, a server for efficiently collecting and organizing local digital information and a terminal for receiving information and interacting with users are required. This system is equipped with a program that accesses multiple data sources providing local information and performs web scraping using Python scripts. As a result, the server automatically acquires event information and stores it as structured data in a database system.

[0320] The server utilizes the Flask framework to generate and update user profile information, managing information of user interest as characteristic data. Based on the collected data and user profiles, AWS Lambda and natural language processing technologies are used to generate and send personalized communications in real time.

[0321] User terminals are primarily information terminals such as smartphones, which receive communications sent from the server via the Twilio API and provide notifications to users. These notifications support actions such as expressing interest in participating or sending feedback, encouraging participation in activities through the user interface.

[0322] User feedback is analyzed on the server using the SciKit-learn library and stored as information to improve future activities. A concrete example is a local cleanup activity. In this case, the user receives a notification asking, "Would you like to participate in the next local cleanup event?", and by expressing their interest, the participation process is automatically completed.

[0323] An example of a prompt using a generative AI model is: "Please explain the techniques for a program that retrieves local event information and generates customized notifications based on the user profile."

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The server collects local information from data sources using web scraping techniques. Specifically, it uses Python to retrieve local event and activity information from websites. The raw data obtained is then organized and transformed using the Pandas library and stored in a database. The input is HTML data from websites, and the output is organized structured data.

[0327] Step 2:

[0328] Users register their interests and preferred locations using their device. This generates a user profile. The user's interests and preferences are entered into the device as input and sent to the server. The output is the updated user profile information, which is processed using the Flask framework.

[0329] Step 3:

[0330] The server uses AWS Lambda to periodically analyze user profiles and regional information in the database to generate personalized communications. Natural language processing techniques are used in this process to create notifications in a format that users can intuitively understand. The input is the user profile and regional information, and the output is the generated communication content.

[0331] Step 4:

[0332] The device receives notifications sent from the server via the Twilio API and displays them to the user. The user can review these notifications, express interest in participating in activities, or view more details. The input is notification data from the server, and the output is a visual notification display on the user's device.

[0333] Step 5:

[0334] Users send feedback from their devices to the server. The server then uses SciKit-learn to analyze the feedback data and extract information that can be used to improve future activities. The input is user feedback information, and the output is the analyzed insights.

[0335] 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.

[0336] This invention is a local community activity support system that combines an emotion engine that recognizes user emotions. The emotion engine extracts emotions from user input and interactions and reflects them in a profile, thereby providing a more personalized experience.

[0337] The system consists of a server, user terminals, a network, and an emotion engine. The server collects event information from local sources and stores it in a database. In addition, it utilizes the emotion engine to determine the user's emotional state and incorporate it into a profile.

[0338] Users communicate and provide feedback using their devices. The emotion engine analyzes the user's emotions from text input and voice data and sends the data to the server. The server then adds emotion tags to the user's profile based on this analysis, which are used later for generating notifications and event participation.

[0339] In generating notifications, the server creates wording that takes emotional information into account. For example, if a user is feeling stressed, notifications for refreshing events will be prioritized, allowing for personalized suggestions.

[0340] As a concrete example, when a user sends feedback after an event, the emotion engine identifies that emotion and provides the emotion data to the server along with feedback such as "fun" or "tired." The server then analyzes this data and uses it to plan future events.

[0341] In this way, by using an emotion engine, we can improve the user experience and provide a model that balances effective participation in local activities with improving the quality of those activities through feedback.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The server collects event information from local sources and stores it in a database. During this process, it also records attribute information such as event type, date and time, and location, using data obtained through web scraping and APIs.

[0345] Step 2:

[0346] Users create a profile using their device. Here, users specify their preferred event categories, geographical location, and available times. The device then sends this information to the server.

[0347] Step 3:

[0348] The emotion engine analyzes the user's text input and voice data to determine their emotional state. The emotion engine generates emotion tags such as "joy," "sadness," and "stress," and sends them to the server.

[0349] Step 4:

[0350] The server adds the received emotional information to the user profile. This profile includes the user's interests and behavioral patterns, as well as their most recent emotional state.

[0351] Step 5:

[0352] The server selects event information relevant to the user based on their profile. In particular, it prioritizes events that align with the user's emotional state to generate personalized notifications.

[0353] Step 6:

[0354] The generated notification is sent to the user's device in real time. The device displays this notification to the user, encouraging them to participate in the event.

[0355] Step 7:

[0356] If a user checks the notification and wishes to participate in the event, they send a participation request from their device to the server. The server updates the participation information and registers the user as an event participant.

[0357] Step 8:

[0358] After participating in the event, the server sends a feedback form to the user's device. The device collects feedback and sentiment data from the user through this form and sends it back to the server.

[0359] Step 9:

[0360] The server analyzes the emotional data obtained along with the feedback, and uses it to improve the activities. This information is reflected in decision-making for future event planning, contributing to the overall improvement of the quality of activities.

[0361] (Example 2)

[0362] 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".

[0363] In local community activities, it is essential to understand the individual feelings and interests of each participant and effectively provide information tailored to them. Traditional information delivery methods have been limited to general information dissemination, making it difficult to address the specific needs and emotional states of users. This has resulted in challenges such as decreased motivation and satisfaction with community activities.

[0364] 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.

[0365] In this invention, the server includes means for collecting and organizing local information from multiple sources, means for analyzing user sentiment data and updating the profile, and means for generating personalized notifications based on the profile. This makes it possible to provide customized information that is tailored to each individual's emotions and interests.

[0366] "Local information" refers to information such as events, activities, and news related to a specific region, and is collected through multiple sources.

[0367] "Personal data" refers to specific information related to each user, including the user's profile, preferences, and past behavioral history.

[0368] "Emotional data" refers to information that indicates the user's emotional state, which is analyzed by the emotion engine and reflected in the profile.

[0369] A "profile" is a data storage system set up for each user, managing user-specific information including emotional data and interest information.

[0370] "Language processing technology" refers to the technology of analyzing and processing natural language, and is used for generating notification information and interpreting user input data.

[0371] A "notification" is an informational message provided to a user, which is personalized and sent under specific conditions.

[0372] "Opinions" refer to feedback and comments collected from users, which are used to improve the system and optimize events.

[0373] The system of this invention provides personalized information to support community activities in a local area. The system consists of a data processing server, a user terminal, a network, and an emotion analysis engine.

[0374] The server collects local information from multiple sources and stores it in a database. This data collection includes common web scraping techniques and the use of APIs. The server also manages user-defined profiles and updates personal data, including sentiment data. This profile management utilizes a cloud-based database.

[0375] A terminal is a device used by users to interact with the system, such as a smartphone or personal computer. Users use the terminal to input text or voice, and this information is pre-processed locally before being sent to the server. In particular, text data is analyzed using natural language processing techniques, and sentiment data is extracted.

[0376] Users can receive more personalized information and notifications by providing their emotional state to the system. Based on the emotional information analyzed from the user's input, the server generates customized event information and notifications and sends them to the user's device.

[0377] For example, if a user submits feedback stating they were "very satisfied" after attending an event, this sentiment information is analyzed by the server and used to recommend future events. The system reflects this in the user's profile, ensuring that similar events are recommended again in the future.

[0378] Examples of prompts include the following:

[0379] "Design a system that analyzes emotional data entered by users daily and suggests local events based on the results. For example, if a user enters 'I felt stressed today,' explain how the system would suggest appropriate events (such as yoga or nature walks)."

[0380] In this way, this system enhances the user experience and promotes effective participation in community activities.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The server collects local information from multiple sources. Specifically, it obtains data through web scraping and APIs. The input data consists of local event information and news. This data is then processed and stored in a database. The output is a database of organized event information.

