system

Abstract

We provide the system. [Solution] Means of collecting local event information, A means of managing user interest information, A method for analyzing user requests using natural language processing, A means of selecting and notifying event information based on the analysis results, Means for collecting and analyzing user feedback, 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] In modern cities, the participation and integration of local communities are considered very important. However, there are problems such as the dispersion of information and insufficient communication among members, making it difficult for new residents to quickly integrate into the community. Also, in order to find opportunities for activities along one's interests, it is necessary to search for information individually, which has become a hurdle for participation. Due to these problems, the local bond is not sufficiently deep.

Means for Solving the Problems

[0005] This invention provides a system that centrally collects local event information and manages the interests of individual users. Furthermore, it analyzes user requests using natural language processing and selects and notifies users of the most suitable event information based on the analysis results. This system allows users to easily find opportunities for activities that interest them, and enables new residents to quickly integrate into the community. In addition, by analyzing user feedback and reflecting it in planning future events, the quality of the service can be continuously improved and community ties can be strengthened.

[0006] "Means for collecting local event information" refers to a device or program that has the function of acquiring data on events held within a region and storing it in a database.

[0007] "Means for managing user interest information" refers to a program or device that records a user's interests and past activity history, and provides relevant information appropriately based on this information.

[0008] "Means of analyzing user requests using natural language processing" refers to a system that uses technology to analyze text or voice input from users and understand their intent.

[0009] "A means of selecting and notifying event information based on analysis results" refers to a system that has the function of selecting the most suitable event information according to the analyzed user's requests and informing the user.

[0010] "Means for collecting and analyzing user feedback" refers to a system or program that has the function of collecting and organizing feedback information provided by users and using it to improve future events or services. [Brief explanation of the drawing]

[0011] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the labeled 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.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] The system of this invention operates in cooperation with servers, terminals, and users to support the smooth operation of local community activities. The system centrally manages local event information and provides optimal information based on the user's interests and participation history.

[0033] In this system, the server first collects local event information from the internet. For example, it uses web scraping technology to collect information from event calendars published by local governments and websites run by local non-profit organizations, and stores it in a database. This database includes event names, dates and times, locations, and participation requirements.

[0034] Next, the user logs into the system using their device and sets their areas of interest and available time slots. The device sends this information to the server, where it is stored in the database as a user profile. This allows the system to provide information tailored to the user's interests.

[0035] When a user makes a specific question or request to the system, the server uses natural language processing techniques to analyze the input. For example, if a user requests, "Are there any volunteer activities I can participate in this weekend?", the server will extract relevant event information from the database and select the most suitable one.

[0036] Selected event information is notified to the user's device, allowing them to view the details. Users who wish to register for an event complete the registration process on their device, and this information is sent to the community's event administrator via the server. This process ensures that registration is completed quickly and efficiently.

[0037] After the activity, users provide feedback through their devices. The server collects and analyzes this feedback and uses it to plan future events and optimize services. Specifically, it statistically processes participant evaluations and improvement requests and proposes feasible improvement measures to event organizers.

[0038] Thus, the system according to the present invention aims to enhance community cohesion by efficiently managing event information for local communities and providing a user-friendly participation platform.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects local event information. Using web scraping and public APIs, it retrieves data such as event names, dates, times, locations, and participation requirements from various internet sources and stores them in a database.

[0042] Step 2:

[0043] Users log in to the system using their device. Users set their interests and available time slots. The device sends this information to the server, and the user profile is stored in the database.

[0044] Step 3:

[0045] When a user sends a question or request to the system, the server uses natural language processing technology to analyze its content. For example, if a request asks about "volunteer activities this weekend," the server understands the content and extracts relevant keywords.

[0046] Step 4:

[0047] Based on the analysis results, the server searches the database for relevant event information. It prioritizes selecting events that match the user profile's interests and schedule.

[0048] Step 5:

[0049] The terminal notifies the user of event information selected by the server. The notification includes an overview of the event, date and time, location, and details on how to participate.

[0050] Step 6:

[0051] If a user wishes to participate, they register for the event through their device. The device sends the registration information to the server, completes the necessary registration procedures, and sends a registration completion notification to the user.

[0052] Step 7:

[0053] After the event ends, the server sends a notification to the user requesting feedback. The user provides feedback via their device, and the server stores this feedback in a database.

[0054] Step 8:

[0055] The server analyzes the collected feedback to help plan future events and improve services, thereby enhancing the quality of local communication.

[0056] (Example 1)

[0057] 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."

[0058] Information about community activities and events tends to be scattered, making it difficult for residents to find events that suit their interests and schedules. Furthermore, there is no established process for quickly collecting feedback from participants after an event and using that feedback to improve the event. A system is needed to address these inconveniences and challenges and facilitate the smooth operation of community activities.

[0059] 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.

[0060] In this invention, the server includes means for acquiring local activity information using an information processing device, means for organizing data related to the user's interests, and means for understanding the user's inquiries using natural language processing technology. This enables the efficient aggregation of local activity information and the provision of information and incorporation of feedback that meets the user's needs.

[0061] An "information processing system" is a computer system used to acquire, organize, and manage information related to local community activities.

[0062] "Users" refers to individuals or groups who obtain information on local activities and seek event information that matches their interests.