[0384] Step 2:

[0385] The user terminal receives user input. The user inputs their emotions and opinions as text or voice. The input is data related to the user's emotions and opinions. The terminal preprocesses the data and converts it into a format that can be sent to the server. The output is data prepared for sentiment analysis.

[0386] Step 3:

[0387] The server receives the transmitted emotion data and analyzes it using the emotion engine. This analysis identifies the user's emotional state. The input is data containing emotions sent by the user. Based on the analysis, the emotional state is updated in the profile and emotion tags are added. The output of this step is the updated user profile.

[0388] Step 4:

[0389] The server generates personalized notifications based on the updated profile. Input data includes user profile information and local event information. Based on this information, natural language processing techniques are used to create event notifications tailored to the user. The output is a customized notification sent to the user.

[0390] Step 5:

[0391] The server sends the generated notification to the user's terminal. The notification contains event information based on the user's emotional state and interests. The input is the generated notification message. The output is the notification displayed on the user's terminal.

[0392] Step 6:

[0393] Users check notifications, participate in events, and then provide feedback. They re-enter their thoughts and opinions into their devices. The input is user feedback data. This information is sent back to the server and used for planning future events. The output is the feedback information that will be reflected in planning future events.

[0394] (Application Example 2)

[0395] 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 as the "terminal".

[0396] In local community activities, there is a challenge in improving participation rates and user satisfaction due to a lack of information tailored to participants' emotions and interests. Furthermore, there is a need for a system that can grasp individual emotional states in real time and provide guidance based on that information.

[0397] 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.

[0398] In this invention, the server includes means for collecting and organizing local information from multiple sources; means for creating a profile for each individual user and controlling their interests; means for generating personalized guidance based on the profile and the user's emotional state; means for analyzing the user's emotions using an emotion recognition algorithm; means for sending suggestions to the user's terminal based on the analyzed emotional information; and means for collecting and analyzing user feedback and reflecting it in future event planning. This makes it possible to promote participation in local community activities and provide information tailored to each individual user.

[0399] "Local information" refers to information about events, news, and activities in a specific region.

[0400] "Information sources" refer to a variety of sources for obtaining local information, such as news websites, local government announcements, and community message boards.

[0401] A "profile" is a structure for storing information in a database, including a user's interests, concerns, emotional state, and past participation history.

[0402] "Interest information" refers to information about activities and themes that users enjoy.

[0403] "Information" refers to announcements about events and activities provided to users.

[0404] An "emotion recognition algorithm" is a program that analyzes emotions from a user's text input or voice data.

[0405] "Analysis" is a method of breaking down data to understand its meaning and trends.

[0406] "Opinions" refer to feedback and evaluations regarding events and activities provided by users.

[0407] A "proposal" is an announcement of an event or activity optimized for a specific user.

[0408] "Real-time" refers to a state where data collection, analysis, and notification occur almost simultaneously.

[0409] To implement this invention, a system comprising a server, user terminals, a network, and an emotion recognition algorithm is used. The server is responsible for collecting event information from local information sources and storing it in a database. The server also creates individual user profiles and manages information on interests. This makes it possible to generate personalized guidance and analyze the user's emotions using the emotion recognition algorithm.

[0410] The application is installed on smartphones and smart glasses and performs real-time sentiment analysis. Based on the analysis results, it sends suggestions for events best suited to the user's device. Specifically, it uses Python to run natural language processing libraries (e.g., NLTK and TextBlob) to extract sentiment. The frontend uses React Native to create the user interface and is designed to facilitate user feedback.

[0411] When users provide feedback via text or voice input after attending an event, the device sends this data to an emotion analysis algorithm, which then sends the analysis results to a server. This allows the feedback to be used to suggest future events. For example, if a user says, "I really enjoyed this event," the emotion recognition algorithm classifies this as positive, and the server prioritizes activities that evoke positive emotions when suggesting future events.

[0412] For example, if a user enters "I'm tired today," the system will detect stress and suggest relaxation events.

[0413] Example of a prompt:

[0414] User: "I'm tired today."

[0415] Emotion Engine: Recognizes the user's emotions as "stress."

[0416] App: Sends a notification saying, "We'll show you nearby events that are perfect for relieving stress!"

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The server automatically collects event information from local sources. This information includes the event name, date and time, location, and organizer information. The collected data is organized and stored in a database on the server.

[0420] Step 2:

[0421] Users input information into the device, including their interests, concerns, and emotional state. This input data is collected by the device and sent to an emotion recognition algorithm. The transmitted data is processed and reflected in each user's profile.

[0422] Step 3:

[0423] The device uses an emotion recognition algorithm to analyze emotions from user input. Specifically, it analyzes text or voice data using a natural language processing library (e.g., NLTK, TextBlob) and extracts emotion tags. These emotion tags are added to the user's profile.

[0424] Step 4:

[0425] The server generates event recommendations optimized for the user based on updated profile and sentiment information. This process compares collected local event information with the profile and selects events that are appropriate for the user's sentiment state.

[0426] Step 5:

[0427] The server sends the generated event announcement to the user's terminal. The terminal receives this and displays it as a notification to the user. The user can review the event information and decide whether to participate if they are interested.

[0428] Step 6:

[0429] After participating in an event, users enter their feedback into a device. The device then sends this feedback back to an emotion recognition algorithm for emotional analysis. The analysis results are stored in a database on the server to serve as a reference for suggesting future events.

[0430] Step 7:

[0431] The server then uses the feedback data to further improve the user profile. This step involves analyzing past participation history and sentiment feedback to enable more accurate personalization.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention is an information system designed to facilitate local community activities and volunteer work, efficiently collecting local information and providing it to users in an appropriate format. It allows for continuous improvement based on user feedback.

[0449] The system primarily consists of a server, user terminals, and the network connecting them. The server centrally manages regional information, generates personalized notifications for each user, and sends them to the user terminals.

[0450] The server automatically collects information on events and volunteer activities from multiple sources. The information is stored in a database in a specific format, and each event is tagged to indicate which category it belongs to.

[0451] On the other hand, users generate a profile by entering their interests and preferred locations using their device during their first use. This data is sent to the server, where it is combined with other information to create the profile.

[0452] Based on this profile, the server periodically extracts relevant local event information that is likely to interest the user and generates personalized notifications. These notifications utilize natural language processing technology to present information in a way that the user can intuitively understand.

[0453] These notifications are displayed on the user's device in real time, allowing the user to access new activity information upon receiving the notification. If a user wishes to participate, they can submit a participation request to the server via their device, completing the participation process.

[0454] After the event ends, users submit feedback about their experience using their devices. This feedback information is collected on the server and used as data to improve the quality of future events.

[0455] As a concrete example, consider a local cleanup activity. The server collects information about the next cleanup activity from the local environmental organization's website and generates a notification for interested users saying, "Would you like to participate in the next local cleanup event?" When a user indicates their interest in participating, the server immediately processes the participation request, allowing the user to join the activity. In this way, centralized information management and personalized information provision to users enable efficient participation in local activities.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The server automatically collects event information from local sources. Specifically, it uses web scraping and APIs to retrieve data from websites, social media, and local news, and stores it in a database.

[0459] Step 2:

[0460] The user initiates the registration process through their device. They create a personalized interest profile by entering their interests, preferred locations, and available days and times. This information is then sent to the server.

[0461] Step 3:

[0462] The server updates the user's profile based on the data it receives. The profile is tagged according to the user's interests and past participation experience, forming the basis for generating personalized notifications.

[0463] Step 4:

[0464] The server periodically extracts the most relevant events for each user based on their profile information and generates notification messages. Natural language processing techniques are used to create messages that are easy for users to understand.

[0465] Step 5:

[0466] The generated notifications are sent to the user's device and displayed to the user in real time. The user can check notifications of interest and view detailed information.

[0467] Step 6:

[0468] When a user indicates their intention to participate, a participation request is sent from their device to the server. The server updates its database and registers them as an event participant.

[0469] Step 7:

[0470] After the event ends, the server sends a feedback form to the user's device. The user fills out and submits this form, and the server collects the feedback.

[0471] Step 8:

[0472] The server analyzes the collected feedback and extracts information to help improve future events and services. This provides data to enhance the quality of community activities.

[0473] (Example 1)

[0474] 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."

[0475] To promote participation in local community and volunteer activities and to provide information optimized for each individual, it is necessary to efficiently collect vast amounts of local information and provide information based on users' interests. However, conventional systems are insufficient in organizing and personalizing information, and do not provide an attractive participation experience for many users. Furthermore, there is a challenge in that mechanisms for effectively utilizing user feedback are not in place.

[0476] 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.

[0477] In this invention, the server includes means for acquiring and classifying local information from multiple sources, means for creating summaries for each user and managing interest information, and means for recording the user's behavior history and generating personalized participation recommendation information. This makes it possible to provide local information optimized for each user and promotes participation in local community activities.

[0478] "Local information" refers to information such as events, volunteer activities, and news related to a specific region, and is collected from a number of sources.

[0479] "Information sources" refer to media that provide local information, such as websites, social media, newsletters, and materials published by local organizations.

[0480] "Classifying" refers to the process of organizing and systematizing collected regional information based on themes and categories.

[0481] "Users" refer to people who use the system, who are interested in local activities, and who receive information from it.

[0482] "Overview" refers to the profile information created for each user, which includes information about their interests and local area.

[0483] "Means" refers to the methods and processes used to achieve an objective, and in this invention in particular, it refers to technical methods for collecting, organizing, and distributing information.

[0484] "Personalized notifications" refer to messages that are optimized based on the user's interests and preferences, and are generated to present information that is highly relevant to the user.

[0485] "Feedback" refers to opinions from users about the system and evaluations of activities they participated in, which are used to improve the system.

[0486] "Activity history" refers to data that records past activities and participation information performed by users through the system.

[0487] "Recommended participation information" refers to information that indicates local events and activities that users are recommended to participate in, based on their interests and past activity history.

[0488] The system of this invention is designed to effectively collect information on local community activities and volunteer work, and to provide users with optimized information. The system mainly consists of servers, terminals, and a network connecting them.

[0489] server:

[0490] The server utilizes web crawlers and scraping tools to collect information about local events and volunteer activities. Data analysis platforms and APIs may also be used. The collected information is stored in a database in a specific format and tagged by category using natural language processing techniques. Categories may include, for example, "environment," "education," and "culture."

[0491] The server also manages user profiles. These profiles are generated based on the activities and locations that users have shown interest in, and they form the foundation for accurate personalization.

[0492] Terminal:

[0493] The devices used by users are smartphones, personal computers, and other devices used to input and retrieve information through the user interface. Upon first use, a profile is created by inputting activities and regions of interest through the device and sending this information to the server.

[0494] The device also receives personalized notifications sent from the server, allowing users to access new activity information in real time. Furthermore, it makes it easy to register for events and submit feedback using the device.

[0495] Examples of specific cases and prompt statements:

[0496] For example, a server can collect information about upcoming cleanup activities from a local environmental organization's website and generate a notification for users asking, "Would you like to participate in the next local cleanup event?" If a user is interested in this notification and wishes to participate, they can immediately complete the participation process via their device.

[0497] An example of a prompt message might be: "Generate a notification to encourage participation in local community activities. Describe an activity that users are likely to be interested in, as follows: 'Would you like to participate in the next community cleanup event?'"

[0498] This will enable the efficient promotion of local community activities and provide a user-friendly environment for participation.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server collects local information using web crawlers and scraping tools. The data obtained from each information source is retrieved in JSON or CSV format and temporarily stored on the server. Because this input data has various formats and structures, it is pre-processed as needed and formatted into a unified data format. The output is a formatted dataset containing event dates, locations, content, categories, and other information.

[0502] Step 2:

[0503] The server uses natural language processing techniques to categorize the formatted regional information dataset. Specifically, it uses a text analysis algorithm to extract keywords from event summaries and then uses these keywords to determine categories (e.g., environment, education, culture). The input data consists of event information formatted in a unified format, and the output is a dataset with added category information.

[0504] Step 3:

[0505] The user accesses a form using their device to enter their interests and preferred locations. This input consists of selections of categories and locations the user is interested in. This information is sent to a server, which generates a user profile based on it and stores it in a database. The output is a personalized user profile.

[0506] Step 4:

[0507] The server references each user profile and extracts relevant local event information. It uses past participation history data and profile information stored on the server as input data. A generative AI model automatically selects events likely to interest the user and generates notification messages in natural language. The output is a personalized notification sent to the user.

[0508] Step 5:

[0509] The device receives personalized notifications sent from the server and displays them as push notifications. The user checks the notification and, if there is an event of interest, accesses the details page and clicks the "Join" button. The input at this time is the user's intention to participate, and based on this, the server performs the registration process. As output, a participation completion notification is sent to the user.

[0510] Step 6:

[0511] After the event ends, users submit feedback via their devices. This feedback includes user opinions on event satisfaction and areas for improvement. The server aggregates the received feedback and performs data analysis to inform notifications for future local events. The input data is user feedback, and the output provides insights that can help improve future events and encourage participation.

[0512] (Application Example 1)

[0513] 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."

[0514] This invention aims to solve the problem of cumbersome information gathering and participation processes in local communities and volunteer activities. Conventional systems have difficulty efficiently obtaining information that residents need to actively participate in community activities, and furthermore, feedback after participation is not adequately utilized for improvement in the future.

[0515] 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.

[0516] In this invention, the server includes means for collecting and organizing local information from multiple data sources, means for creating characteristic information for each user and managing interest information, and means for providing real-time communication to support participation in local activities. This makes it possible for residents to easily obtain information on activities they wish to participate in and easily send feedback.

[0517] "Local information" refers to data about events and activities within a local community that is available to residents.

[0518] "Data sources" is a general term for various media and platforms that provide local information, and refers to the sources from which digitized information is obtained.

[0519] "Characteristic information" refers to individual profile information created based on a user's interests, preferences, and past behavioral history.

[0520] "Communication" refers to the notification of information that is generated taking into account the user's characteristics and is directed at an individual target.

[0521] An "information terminal" is an electronic device used to receive communications and transmit them to the user, and includes smartphones and personal computers.

[0522] "Evaluation information" refers to feedback provided by users after an activity, and the data necessary for improving that activity.

[0523] "Analysis" refers to the process of analyzing collected evaluation information to improve the quality of future activities.

[0524] "Providing real-time communication" refers to a method of promoting participation in activities by immediately providing users with information they are interested in.

[0525] To implement this invention, a server for efficiently collecting and organizing local digital information and a terminal for receiving information and interacting with users are required. This system is equipped with a program that accesses multiple data sources providing local information and performs web scraping using Python scripts. As a result, the server automatically acquires event information and stores it as structured data in a database system.

[0526] The server utilizes the Flask framework to generate and update user profile information, managing information of user interest as characteristic data. Based on the collected data and user profiles, AWS Lambda and natural language processing technologies are used to generate and send personalized communications in real time.

[0527] User terminals are primarily information terminals such as smartphones, which receive communications sent from the server via the Twilio API and provide notifications to users. These notifications support actions such as expressing interest in participating or sending feedback, encouraging participation in activities through the user interface.