[0063] "Interest-related data" refers to profile information that includes information such as the user's interests, preferences, and available times.

[0064] "Natural language processing technology" is a technique that analyzes text-based requests from users and accurately understands their intent, and generally uses machine learning or generative AI models.

[0065] "Feedback" refers to the impressions, evaluations, or suggestions for improvement that users provide after participating in an event, and this information is used to improve future services.

[0066] The system of this invention is designed to smoothly support community activities and operates in cooperation with servers, terminals, and users. The system aims to aggregate local activity information and provide users with personalized information.

[0067] The server uses an information processing device to acquire local activity information. Specifically, the server uses web scraping technology to collect event information from publicly available sources on the internet. The software used here includes libraries such as Beautiful Soup and Scrapy. This allows the server to efficiently collect data such as event name, date and time, location, and participation requirements, and store it in a database.

[0068] Users access the system using their own devices and enter their areas of interest and available time slots. The devices utilize dedicated applications or web portals to simplify initial setup and operation. Data related to the user's interests is sent to the server and stored in a database as a user profile.

[0069] Furthermore, when a user requests specific activity information, the server analyzes the user's request using natural language processing technology. The technologies used for analysis include generative AI models, such as GPT-based models. For example, if a user enters "Tell me about sports events I can participate in this month" into their device, the server analyzes this request and selects appropriate events based on the conditions.

[0070] The selected information is notified to the user's device. This allows the user to immediately check the details and register to participate. If the user provides feedback after participating in the event, the server will collect this information and use it to improve future events.

[0071] In this way, the system efficiently manages local activities and provides users with a highly convenient platform. An example of a prompt message would be, "Show me three of the latest art events that match the user's interests."

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server collects local activity information from publicly available sources on the internet. Using web scraping technology, the server retrieves event names, dates, times, locations, and participation requirements via an information processing device. The input is local event information websites, and the output is the storage of the retrieved event information in a database. Specifically, the server periodically accesses designated websites to check for new information.

[0075] Step 2:

[0076] Users log in to the system using their own devices and set their areas of interest and available time slots. The device sends this input data to the server, which stores it in a database as a user profile. The input consists of the user's interests and available time slots, while the output is the saved profile information. Specifically, users select their activity areas in a form on the app and set their available time slots using drag-and-drop.

[0077] Step 3:

[0078] When a user requests specific event information, they send a text-based request from their device. The server analyzes this request using a generative AI model. The input is the user's request text, and the output is a database query based on the analysis results. For example, the server analyzes a prompt such as "Tell me about art exhibitions I can go to this weekend" and extracts useful data.

[0079] Step 4:

[0080] Based on the analysis results, the server selects event information that meets the specified criteria from the database and sends it back to the terminal. The input is the analysis query, and the output is the selected event information. Specifically, the server sorts the results by the most recent event date and filters out the appropriate events.

[0081] Step 5:

[0082] Users check the event information notified on their device and register if they wish to participate. The device sends the participation information to the server, which then notifies the event administrator. The input is the user's indication of their intention to participate, and the output is the transmission of the registration information to the administrator. Specifically, the user taps the "Register" button and selects "Confirm" on the confirmation screen.

[0083] Step 6:

[0084] When users provide feedback after participating in an event, they submit ratings and comments using their devices. The server collects this information and statistically processes the rating data to help improve future events. The input is user feedback, and the output is analyzed rating data. Specifically, users write comments and assign star ratings in a rating form within the app.

[0085] (Application Example 1)

[0086] 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."

[0087] In local communities, there is a need to effectively collect information on activities and promote participation. However, providing personalized information based on users' diverse interests and participation history is difficult, and real-time information is not adequately provided. This may lead to a decline in community cohesion and participation.

[0088] 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.

[0089] In this invention, the server includes means for collecting local activity information, means for managing user interest information, and means for analyzing user inquiries using language analysis technology. This enables personalized provision of activity information tailored to the user's interests and real-time information presentation.

[0090] "Means for collecting local activity information" refers to technical means for efficiently collecting information on various activities in local communities from the internet and electronic platforms.

[0091] "Means for managing user interest information" refers to technical means of storing information about individual users' interests and preferences in a database and making it available as needed.

[0092] "Means of analyzing user inquiries using language analysis technology" refers to technical means of understanding and analyzing questions and requests from users using natural language processing technology and deriving appropriate information.

[0093] "Means of presenting activity information in real time" refers to technical means that present collected activity information to users in its most up-to-date state, thereby enabling rapid information sharing.

[0094] "Means of recommending activities based on user interests" refers to technical means of selecting and suggesting activities that users are likely to be interested in, based on their profile and past behavioral history.

[0095] This invention realizes a system that efficiently collects information on local community activities and provides personalized information to users. The system mainly consists of a server, terminals, and users who operate them.

[0096] The server uses a centralized database to centrally collect and manage local activity information. Web scraping techniques are employed for information gathering, targeting publicly available online information from local governments and non-profit organizations. During this process, the Python programming language and libraries such as BeautifulSoup and Requests are used to store the information as structured data.

[0097] Users access the system via a device and create a profile by entering their interests and available time slots. The device is expected to be a mobile device or personal computer, and will be operated through an internet browser or dedicated application.

[0098] The server analyzes user requests using natural language processing techniques that employ machine learning algorithms. This technique is essential for selecting appropriate event information in response to user questions and requests.