[0528] User feedback is analyzed on the server using the SciKit-learn library and stored as information to improve future activities. A concrete example is a local cleanup activity. In this case, the user receives a notification asking, "Would you like to participate in the next local cleanup event?", and by expressing their interest, the participation process is automatically completed.

[0529] An example of a prompt using a generative AI model is: "Please explain the techniques for a program that retrieves local event information and generates customized notifications based on the user profile."

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The server collects local information from data sources using web scraping techniques. Specifically, it uses Python to retrieve local event and activity information from websites. The raw data obtained is then organized and transformed using the Pandas library and stored in a database. The input is HTML data from websites, and the output is organized structured data.

[0533] Step 2:

[0534] Users register their interests and preferred locations using their device. This generates a user profile. The user's interests and preferences are entered into the device as input and sent to the server. The output is the updated user profile information, which is processed using the Flask framework.

[0535] Step 3:

[0536] The server uses AWS Lambda to periodically analyze user profiles and regional information in the database to generate personalized communications. Natural language processing techniques are used in this process to create notifications in a format that users can intuitively understand. The input is the user profile and regional information, and the output is the generated communication content.

[0537] Step 4:

[0538] The device receives notifications sent from the server via the Twilio API and displays them to the user. The user can review these notifications, express interest in participating in activities, or view more details. The input is notification data from the server, and the output is a visual notification display on the user's device.

[0539] Step 5:

[0540] Users send feedback from their devices to the server. The server then uses SciKit-learn to analyze the feedback data and extract information that can be used to improve future activities. The input is user feedback information, and the output is the analyzed insights.

[0541] 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.

[0542] This invention is a local community activity support system that combines an emotion engine that recognizes user emotions. The emotion engine extracts emotions from user input and interactions and reflects them in a profile, thereby providing a more personalized experience.

[0543] The system consists of a server, user terminals, a network, and an emotion engine. The server collects event information from local sources and stores it in a database. In addition, it utilizes the emotion engine to determine the user's emotional state and incorporate it into a profile.

[0544] Users communicate and provide feedback using their devices. The emotion engine analyzes the user's emotions from text input and voice data and sends the data to the server. The server then adds emotion tags to the user's profile based on this analysis, which are used later for generating notifications and event participation.

[0545] In generating notifications, the server creates wording that takes emotional information into account. For example, if a user is feeling stressed, notifications for refreshing events will be prioritized, allowing for personalized suggestions.

[0546] As a concrete example, when a user sends feedback after an event, the emotion engine identifies that emotion and provides the emotion data to the server along with feedback such as "fun" or "tired." The server then analyzes this data and uses it to plan future events.

[0547] In this way, by using an emotion engine, we can improve the user experience and provide a model that balances effective participation in local activities with improving the quality of those activities through feedback.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] The server collects event information from local sources and stores it in a database. During this process, it also records attribute information such as event type, date and time, and location, using data obtained through web scraping and APIs.

[0551] Step 2:

[0552] Users create a profile using their device. Here, users specify their preferred event categories, geographical location, and available times. The device then sends this information to the server.

[0553] Step 3:

[0554] The emotion engine analyzes the user's text input and voice data to determine their emotional state. The emotion engine generates emotion tags such as "joy," "sadness," and "stress," and sends them to the server.

[0555] Step 4:

[0556] The server adds the received emotional information to the user profile. This profile includes the user's interests and behavioral patterns, as well as their most recent emotional state.

[0557] Step 5:

[0558] The server selects event information relevant to the user based on their profile. In particular, it prioritizes events that align with the user's emotional state to generate personalized notifications.

[0559] Step 6:

[0560] The generated notification is sent to the user's device in real time. The device displays this notification to the user, encouraging them to participate in the event.

[0561] Step 7:

[0562] If a user checks the notification and wishes to participate in the event, they send a participation request from their device to the server. The server updates the participation information and registers the user as an event participant.

[0563] Step 8:

[0564] After participating in the event, the server sends a feedback form to the user's device. The device collects feedback and sentiment data from the user through this form and sends it back to the server.

[0565] Step 9:

[0566] The server analyzes the emotional data obtained along with the feedback, and uses it to improve the activities. This information is reflected in decision-making for future event planning, contributing to the overall improvement of the quality of activities.

[0567] (Example 2)

[0568] 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."

[0569] In local community activities, it is essential to understand the individual feelings and interests of each participant and effectively provide information tailored to them. Traditional information delivery methods have been limited to general information dissemination, making it difficult to address the specific needs and emotional states of users. This has resulted in challenges such as decreased motivation and satisfaction with community activities.

[0570] 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.

[0571] In this invention, the server includes means for collecting and organizing local information from multiple sources, means for analyzing user sentiment data and updating the profile, and means for generating personalized notifications based on the profile. This makes it possible to provide customized information that is tailored to each individual's emotions and interests.

[0572] "Local information" refers to information such as events, activities, and news related to a specific region, and is collected through multiple sources.

[0573] "Personal data" refers to specific information related to each user, including the user's profile, preferences, and past behavioral history.

[0574] "Emotional data" refers to information that indicates the user's emotional state, which is analyzed by the emotion engine and reflected in the profile.

[0575] A "profile" is a data storage system set up for each user, managing user-specific information including emotional data and interest information.

[0576] "Language processing technology" refers to the technology of analyzing and processing natural language, and is used for generating notification information and interpreting user input data.

[0577] A "notification" is an informational message provided to a user, which is personalized and sent under specific conditions.

[0578] "Opinions" refer to feedback and comments collected from users, which are used to improve the system and optimize events.

[0579] The system of this invention provides personalized information to support community activities in a local area. The system consists of a data processing server, a user terminal, a network, and an emotion analysis engine.

[0580] The server collects local information from multiple sources and stores it in a database. This data collection includes common web scraping techniques and the use of APIs. The server also manages user-defined profiles and updates personal data, including sentiment data. This profile management utilizes a cloud-based database.

[0581] A terminal is a device used by users to interact with the system, such as a smartphone or personal computer. Users use the terminal to input text or voice, and this information is pre-processed locally before being sent to the server. In particular, text data is analyzed using natural language processing techniques, and sentiment data is extracted.

[0582] Users can receive more personalized information and notifications by providing their emotional state to the system. Based on the emotional information analyzed from the user's input, the server generates customized event information and notifications and sends them to the user's device.

[0583] For example, if a user submits feedback stating they were "very satisfied" after attending an event, this sentiment information is analyzed by the server and used to recommend future events. The system reflects this in the user's profile, ensuring that similar events are recommended again in the future.

[0584] Examples of prompts include the following:

[0585] "Design a system that analyzes emotional data entered by users daily and suggests local events based on the results. For example, if a user enters 'I felt stressed today,' explain how the system would suggest appropriate events (such as yoga or nature walks)."

[0586] In this way, this system enhances the user experience and promotes effective participation in community activities.

[0587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0588] Step 1:

[0589] The server collects local information from multiple sources. Specifically, it obtains data through web scraping and APIs. The input data consists of local event information and news. This data is then processed and stored in a database. The output is a database of organized event information.

[0590] Step 2:

[0591] The user terminal receives user input. The user inputs their emotions and opinions as text or voice. The input is data related to the user's emotions and opinions. The terminal preprocesses the data and converts it into a format that can be sent to the server. The output is data prepared for sentiment analysis.

[0592] Step 3:

[0593] The server receives the transmitted emotion data and analyzes it using the emotion engine. This analysis identifies the user's emotional state. The input is data containing emotions sent by the user. Based on the analysis, the emotional state is updated in the profile and emotion tags are added. The output of this step is the updated user profile.

[0594] Step 4:

[0595] The server generates personalized notifications based on the updated profile. Input data includes user profile information and local event information. Based on this information, natural language processing techniques are used to create event notifications tailored to the user. The output is a customized notification sent to the user.

[0596] Step 5:

[0597] The server sends the generated notification to the user's terminal. The notification contains event information based on the user's emotional state and interests. The input is the generated notification message. The output is the notification displayed on the user's terminal.

[0598] Step 6:

[0599] Users check notifications, participate in events, and then provide feedback. They re-enter their thoughts and opinions into their devices. The input is user feedback data. This information is sent back to the server and used for planning future events. The output is the feedback information that will be reflected in planning future events.

[0600] (Application Example 2)

[0601] 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."

[0602] In local community activities, there is a challenge in improving participation rates and user satisfaction due to a lack of information tailored to participants' emotions and interests. Furthermore, there is a need for a system that can grasp individual emotional states in real time and provide guidance based on that information.

[0603] 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.

[0604] In this invention, the server includes means for collecting and organizing local information from multiple sources; means for creating a profile for each individual user and controlling their interests; means for generating personalized guidance based on the profile and the user's emotional state; means for analyzing the user's emotions using an emotion recognition algorithm; means for sending suggestions to the user's terminal based on the analyzed emotional information; and means for collecting and analyzing user feedback and reflecting it in future event planning. This makes it possible to promote participation in local community activities and provide information tailored to each individual user.

[0605] "Local information" refers to information about events, news, and activities in a specific region.

[0606] "Information sources" refer to a variety of sources for obtaining local information, such as news websites, local government announcements, and community message boards.

[0607] A "profile" is a structure for storing information in a database, including a user's interests, concerns, emotional state, and past participation history.

[0608] "Interest information" refers to information about activities and themes that users enjoy.

[0609] "Information" refers to announcements about events and activities provided to users.

[0610] An "emotion recognition algorithm" is a program that analyzes emotions from a user's text input or voice data.

[0611] "Analysis" is a method of breaking down data to understand its meaning and trends.

[0612] "Opinions" refer to feedback and evaluations regarding events and activities provided by users.

[0613] A "proposal" is an announcement of an event or activity optimized for a specific user.

[0614] "Real-time" refers to a state where data collection, analysis, and notification occur almost simultaneously.

[0615] To implement this invention, a system comprising a server, user terminals, a network, and an emotion recognition algorithm is used. The server is responsible for collecting event information from local information sources and storing it in a database. The server also creates individual user profiles and manages information on interests. This makes it possible to generate personalized guidance and analyze the user's emotions using the emotion recognition algorithm.

[0616] The application is installed on smartphones and smart glasses and performs real-time sentiment analysis. Based on the analysis results, it sends suggestions for events best suited to the user's device. Specifically, it uses Python to run natural language processing libraries (e.g., NLTK and TextBlob) to extract sentiment. The frontend uses React Native to create the user interface and is designed to facilitate user feedback.

[0617] When users provide feedback via text or voice input after attending an event, the device sends this data to an emotion analysis algorithm, which then sends the analysis results to a server. This allows the feedback to be used to suggest future events. For example, if a user says, "I really enjoyed this event," the emotion recognition algorithm classifies this as positive, and the server prioritizes activities that evoke positive emotions when suggesting future events.

[0618] For example, if a user enters "I'm tired today," the system will detect stress and suggest relaxation events.

[0619] Example of a prompt:

[0620] User: "I'm tired today."

[0621] Emotion Engine: Recognizes the user's emotions as "stress."

[0622] App: Sends a notification saying, "We'll show you nearby events that are perfect for relieving stress!"

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The server automatically collects event information from local sources. This information includes the event name, date and time, location, and organizer information. The collected data is organized and stored in a database on the server.

[0626] Step 2:

[0627] Users input information into the device, including their interests, concerns, and emotional state. This input data is collected by the device and sent to an emotion recognition algorithm. The transmitted data is processed and reflected in each user's profile.

[0628] Step 3:

[0629] The device uses an emotion recognition algorithm to analyze emotions from user input. Specifically, it analyzes text or voice data using a natural language processing library (e.g., NLTK, TextBlob) and extracts emotion tags. These emotion tags are added to the user's profile.

[0630] Step 4:

[0631] The server generates event recommendations optimized for the user based on updated profile and sentiment information. This process compares collected local event information with the profile and selects events that are appropriate for the user's sentiment state.

[0632] Step 5:

[0633] The server sends the generated event announcement to the user's terminal. The terminal receives this and displays it as a notification to the user. The user can review the event information and decide whether to participate if they are interested.

[0634] Step 6:

[0635] After participating in an event, users enter their feedback into a device. The device then sends this feedback back to an emotion recognition algorithm for emotional analysis. The analysis results are stored in a database on the server to serve as a reference for suggesting future events.

[0636] Step 7:

[0637] The server then uses the feedback data to further improve the user profile. This step involves analyzing past participation history and sentiment feedback to enable more accurate personalization.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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".

[0655] This invention is an information system designed to facilitate local community activities and volunteer work, efficiently collecting local information and providing it to users in an appropriate format. It allows for continuous improvement based on user feedback.

[0656] The system primarily consists of a server, user terminals, and the network connecting them. The server centrally manages regional information, generates personalized notifications for each user, and sends them to the user terminals.

[0657] The server automatically collects information on events and volunteer activities from multiple sources. The information is stored in a database in a specific format, and each event is tagged to indicate which category it belongs to.

[0658] On the other hand, users generate a profile by entering their interests and preferred locations using their device during their first use. This data is sent to the server, where it is combined with other information to create the profile.

[0659] Based on this profile, the server periodically extracts relevant local event information that is likely to interest the user and generates personalized notifications. These notifications utilize natural language processing technology to present information in a way that the user can intuitively understand.

[0660] These notifications are displayed on the user's device in real time, allowing the user to access new activity information upon receiving the notification. If a user wishes to participate, they can submit a participation request to the server via their device, completing the participation process.

[0661] After the event ends, users submit feedback about their experience using their devices. This feedback information is collected on the server and used as data to improve the quality of future events.

[0662] As a concrete example, consider a local cleanup activity. The server collects information about the next cleanup activity from the local environmental organization's website and generates a notification for interested users saying, "Would you like to participate in the next local cleanup event?" When a user indicates their interest in participating, the server immediately processes the participation request, allowing the user to join the activity. In this way, centralized information management and personalized information provision to users enable efficient participation in local activities.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The server automatically collects event information from local sources. Specifically, it uses web scraping and APIs to retrieve data from websites, social media, and local news, and stores it in a database.

[0666] Step 2:

[0667] The user initiates the registration process through their device. They create a personalized interest profile by entering their interests, preferred locations, and available days and times. This information is then sent to the server.

[0668] Step 3:

[0669] The server updates the user's profile based on the data it receives. The profile is tagged according to the user's interests and past participation experience, forming the basis for generating personalized notifications.

[0670] Step 4:

[0671] The server periodically extracts the most relevant events for each user based on their profile information and generates notification messages. Natural language processing techniques are used to create messages that are easy for users to understand.

[0672] Step 5:

[0673] The generated notifications are sent to the user's device and displayed to the user in real time. The user can check notifications of interest and view detailed information.

[0674] Step 6:

[0675] When a user indicates their intention to participate, a participation request is sent from their device to the server. The server updates its database and registers them as an event participant.

[0676] Step 7:

[0677] After the event ends, the server sends a feedback form to the user's device. The user fills out and submits this form, and the server collects the feedback.

[0678] Step 8:

[0679] The server analyzes the collected feedback and extracts information to help improve future events and services. This provides data to enhance the quality of community activities.

[0680] (Example 1)

[0681] 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".