[0099] Activity information optimized according to the user's interests is provided in real time by the server. During this process, the user's profile is dynamically updated based on their browsing history and feedback, improving the accuracy of recommendations.

[0100] For example, when a user uses their smartphone to search for "weekend events for children," the system uses the user profile and local event data to provide information on "children's craft workshops" held nearby.

[0101] An example of a generated prompt is: "Create a prompt that recommends events that the user might be interested in. The conditions are 'weekend' and 'kid-friendly'."

[0102] This invention makes it easy for users to find meaningful local activities, thereby promoting the revitalization of local communities.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server automatically collects local activity information via the internet. It uses web scraping techniques to collect data, targeting publicly available online information from local governments and non-profit organizations. It accepts URLs as input, parses the HTML data using BeautifulSoup and the Requests library, and outputs structured data containing information such as event name, date, time, and location.

[0106] Step 2:

[0107] The server stores the collected local activity information in a database. Structured data obtained through web scraping is directly inserted into the database, enabling rapid searching and filtering in subsequent processing. The input is the output data from step 1, and the storage destination is the database.

[0108] Step 3:

[0109] Users access the system from their devices and create their own profiles by entering their interests and available times. The information entered from the device is sent to the server and stored in the user profile database. Based on this input information, information tailored to the user's interests will be provided in subsequent steps.

[0110] Step 4:

[0111] The server uses profile information submitted by the user to analyze it using natural language processing techniques. User requests are input as text data, and machine learning algorithms are used to decipher their content. The analysis results are used to select relevant event information.

[0112] Step 5:

[0113] The server selects the most relevant activity information based on the analysis results and notifies the terminal. Based on the user's profile information and the results of natural language processing, it searches the database, selects the most relevant activity information as output, and sends it to the user's terminal.

[0114] Step 6:

[0115] Users can view suggested activity information using their devices and register on the device if they wish to participate. The registration information is sent to a server and notified to the relevant event administrator. This process enables quick and efficient participation registration.

[0116] Step 7:

[0117] Users provide feedback after participating in an activity. Feedback is entered via a terminal, and the server collects and analyzes it. The evaluations and improvement requests obtained are then incorporated into the next event plan as suggestions using a generative AI model. The feedback data becomes output data that contributes to improving the quality of future activities.

[0118] 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.

[0119] This invention provides users with a more personalized experience by combining an emotion engine with a system that supports local community activities. The system works in cooperation with the server, terminals, and users, each fulfilling its respective role.

[0120] First, the server collects local event information and stores it in a database. This step involves using web scraping techniques or publicly available APIs to obtain the information. The information collected includes the event name, date and time, location, and participation requirements.

[0121] Next, the user creates a user profile by setting their interests and available times using their device. The device sends this information to the server, where it is stored in the database. This profile information is then used to customize the information provided later.

[0122] When a user submits a question or request to the system, the server uses natural language processing techniques to analyze its content and identify the user's needs. In addition, the server uses an emotion engine to analyze the user's emotional state. This emotional state is determined based on the user's input and past interaction data.

[0123] Based on the analysis results, the server selects the most relevant event information, reflecting the user's emotional state, and notifies the device. The device then presents this information to the user, suggesting events that are likely to interest them. This personalized suggestion allows the user to actively participate in activities that interest them.

[0124] For example, if a user requests to "refresh my mood this weekend," the server analyzes the request using natural language processing, and the emotion engine determines that the user needs relaxation. As a result, the server suggests events that can help relieve stress, such as yoga classes or nature walks, and notifies the user via their device.

[0125] Thus, the system of the present invention aims to enable users to engage with local communities in an effective and individually tailored manner, thereby deepening community ties.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server collects local event information from the web. It uses web scraping and APIs to obtain information such as event name, date and time, location, and participation requirements, and stores it in a database.

[0129] Step 2:

[0130] Users log in to the system using their device and enter their preferred event categories and available time slots. The device sends this information to the server, where it is stored in the database as a user profile.

[0131] Step 3:

[0132] Users send requests from their devices to obtain specific information. For example, they might enter a request such as, "Please tell me about art events I can attend this weekend."

[0133] Step 4:

[0134] The server uses natural language processing technology to analyze user requests and understand what information the user needs.

[0135] Step 5:

[0136] The server uses an emotion engine to identify the user's emotional state from their input. It estimates emotions such as "I want to relax" based on the user's wording and the content of their requests.

[0137] Step 6:

[0138] The server searches the database for appropriate events based on the analysis results and emotional state. It prioritizes selecting events that are suitable for the identified emotional state, such as relaxation-related events.

[0139] Step 7:

[0140] The terminal notifies the user of event information sent from the server. The notification includes an overview of the event, its highlights, date and time, and location.

[0141] Step 8:

[0142] Users register for events they are interested in via their device. The device sends the registration information to the server, which processes it to complete the necessary procedures.

[0143] Step 9:

[0144] After the event ends, the server sends a notification to the user's device requesting feedback. The user submits the feedback from their device, and the server stores it in its database.

[0145] Step 10:

[0146] The server analyzes the collected feedback and uses it to suggest future events and improve the user experience. This process continuously improves user satisfaction.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0149] There are challenges in increasing motivation to participate in community activities and providing experiences that meet the individual needs and emotional states of users. These challenges stem from the sheer volume of information available, the inability to accurately reflect the diverse interests of users, and the difficulty in providing information that takes users' emotions into consideration.