[0682] To promote participation in local community and volunteer activities and to provide information optimized for each individual, it is necessary to efficiently collect vast amounts of local information and provide information based on users' interests. However, conventional systems are insufficient in organizing and personalizing information, and do not provide an attractive participation experience for many users. Furthermore, there is a challenge in that mechanisms for effectively utilizing user feedback are not in place.

[0683] 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.

[0684] In this invention, the server includes means for acquiring and classifying local information from multiple sources, means for creating summaries for each user and managing interest information, and means for recording the user's behavior history and generating personalized participation recommendation information. This makes it possible to provide local information optimized for each user and promotes participation in local community activities.

[0685] "Local information" refers to information such as events, volunteer activities, and news related to a specific region, and is collected from a number of sources.

[0686] "Information sources" refer to media that provide local information, such as websites, social media, newsletters, and materials published by local organizations.

[0687] "Classifying" refers to the process of organizing and systematizing collected regional information based on themes and categories.

[0688] "Users" refer to people who use the system, who are interested in local activities, and who receive information from it.

[0689] "Overview" refers to the profile information created for each user, which includes information about their interests and local area.

[0690] "Means" refers to the methods and processes used to achieve an objective, and in this invention in particular, it refers to technical methods for collecting, organizing, and distributing information.

[0691] "Personalized notifications" refer to messages that are optimized based on the user's interests and preferences, and are generated to present information that is highly relevant to the user.

[0692] "Feedback" refers to opinions from users about the system and evaluations of activities they participated in, which are used to improve the system.

[0693] "Activity history" refers to data that records past activities and participation information performed by users through the system.

[0694] "Recommended participation information" refers to information that indicates local events and activities that users are recommended to participate in, based on their interests and past activity history.

[0695] The system of this invention is designed to effectively collect information on local community activities and volunteer work, and to provide users with optimized information. The system mainly consists of servers, terminals, and a network connecting them.

[0696] server:

[0697] The server utilizes web crawlers and scraping tools to collect information about local events and volunteer activities. Data analysis platforms and APIs may also be used. The collected information is stored in a database in a specific format and tagged by category using natural language processing techniques. Categories may include, for example, "environment," "education," and "culture."

[0698] The server also manages user profiles. These profiles are generated based on the activities and locations that users have shown interest in, and they form the foundation for accurate personalization.

[0699] Terminal:

[0700] The devices used by users are smartphones, personal computers, and other devices used to input and retrieve information through the user interface. Upon first use, a profile is created by inputting activities and regions of interest through the device and sending this information to the server.

[0701] The device also receives personalized notifications sent from the server, allowing users to access new activity information in real time. Furthermore, it makes it easy to register for events and submit feedback using the device.

[0702] Examples of specific cases and prompt statements:

[0703] For example, a server can collect information about upcoming cleanup activities from a local environmental organization's website and generate a notification for users asking, "Would you like to participate in the next local cleanup event?" If a user is interested in this notification and wishes to participate, they can immediately complete the participation process via their device.

[0704] An example of a prompt message might be: "Generate a notification to encourage participation in local community activities. Describe an activity that users are likely to be interested in, as follows: 'Would you like to participate in the next community cleanup event?'"

[0705] This will enable the efficient promotion of local community activities and provide a user-friendly environment for participation.

[0706] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0707] Step 1:

[0708] The server collects local information using web crawlers and scraping tools. The data obtained from each information source is retrieved in JSON or CSV format and temporarily stored on the server. Because this input data has various formats and structures, it is pre-processed as needed and formatted into a unified data format. The output is a formatted dataset containing event dates, locations, content, categories, and other information.

[0709] Step 2:

[0710] The server uses natural language processing techniques to categorize the formatted regional information dataset. Specifically, it uses a text analysis algorithm to extract keywords from event summaries and then uses these keywords to determine categories (e.g., environment, education, culture). The input data consists of event information formatted in a unified format, and the output is a dataset with added category information.

[0711] Step 3:

[0712] The user accesses a form using their device to enter their interests and preferred locations. This input consists of selections of categories and locations the user is interested in. This information is sent to a server, which generates a user profile based on it and stores it in a database. The output is a personalized user profile.

[0713] Step 4:

[0714] The server references each user profile and extracts relevant local event information. It uses past participation history data and profile information stored on the server as input data. A generative AI model automatically selects events likely to interest the user and generates notification messages in natural language. The output is a personalized notification sent to the user.

[0715] Step 5:

[0716] The device receives personalized notifications sent from the server and displays them as push notifications. The user checks the notification and, if there is an event of interest, accesses the details page and clicks the "Join" button. The input at this time is the user's intention to participate, and based on this, the server performs the registration process. As output, a participation completion notification is sent to the user.

[0717] Step 6:

[0718] After the event ends, users submit feedback via their devices. This feedback includes user opinions on event satisfaction and areas for improvement. The server aggregates the received feedback and performs data analysis to inform notifications for future local events. The input data is user feedback, and the output provides insights that can help improve future events and encourage participation.

[0719] (Application Example 1)

[0720] 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".

[0721] This invention aims to solve the problem of cumbersome information gathering and participation processes in local communities and volunteer activities. Conventional systems have difficulty efficiently obtaining information that residents need to actively participate in community activities, and furthermore, feedback after participation is not adequately utilized for improvement in the future.

[0722] 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.

[0723] In this invention, the server includes means for collecting and organizing local information from multiple data sources, means for creating characteristic information for each user and managing interest information, and means for providing real-time communication to support participation in local activities. This makes it possible for residents to easily obtain information on activities they wish to participate in and easily send feedback.

[0724] "Local information" refers to data about events and activities within a local community that is available to residents.

[0725] "Data sources" is a general term for various media and platforms that provide local information, and refers to the sources from which digitized information is obtained.

[0726] "Characteristic information" refers to individual profile information created based on a user's interests, preferences, and past behavioral history.

[0727] "Communication" refers to the notification of information that is generated taking into account the user's characteristics and is directed at an individual target.

[0728] An "information terminal" is an electronic device used to receive communications and transmit them to the user, and includes smartphones and personal computers.

[0729] "Evaluation information" refers to feedback provided by users after an activity, and the data necessary for improving that activity.

[0730] "Analysis" refers to the process of analyzing collected evaluation information to improve the quality of future activities.

[0731] "Providing real-time communication" refers to a method of promoting participation in activities by immediately providing users with information they are interested in.

[0732] To implement this invention, a server for efficiently collecting and organizing local digital information and a terminal for receiving information and interacting with users are required. This system is equipped with a program that accesses multiple data sources providing local information and performs web scraping using Python scripts. As a result, the server automatically acquires event information and stores it as structured data in a database system.

[0733] The server utilizes the Flask framework to generate and update user profile information, managing information of user interest as characteristic data. Based on the collected data and user profiles, AWS Lambda and natural language processing technologies are used to generate and send personalized communications in real time.

[0734] User terminals are primarily information terminals such as smartphones, which receive communications sent from the server via the Twilio API and provide notifications to users. These notifications support actions such as expressing interest in participating or sending feedback, encouraging participation in activities through the user interface.

[0735] User feedback is analyzed on the server using the SciKit-learn library and stored as information to improve future activities. A concrete example is a local cleanup activity. In this case, the user receives a notification asking, "Would you like to participate in the next local cleanup event?", and by expressing their interest, the participation process is automatically completed.

[0736] An example of a prompt using a generative AI model is: "Please explain the techniques for a program that retrieves local event information and generates customized notifications based on the user profile."

[0737] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0738] Step 1:

[0739] The server collects local information from data sources using web scraping techniques. Specifically, it uses Python to retrieve local event and activity information from websites. The raw data obtained is then organized and transformed using the Pandas library and stored in a database. The input is HTML data from websites, and the output is organized structured data.

[0740] Step 2:

[0741] Users register their interests and preferred locations using their device. This generates a user profile. The user's interests and preferences are entered into the device as input and sent to the server. The output is the updated user profile information, which is processed using the Flask framework.

[0742] Step 3:

[0743] The server uses AWS Lambda to periodically analyze user profiles and regional information in the database to generate personalized communications. Natural language processing techniques are used in this process to create notifications in a format that users can intuitively understand. The input is the user profile and regional information, and the output is the generated communication content.

[0744] Step 4:

[0745] The device receives notifications sent from the server via the Twilio API and displays them to the user. The user can review these notifications, express interest in participating in activities, or view more details. The input is notification data from the server, and the output is a visual notification display on the user's device.

[0746] Step 5:

[0747] Users send feedback from their devices to the server. The server then uses SciKit-learn to analyze the feedback data and extract information that can be used to improve future activities. The input is user feedback information, and the output is the analyzed insights.

[0748] 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.

[0749] This invention is a local community activity support system that combines an emotion engine that recognizes user emotions. The emotion engine extracts emotions from user input and interactions and reflects them in a profile, thereby providing a more personalized experience.

[0750] The system consists of a server, user terminals, a network, and an emotion engine. The server collects event information from local sources and stores it in a database. In addition, it utilizes the emotion engine to determine the user's emotional state and incorporate it into a profile.

[0751] Users communicate and provide feedback using their devices. The emotion engine analyzes the user's emotions from text input and voice data and sends the data to the server. The server then adds emotion tags to the user's profile based on this analysis, which are used later for generating notifications and event participation.

[0752] In generating notifications, the server creates wording that takes emotional information into account. For example, if a user is feeling stressed, notifications for refreshing events will be prioritized, allowing for personalized suggestions.

[0753] As a concrete example, when a user sends feedback after an event, the emotion engine identifies that emotion and provides the emotion data to the server along with feedback such as "fun" or "tired." The server then analyzes this data and uses it to plan future events.

[0754] In this way, by using an emotion engine, we can improve the user experience and provide a model that balances effective participation in local activities with improving the quality of those activities through feedback.

[0755] The following describes the processing flow.

[0756] Step 1:

[0757] The server collects event information from local sources and stores it in a database. During this process, it also records attribute information such as event type, date and time, and location, using data obtained through web scraping and APIs.

[0758] Step 2:

[0759] Users create a profile using their device. Here, users specify their preferred event categories, geographical location, and available times. The device then sends this information to the server.

[0760] Step 3:

[0761] The emotion engine analyzes the user's text input and voice data to determine their emotional state. The emotion engine generates emotion tags such as "joy," "sadness," and "stress," and sends them to the server.

[0762] Step 4:

[0763] The server adds the received emotional information to the user profile. This profile includes the user's interests and behavioral patterns, as well as their most recent emotional state.

[0764] Step 5:

[0765] The server selects event information relevant to the user based on their profile. In particular, it prioritizes events that align with the user's emotional state to generate personalized notifications.

[0766] Step 6:

[0767] The generated notification is sent to the user's device in real time. The device displays this notification to the user, encouraging them to participate in the event.

[0768] Step 7:

[0769] If a user checks the notification and wishes to participate in the event, they send a participation request from their device to the server. The server updates the participation information and registers the user as an event participant.

[0770] Step 8:

[0771] After participating in the event, the server sends a feedback form to the user's device. The device collects feedback and sentiment data from the user through this form and sends it back to the server.

[0772] Step 9:

[0773] The server analyzes the emotional data obtained along with the feedback, and uses it to improve the activities. This information is reflected in decision-making for future event planning, contributing to the overall improvement of the quality of activities.

[0774] (Example 2)

[0775] 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".

[0776] In local community activities, it is essential to understand the individual feelings and interests of each participant and effectively provide information tailored to them. Traditional information delivery methods have been limited to general information dissemination, making it difficult to address the specific needs and emotional states of users. This has resulted in challenges such as decreased motivation and satisfaction with community activities.

[0777] 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.

[0778] In this invention, the server includes means for collecting and organizing local information from multiple sources, means for analyzing user sentiment data and updating the profile, and means for generating personalized notifications based on the profile. This makes it possible to provide customized information that is tailored to each individual's emotions and interests.

[0779] "Local information" refers to information such as events, activities, and news related to a specific region, and is collected through multiple sources.

[0780] "Personal data" refers to specific information related to each user, including the user's profile, preferences, and past behavioral history.

[0781] "Emotional data" refers to information that indicates the user's emotional state, which is analyzed by the emotion engine and reflected in the profile.

[0782] A "profile" is a data storage system set up for each user, managing user-specific information including emotional data and interest information.

[0783] "Language processing technology" refers to the technology of analyzing and processing natural language, and is used for generating notification information and interpreting user input data.

[0784] A "notification" is an informational message provided to a user, which is personalized and sent under specific conditions.

[0785] "Opinions" refer to feedback and comments collected from users, which are used to improve the system and optimize events.

[0786] The system of this invention provides personalized information to support community activities in a local area. The system consists of a data processing server, a user terminal, a network, and an emotion analysis engine.

[0787] The server collects local information from multiple sources and stores it in a database. This data collection includes common web scraping techniques and the use of APIs. The server also manages user-defined profiles and updates personal data, including sentiment data. This profile management utilizes a cloud-based database.

[0788] A terminal is a device used by users to interact with the system, such as a smartphone or personal computer. Users use the terminal to input text or voice, and this information is pre-processed locally before being sent to the server. In particular, text data is analyzed using natural language processing techniques, and sentiment data is extracted.

[0789] Users can receive more personalized information and notifications by providing their emotional state to the system. Based on the emotional information analyzed from the user's input, the server generates customized event information and notifications and sends them to the user's device.

[0790] For example, if a user submits feedback stating they were "very satisfied" after attending an event, this sentiment information is analyzed by the server and used to recommend future events. The system reflects this in the user's profile, ensuring that similar events are recommended again in the future.

[0791] Examples of prompts include the following:

[0792] "Design a system that analyzes emotional data entered by users daily and suggests local events based on the results. For example, if a user enters 'I felt stressed today,' explain how the system would suggest appropriate events (such as yoga or nature walks)."

[0793] In this way, this system enhances the user experience and promotes effective participation in community activities.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The server collects local information from multiple sources. Specifically, it obtains data through web scraping and APIs. The input data consists of local event information and news. This data is then processed and stored in a database. The output is a database of organized event information.

[0797] Step 2:

[0798] The user terminal receives user input. The user inputs their emotions and opinions as text or voice. The input is data related to the user's emotions and opinions. The terminal preprocesses the data and converts it into a format that can be sent to the server. The output is data prepared for sentiment analysis.

[0799] Step 3:

[0800] The server receives the transmitted emotion data and analyzes it using the emotion engine. This analysis identifies the user's emotional state. The input is data containing emotions sent by the user. Based on the analysis, the emotional state is updated in the profile and emotion tags are added. The output of this step is the updated user profile.

[0801] Step 4:

[0802] The server generates personalized notifications based on the updated profile. Input data includes user profile information and local event information. Based on this information, natural language processing techniques are used to create event notifications tailored to the user. The output is a customized notification sent to the user.

[0803] Step 5:

[0804] The server sends the generated notification to the user's terminal. The notification contains event information based on the user's emotional state and interests. The input is the generated notification message. The output is the notification displayed on the user's terminal.

[0805] Step 6:

[0806] Users check notifications, participate in events, and then provide feedback. They re-enter their thoughts and opinions into their devices. The input is user feedback data. This information is sent back to the server and used for planning future events. The output is the feedback information that will be reflected in planning future events.

[0807] (Application Example 2)

[0808] 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".