[0150] 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.

[0151] In this invention, the server includes means for acquiring information on local activities from information resources, means for recording the user's interests and time, means for analyzing the content of communications using natural language processing, means for evaluating the user's emotional state using emotion analysis technology, and means for selecting and notifying activity information based on the analysis results and evaluated emotions. This makes it possible to suggest optimal activities that are tailored to the user's interests and emotions.

[0152] "Information resources" is a general term for data providers such as websites, databases, and APIs that can be used to obtain information on local activities.

[0153] "Community activities" refer to events, workshops, gatherings, and other activities held in a specific area, with the aim of deepening participants' interaction with the local community.

[0154] A "user" refers to an individual who obtains information about local activities through this system and participates in events that match their interests and needs.

[0155] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is a means of literally interpreting user requests and feedback.

[0156] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotions and psychological state based on their input data and interactions.

[0157] A "predictive model" is a mathematical or machine learning method used to predict future events based on past data and current input information.

[0158] "Personal information" refers to data that includes information associated with a specific individual, such as a user's interests, lifestyle patterns, and past participation history.

[0159] This system aims to deepen relationships with local communities by providing users with information on local activities and encouraging their participation. The system mainly consists of three parties: a server, terminals, and users, each fulfilling its respective role.

[0160] The server is responsible for collecting and managing local activity information from information resources. Here, it obtains information from the internet using web scraping techniques and public APIs. The server stores the collected information in a database and prepares to provide information according to user requests. The server utilizes generative AI models and natural language processing techniques to analyze user requests, and also uses sentiment analysis techniques to evaluate the user's emotional state. By using these technologies, the server provides optimal information tailored to the user's interests and mood.

[0161] The terminal functions as an interface for information entered by the user. The user enters their interests and available time to create a profile. The terminal sends this information to the server, and the server displays the user the most suitable event suggestions received from the server.

[0162] Users can use their devices to enter their interests and available times and send requests to the system. For example, if a user enters, "Please tell me about events I can attend this weekend. I want to refresh my mind," the system will suggest the most suitable events based on that request.

[0163] By repeating this process, the system provides an environment where users can efficiently participate in local activities that they are likely to be interested in, thereby strengthening community ties.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server collects local activity information from information resources. Specifically, it uses web scraping techniques and public APIs to retrieve event information from the internet. Inputs are URLs of information sources and API request parameters, while output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database on the server.

[0167] Step 2:

[0168] Users create a profile by entering their interests and available times using a device. The device then transmits this input information to the server. The input consists of the user's interest categories and time slots, and the output is user profile data containing this information. The server stores this data in a database, which serves as the basis for providing personalized information.

[0169] Step 3:

[0170] The user sends a "prompt message" via the terminal. For example, a request such as, "Please tell me about any events I can attend this weekend. I want to refresh my mood." The terminal sends this message to the server. The input is the text representing the user's request, and the output is the prepared data for parsing in subsequent processing steps.

[0171] Step 4:

[0172] The server analyzes the user's request using a generation AI model and natural language processing technology. The input is the prompt sent in step 3, and the output is the analysis result that extracts the user's needs and intentions. This process involves keyword extraction and contextual understanding.

[0173] Step 5:

[0174] The server uses emotion analysis technology to evaluate the user's emotional state. The input is the needs information analyzed in step 4 and past user data, and the output is data indicating the user's emotional state at that time. The server uses a predictive model to determine the emotion with high accuracy.

[0175] Step 6:

[0176] The server selects appropriate activity information based on analysis results and sentiment evaluations, while referring to user profile information. Inputs are user profile, needs, and sentiment data, and output is selected event information. This information selects activities considered optimal for the user.

[0177] Step 7:

[0178] The server sends the selected event information to the terminal, and the terminal notifies the user. The input is the event information determined in step 6, and the output is the information display as a visual user interface. The terminal is designed to display the information in the most intuitive and easy-to-understand format for the user.

[0179] Step 8:

[0180] Users review event information displayed on their devices and consider participating. Input is the event proposal received from the server, and output is the user's participation actions and feedback. This feedback is collected by the server and used to further improve the service.

[0181] (Application Example 2)

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

[0183] There is a need for appropriate methods to promote participation in local community activities while providing personalized experiences for individual users. However, conventional systems have been unable to suggest the most suitable events while taking into account the user's emotional state. Therefore, the challenge is to enable users to access the information they need in a timely manner and participate more actively in local activities.

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

[0185] In this invention, the server includes means for collecting local event information, means for managing user interest information, means for analyzing the user's emotional state using an emotion engine, means for selecting and notifying event information based on the analysis results and the user's emotional state, and means for collecting and analyzing user feedback. This makes it possible to personalize and suggest the most suitable events according to the user's emotional state.

[0186] "Means for collecting local event information" refers to devices or software that have the function of acquiring and centrally managing data on various events and activities held within a region.

[0187] "Means for managing user interest information" refers to devices or software that acquire data on areas and activities that users are interested in and organize systematically.

[0188] "Means of analyzing user requests using natural language processing" refers to devices or software that use computers to analyze natural language in order to understand linguistic input from users and perform appropriate interpretations.

[0189] "Means for selecting and notifying event information based on analysis results and the user's emotional state" refers to devices or software that propose the most suitable events to the user based on the results of analysis and emotional evaluation, and communicate that information to the user.

[0190] "Means for collecting and analyzing user feedback" refers to devices and software that collect evaluations and opinions from users, analyze the data based on that information, and use it to improve services.

[0191] "Means of using an emotion engine" refers to a device equipped with algorithms and software for evaluating a user's emotional state and generating a response appropriate to that state.

[0192] The system that realizes this invention operates in cooperation with three parties: a server, a terminal, and a user. The server utilizes web scraping technology and public APIs to collect local event information, storing data such as event name, date and time, location, and participation requirements in a database. This data comprehensively covers diverse information regarding local community activities.

[0193] Users use their devices to set their interests and available times to generate a user profile. This profile information is sent to the server and stored in a database. When a request is received from a user via the device, the server uses natural language processing technology to analyze the request and precisely identifies the user's needs through a generative AI model.

[0194] Furthermore, the server uses an emotion engine to analyze the user's emotional state. Based on this, appropriate event information is selected for users who are determined to need relaxation. This selected information is sent to the device, and events that the user is likely to be interested in are suggested. For example, if a user requests to "refresh their mood this weekend," the server identifies and notifies them of events that can help relieve stress.

[0195] For example, if a user enters "I'm a little tired today," they can receive suggestions for refreshing events such as nature walks or yoga classes. In this way, users can receive personalized suggestions through their devices, actively participate in local community activities, and strengthen community ties.

[0196] An example of a prompt is: "Design an application that suggests what events might be helpful when a user feels more relaxed."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server collects local event information using web scraping techniques and public APIs. The input consists of raw data obtained from various websites and APIs, while the output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database.

[0200] Step 2:

[0201] Users create a user profile by entering their interests and available times using a device. The input includes areas of interest and available time slots, and the output is organized user profile information. This information is sent to the server and stored in a database.

[0202] Step 3:

[0203] The user enters questions or requests through the terminal. The input is in the user's natural language, and the output is the request data sent directly to the server. The request is then sent to the server.

[0204] Step 4:

[0205] The server uses a natural language processing engine to analyze the user's request. The input is the request data obtained in step 3, and the output is the result of the request analysis. A generative AI model is used to reveal specific needs.

[0206] Step 5:

[0207] The server uses an emotion engine to analyze the user's emotional state. The input here is the user's past interaction data and current request information, and the output is the user's emotional state. In this step, an algorithm is used to quantify the emotional state.

[0208] Step 6:

[0209] The server selects the most suitable event information based on the analysis results and emotional state, in comparison with the user profile. Inputs include the user profile, request analysis results, and emotional state, while output is recommended event information.

[0210] Step 7:

[0211] The selected event information is notified to the device. The input is the event information obtained in step 6, and the output is a notification displayed to the user on the device. The user receives this information and can participate in the appropriate event.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] The system of this invention operates in cooperation with servers, terminals, and users to support the smooth operation of local community activities. The system centrally manages local event information and provides optimal information based on the user's interests and participation history.

[0229] In this system, the server first collects local event information from the internet. For example, it uses web scraping technology to collect information from event calendars published by local governments and websites run by local non-profit organizations, and stores it in a database. This database includes event names, dates and times, locations, and participation requirements.

[0230] Next, the user logs into the system using their device and sets their areas of interest and available time slots. The device sends this information to the server, where it is stored in the database as a user profile. This allows the system to provide information tailored to the user's interests.

[0231] When a user makes a specific question or request to the system, the server uses natural language processing techniques to analyze the input. For example, if a user requests, "Are there any volunteer activities I can participate in this weekend?", the server will extract relevant event information from the database and select the most suitable one.

[0232] Selected event information is notified to the user's device, allowing them to view the details. Users who wish to register for an event complete the registration process on their device, and this information is sent to the community's event administrator via the server. This process ensures that registration is completed quickly and efficiently.

[0233] After the activity, users provide feedback through their devices. The server collects and analyzes this feedback and uses it to plan future events and optimize services. Specifically, it statistically processes participant evaluations and improvement requests and proposes feasible improvement measures to event organizers.

[0234] Thus, the system according to the present invention aims to enhance community cohesion by efficiently managing event information for local communities and providing a user-friendly participation platform.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server collects local event information. Using web scraping and public APIs, it retrieves data such as event names, dates, times, locations, and participation requirements from various internet sources and stores them in a database.

[0238] Step 2:

[0239] Users log in to the system using their device. Users set their interests and available time slots. The device sends this information to the server, and the user profile is stored in the database.

[0240] Step 3:

[0241] When a user sends a question or request to the system, the server uses natural language processing technology to analyze its content. For example, if a request asks about "volunteer activities this weekend," the server understands the content and extracts relevant keywords.

[0242] Step 4:

[0243] Based on the analysis results, the server searches the database for relevant event information. It prioritizes selecting events that match the user profile's interests and schedule.

[0244] Step 5:

[0245] The terminal notifies the user of event information selected by the server. The notification includes an overview of the event, date and time, location, and details on how to participate.

[0246] Step 6:

[0247] If a user wishes to participate, they register for the event through their device. The device sends the registration information to the server, completes the necessary registration procedures, and sends a registration completion notification to the user.