[0809] In local community activities, there is a challenge in improving participation rates and user satisfaction due to a lack of information tailored to participants' emotions and interests. Furthermore, there is a need for a system that can grasp individual emotional states in real time and provide guidance based on that information.

[0810] 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.

[0811] In this invention, the server includes means for collecting and organizing local information from multiple sources; means for creating a profile for each individual user and controlling their interests; means for generating personalized guidance based on the profile and the user's emotional state; means for analyzing the user's emotions using an emotion recognition algorithm; means for sending suggestions to the user's terminal based on the analyzed emotional information; and means for collecting and analyzing user feedback and reflecting it in future event planning. This makes it possible to promote participation in local community activities and provide information tailored to each individual user.

[0812] "Local information" refers to information about events, news, and activities in a specific region.

[0813] "Information sources" refer to a variety of sources for obtaining local information, such as news websites, local government announcements, and community message boards.

[0814] A "profile" is a structure for storing information in a database, including a user's interests, concerns, emotional state, and past participation history.

[0815] "Interest information" refers to information about activities and themes that users enjoy.

[0816] "Information" refers to announcements about events and activities provided to users.

[0817] An "emotion recognition algorithm" is a program that analyzes emotions from a user's text input or voice data.

[0818] "Analysis" is a method of breaking down data to understand its meaning and trends.

[0819] "Opinions" refer to feedback and evaluations regarding events and activities provided by users.

[0820] A "proposal" is an announcement of an event or activity optimized for a specific user.

[0821] "Real-time" refers to a state where data collection, analysis, and notification occur almost simultaneously.

[0822] To implement this invention, a system comprising a server, user terminals, a network, and an emotion recognition algorithm is used. The server is responsible for collecting event information from local information sources and storing it in a database. The server also creates individual user profiles and manages information on interests. This makes it possible to generate personalized guidance and analyze the user's emotions using the emotion recognition algorithm.

[0823] The application is installed on smartphones and smart glasses and performs real-time sentiment analysis. Based on the analysis results, it sends suggestions for events best suited to the user's device. Specifically, it uses Python to run natural language processing libraries (e.g., NLTK and TextBlob) to extract sentiment. The frontend uses React Native to create the user interface and is designed to facilitate user feedback.

[0824] When users provide feedback via text or voice input after attending an event, the device sends this data to an emotion analysis algorithm, which then sends the analysis results to a server. This allows the feedback to be used to suggest future events. For example, if a user says, "I really enjoyed this event," the emotion recognition algorithm classifies this as positive, and the server prioritizes activities that evoke positive emotions when suggesting future events.

[0825] For example, if a user enters "I'm tired today," the system will detect stress and suggest relaxation events.

[0826] Example of a prompt:

[0827] User: "I'm tired today."

[0828] Emotion Engine: Recognizes the user's emotions as "stress."

[0829] App: Sends a notification saying, "We'll show you nearby events that are perfect for relieving stress!"

[0830] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0831] Step 1:

[0832] The server automatically collects event information from local sources. This information includes the event name, date and time, location, and organizer information. The collected data is organized and stored in a database on the server.

[0833] Step 2:

[0834] Users input information into the device, including their interests, concerns, and emotional state. This input data is collected by the device and sent to an emotion recognition algorithm. The transmitted data is processed and reflected in each user's profile.

[0835] Step 3:

[0836] The device uses an emotion recognition algorithm to analyze emotions from user input. Specifically, it analyzes text or voice data using a natural language processing library (e.g., NLTK, TextBlob) and extracts emotion tags. These emotion tags are added to the user's profile.

[0837] Step 4:

[0838] The server generates event recommendations optimized for the user based on updated profile and sentiment information. This process compares collected local event information with the profile and selects events that are appropriate for the user's sentiment state.

[0839] Step 5:

[0840] The server sends the generated event announcement to the user's terminal. The terminal receives this and displays it as a notification to the user. The user can review the event information and decide whether to participate if they are interested.

[0841] Step 6:

[0842] After participating in an event, users enter their feedback into a device. The device then sends this feedback back to an emotion recognition algorithm for emotional analysis. The analysis results are stored in a database on the server to serve as a reference for suggesting future events.

[0843] Step 7:

[0844] The server then uses the feedback data to further improve the user profile. This step involves analyzing past participation history and sentiment feedback to enable more accurate personalization.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of collecting and organizing local information from multiple sources,

[0869] A means of creating a profile for each user and managing their interests,

[0870] Means for generating personalized notifications based on the aforementioned profile,

[0871] A means of sending the notified information to the user's terminal,

[0872] A means of collecting and analyzing user feedback,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, which uses natural language processing technology to generate notification information.

[0876] (Claim 3)

[0877] The system according to claim 1, which updates a user's profile using their past participation record.

[0878] "Example 1"

[0879] (Claim 1)

[0880] A means of obtaining and classifying regional information from multiple sources,

[0881] A means of creating an overview for each user and managing their interests,

[0882] Means for creating personalized notifications based on the above summary,

[0883] A means of sending the created notification to the user's terminal,

[0884] Methods for collecting and analyzing user feedback,

[0885] A means for recording user behavior history and generating personalized participation recommendation information,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which utilizes natural language processing technology to create notification information.

[0889] (Claim 3)

[0890] The system according to claim 1, which updates the summary using the user's past participation history.

[0891] "Application Example 1"

[0892] (Claim 1)

[0893] A means of collecting and organizing regional information from multiple data sources,

[0894] A means of creating characteristic information for each user and managing interest information,

[0895] means for generating personalized communication based on the aforementioned characteristic information,

[0896] A means for transmitting the generated communication to an information terminal,

[0897] A means of collecting and analyzing user evaluation information,

[0898] A means of providing real-time communication to support participation in community activities,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, which uses natural language processing technology to provide notification information and communications.

[0902] (Claim 3)

[0903] The system according to claim 1, which updates characteristic information using the user's past activity participation record.

[0904] "Example 2 of combining an emotion engine"

[0905] (Claim 1)

[0906] A means of collecting and organizing local information from multiple sources,

[0907] A means of creating personal data for each user and managing interest information,

[0908] A means of analyzing user sentiment data and updating the profile,

[0909] Means for generating personalized notifications based on the aforementioned profile,

[0910] Means for transmitting the notified information to the user device,

[0911] Methods for collecting and analyzing user feedback,

[0912] A device that includes this.

[0913] (Claim 2)

[0914] The apparatus according to claim 1, which uses language processing technology to generate notification information.

[0915] (Claim 3)

[0916] The apparatus according to claim 1, which updates a user's profile using their past participation records and sentiment data.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A means of gathering and organizing local information from multiple sources,

[0920] A means of creating a profile for each individual user and controlling their interests and preferences,

[0921] Means for generating personalized guidance based on the aforementioned profile and the user's emotional state,

[0922] A means of analyzing a user's emotions using an emotion recognition algorithm,

[0923] A means for sending suggestions to the user's terminal based on analyzed emotional information,

[0924] A means of collecting and analyzing user feedback and incorporating it into future event planning,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, which uses natural language processing technology to generate proposal information.

[0928] (Claim 3)

[0929] The system according to claim 1, which updates a user's profile using their past participation history and emotional feedback. [Explanation of Symbols]

[0930] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting and organizing regional information from multiple data sources, A means of creating characteristic information for each user and managing interest information, means for generating personalized communication based on the aforementioned characteristic information, A means for transmitting the generated communication to an information terminal, A means of collecting and analyzing user evaluation information, A means of providing real-time communication to support participation in community activities, A system that includes this.

2. The system according to claim 1, which uses natural language processing technology for providing notification information and communications.

3. The system according to claim 1, which updates characteristic information using the user's past activity participation record.

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