[0248] Step 7:

[0249] After the event ends, the server sends a notification to the user requesting feedback. The user provides feedback via their device, and the server stores this feedback in a database.

[0250] Step 8:

[0251] The server analyzes the collected feedback to help plan future events and improve services, thereby enhancing the quality of local communication.

[0252] (Example 1)

[0253] 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."

[0254] Information about community activities and events tends to be scattered, making it difficult for residents to find events that suit their interests and schedules. Furthermore, there is no established process for quickly collecting feedback from participants after an event and using that feedback to improve the event. A system is needed to address these inconveniences and challenges and facilitate the smooth operation of community activities.

[0255] 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.

[0256] In this invention, the server includes means for acquiring local activity information using an information processing device, means for organizing data related to the user's interests, and means for understanding the user's inquiries using natural language processing technology. This enables the efficient aggregation of local activity information and the provision of information and incorporation of feedback that meets the user's needs.

[0257] An "information processing system" is a computer system used to acquire, organize, and manage information related to local community activities.

[0258] "Users" refers to individuals or groups who obtain information on local activities and seek event information that matches their interests.

[0259] "Interest-related data" refers to profile information that includes information such as the user's interests, preferences, and available times.

[0260] "Natural language processing technology" is a technique that analyzes text-based requests from users and accurately understands their intent, and generally uses machine learning or generative AI models.

[0261] "Feedback" refers to the impressions, evaluations, or suggestions for improvement that users provide after participating in an event, and this information is used to improve future services.

[0262] The system of this invention is designed to smoothly support community activities and operates in cooperation with servers, terminals, and users. The system aims to aggregate local activity information and provide users with personalized information.

[0263] The server uses an information processing device to acquire local activity information. Specifically, the server uses web scraping technology to collect event information from publicly available sources on the internet. The software used here includes libraries such as Beautiful Soup and Scrapy. This allows the server to efficiently collect data such as event name, date and time, location, and participation requirements, and store it in a database.

[0264] Users access the system using their own devices and enter their areas of interest and available time slots. The devices utilize dedicated applications or web portals to simplify initial setup and operation. Data related to the user's interests is sent to the server and stored in a database as a user profile.

[0265] Furthermore, when a user requests specific activity information, the server analyzes the user's request using natural language processing technology. The technologies used for analysis include generative AI models, such as GPT-based models. For example, if a user enters "Tell me about sports events I can participate in this month" into their device, the server analyzes this request and selects appropriate events based on the conditions.

[0266] The selected information is notified to the user's device. This allows the user to immediately check the details and register to participate. If the user provides feedback after participating in the event, the server will collect this information and use it to improve future events.

[0267] In this way, the system efficiently manages local activities and provides users with a highly convenient platform. An example of a prompt message would be, "Show me three of the latest art events that match the user's interests."

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

[0269] Step 1:

[0270] The server collects local activity information from publicly available sources on the internet. Using web scraping technology, the server retrieves event names, dates, times, locations, and participation requirements via an information processing device. The input is local event information websites, and the output is the storage of the retrieved event information in a database. Specifically, the server periodically accesses designated websites to check for new information.

[0271] Step 2:

[0272] Users log in to the system using their own devices and set their areas of interest and available time slots. The device sends this input data to the server, which stores it in a database as a user profile. The input consists of the user's interests and available time slots, while the output is the saved profile information. Specifically, users select their activity areas in a form on the app and set their available time slots using drag-and-drop.

[0273] Step 3:

[0274] When a user requests specific event information, they send a text-based request from their device. The server analyzes this request using a generative AI model. The input is the user's request text, and the output is a database query based on the analysis results. For example, the server analyzes a prompt such as "Tell me about art exhibitions I can go to this weekend" and extracts useful data.

[0275] Step 4:

[0276] Based on the analysis results, the server selects event information that meets the specified criteria from the database and sends it back to the terminal. The input is the analysis query, and the output is the selected event information. Specifically, the server sorts the results by the most recent event date and filters out the appropriate events.

[0277] Step 5:

[0278] Users check the event information notified on their device and register if they wish to participate. The device sends the participation information to the server, which then notifies the event administrator. The input is the user's indication of their intention to participate, and the output is the transmission of the registration information to the administrator. Specifically, the user taps the "Register" button and selects "Confirm" on the confirmation screen.

[0279] Step 6:

[0280] When a user provides feedback after participating in an event, they send evaluations and comments using a terminal. The server collects this information, statistically processes the evaluation data, and uses it to improve future events. The input is the user's feedback, and the output is the analyzed evaluation data. As a specific operation, the user writes comments in an evaluation form within the app and gives a star rating.

[0281] (Application Example 1)

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

[0283] In local communities, there is a need to effectively collect activity information and promote participation. However, it is difficult to provide individualized information based on the diverse interests and participation histories of users, and real-time information presentation has not been fully realized. As a result, the cohesion and participation awareness of local communities may decline.

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

[0285] In this invention, the server includes means for collecting local activity information, means for managing user interest information, and means for analyzing user inquiries using language analysis technology. This enables the individualized provision of activity information according to users' interests and real-time information presentation.

[0286] The "means for collecting local activity information" is a technical means for efficiently collecting information on various activities in local communities from the Internet or electronic platforms.

[0287] The "means for managing user interest information" is a technical means for storing information on the interests and preferences of individual users in a database and making it available as needed.

[0288] "Means of analyzing user inquiries using language analysis technology" refers to technical means of understanding and analyzing questions and requests from users using natural language processing technology and deriving appropriate information.

[0289] "Means of presenting activity information in real time" refers to technical means that present collected activity information to users in its most up-to-date state, thereby enabling rapid information sharing.

[0290] "Means of recommending activities based on user interests" refers to technical means of selecting and suggesting activities that users are likely to be interested in, based on their profile and past behavioral history.

[0291] This invention realizes a system that efficiently collects information on local community activities and provides personalized information to users. The system mainly consists of a server, terminals, and users who operate them.

[0292] The server uses a centralized database to centrally collect and manage local activity information. Web scraping techniques are employed for information gathering, targeting publicly available online information from local governments and non-profit organizations. During this process, the Python programming language and libraries such as BeautifulSoup and Requests are used to store the information as structured data.

[0293] Users access the system via a device and create a profile by entering their interests and available time slots. The device is expected to be a mobile device or personal computer, and will be operated through an internet browser or dedicated application.

[0294] The server analyzes user requests using natural language processing techniques that employ machine learning algorithms. This technique is essential for selecting appropriate event information in response to user questions and requests.

[0295] Activity information optimized according to the user's interests is provided in real time by the server. During this process, the user's profile is dynamically updated based on their browsing history and feedback, improving the accuracy of recommendations.

[0296] For example, when a user uses their smartphone to search for "weekend events for children," the system uses the user profile and local event data to provide information on "children's craft workshops" held nearby.

[0297] An example of a generated prompt is: "Create a prompt that recommends events that the user might be interested in. The conditions are 'weekend' and 'kid-friendly'."

[0298] This invention makes it easy for users to find meaningful local activities, thereby promoting the revitalization of local communities.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server automatically collects local activity information via the internet. It uses web scraping techniques to collect data, targeting publicly available online information from local governments and non-profit organizations. It accepts URLs as input, parses the HTML data using BeautifulSoup and the Requests library, and outputs structured data containing information such as event name, date, time, and location.

[0302] Step 2:

[0303] The server stores the collected local activity information in a database. Structured data obtained through web scraping is directly inserted into the database, enabling rapid searching and filtering in subsequent processing. The input is the output data from step 1, and the storage destination is the database.

[0304] Step 3:

[0305] The user accesses the system from the terminal, enters their interests and available time slots, and creates their own profile. The information input from the terminal is sent to the server and stored in the user profile database. Based on this input information, information corresponding to the user's interests will be provided in subsequent steps.

[0306] Step 4:

[0307] The server uses the profile information sent from the user and analyzes it using natural language processing technology. The user's request is input as text data, and a machine learning algorithm is utilized to decode its content. The analysis results are used to select relevant event information.

[0308] Step 5:

[0309] The server selects the optimal activity information based on the analysis results and notifies the terminal. Based on the user's profile information and the results of natural language processing, it searches the database, selects the most relevant activity information as the output, and sends it to the user's terminal.

[0310] Step 6:

[0311] The user uses the terminal to check the proposed activity information and, if they wish to participate, registers for participation on the terminal. The registration information is sent to the server, and the system is configured to notify the relevant event administrator. This process enables a quick and efficient participation application.

[0312] Step 7:

[0313] Users provide feedback after participating in an activity. Feedback is entered via a terminal, and the server collects and analyzes it. The evaluations and improvement requests obtained are then incorporated into the next event plan as suggestions using a generative AI model. The feedback data becomes output data that contributes to improving the quality of future activities.

[0314] 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.

[0315] This invention provides users with a more personalized experience by combining an emotion engine with a system that supports local community activities. The system works in cooperation with the server, terminals, and users, each fulfilling its respective role.

[0316] First, the server collects local event information and stores it in a database. This step involves using web scraping techniques or publicly available APIs to obtain the information. The information collected includes the event name, date and time, location, and participation requirements.

[0317] Next, the user creates a user profile by setting their interests and available times using their device. The device sends this information to the server, where it is stored in the database. This profile information is then used to customize the information provided later.

[0318] When a user submits a question or request to the system, the server uses natural language processing techniques to analyze its content and identify the user's needs. In addition, the server uses an emotion engine to analyze the user's emotional state. This emotional state is determined based on the user's input and past interaction data.

[0319] Based on the analysis results, the server selects the most relevant event information, reflecting the user's emotional state, and notifies the device. The device then presents this information to the user, suggesting events that are likely to interest them. This personalized suggestion allows the user to actively participate in activities that interest them.

[0320] For example, if a user requests to "refresh my mood this weekend," the server analyzes the request using natural language processing, and the emotion engine determines that the user needs relaxation. As a result, the server suggests events that can help relieve stress, such as yoga classes or nature walks, and notifies the user via their device.

[0321] Thus, the system of the present invention aims to enable users to engage with local communities in an effective and individually tailored manner, thereby deepening community ties.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server collects local event information from the web. It uses web scraping and APIs to obtain information such as event name, date and time, location, and participation requirements, and stores it in a database.

[0325] Step 2:

[0326] Users log in to the system using their device and enter their preferred event categories and available time slots. The device sends this information to the server, where it is stored in the database as a user profile.

[0327] Step 3:

[0328] Users send requests from their devices to obtain specific information. For example, they might enter a request such as, "Please tell me about art events I can attend this weekend."

[0329] Step 4:

[0330] The server uses natural language processing technology to analyze user requests and understand what information the user needs.

[0331] Step 5:

[0332] The server uses an emotion engine to identify the user's emotional state from their input. It estimates emotions such as "I want to relax" based on the user's wording and the content of their requests.

[0333] Step 6:

[0334] The server searches the database for appropriate events based on the analysis results and emotional state. It prioritizes selecting events that are suitable for the identified emotional state, such as relaxation-related events.

[0335] Step 7:

[0336] The terminal notifies the user of event information sent from the server. The notification includes an overview of the event, its highlights, date and time, and location.

[0337] Step 8:

[0338] Users register for events they are interested in via their device. The device sends the registration information to the server, which processes it to complete the necessary procedures.

[0339] Step 9:

[0340] After the event ends, the server sends a notification to the user's device requesting feedback. The user submits the feedback from their device, and the server stores it in its database.

[0341] Step 10:

[0342] The server analyzes the collected feedback and uses it to suggest future events and improve the user experience. This process continuously improves user satisfaction.

[0343] (Example 2)

[0344] 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".

[0345] There are challenges in increasing motivation to participate in community activities and providing experiences that meet the individual needs and emotional states of users. These challenges stem from the sheer volume of information available, the inability to accurately reflect the diverse interests of users, and the difficulty in providing information that takes users' emotions into consideration.

[0346] 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.

[0347] In this invention, the server includes means for acquiring information on local activities from information resources, means for recording the user's interests and time, means for analyzing the content of communications using natural language processing, means for evaluating the user's emotional state using emotion analysis technology, and means for selecting and notifying activity information based on the analysis results and evaluated emotions. This makes it possible to suggest optimal activities that are tailored to the user's interests and emotions.

[0348] "Information resources" is a general term for data providers such as websites, databases, and APIs that can be used to obtain information on local activities.

[0349] "Community activities" refer to events, workshops, gatherings, and other activities held in a specific area, with the aim of deepening participants' interaction with the local community.

[0350] A "user" refers to an individual who obtains information about local activities through this system and participates in events that match their interests and needs.

[0351] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is a means of literally interpreting user requests and feedback.

[0352] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotions and psychological state based on their input data and interactions.

[0353] A "predictive model" is a mathematical or machine learning method used to predict future events based on past data and current input information.

[0354] "Personal information" refers to data that includes information associated with a specific individual, such as a user's interests, lifestyle patterns, and past participation history.

[0355] This system aims to deepen relationships with local communities by providing users with information on local activities and encouraging their participation. The system mainly consists of three parties: a server, terminals, and users, each fulfilling its respective role.

[0356] The server is responsible for collecting and managing local activity information from information resources. Here, it obtains information from the internet using web scraping techniques and public APIs. The server stores the collected information in a database and prepares to provide information according to user requests. The server utilizes generative AI models and natural language processing techniques to analyze user requests, and also uses sentiment analysis techniques to evaluate the user's emotional state. By using these technologies, the server provides optimal information tailored to the user's interests and mood.

[0357] The terminal functions as an interface for information entered by the user. The user enters their interests and available time to create a profile. The terminal sends this information to the server, and the server displays the user the most suitable event suggestions received from the server.

[0358] Users can use their devices to enter their interests and available times and send requests to the system. For example, if a user enters, "Please tell me about events I can attend this weekend. I want to refresh my mind," the system will suggest the most suitable events based on that request.

[0359] By repeating this process, the system provides an environment where users can efficiently participate in local activities that they are likely to be interested in, thereby strengthening community ties.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The server collects local activity information from information resources. Specifically, it uses web scraping techniques and public APIs to retrieve event information from the internet. Inputs are URLs of information sources and API request parameters, while output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database on the server.

[0363] Step 2:

[0364] Users create a profile by entering their interests and available times using a device. The device then transmits this input information to the server. The input consists of the user's interest categories and time slots, and the output is user profile data containing this information. The server stores this data in a database, which serves as the basis for providing personalized information.

[0365] Step 3:

[0366] The user sends a "prompt message" via the terminal. For example, a request such as, "Please tell me about any events I can attend this weekend. I want to refresh my mood." The terminal sends this message to the server. The input is the text representing the user's request, and the output is the prepared data for parsing in subsequent processing steps.

[0367] Step 4:

[0368] The server analyzes the user's request using a generation AI model and natural language processing technology. The input is the prompt sent in step 3, and the output is the analysis result that extracts the user's needs and intentions. This process involves keyword extraction and contextual understanding.

[0369] Step 5:

[0370] The server uses emotion analysis technology to evaluate the user's emotional state. The input is the needs information analyzed in step 4 and past user data, and the output is data indicating the user's emotional state at that time. The server uses a predictive model to determine the emotion with high accuracy.

[0371] Step 6:

[0372] The server selects appropriate activity information based on analysis results and sentiment evaluations, while referring to user profile information. Inputs are user profile, needs, and sentiment data, and output is selected event information. This information selects activities considered optimal for the user.

[0373] Step 7:

[0374] The server sends the selected event information to the terminal, and the terminal notifies the user. The input is the event information determined in step 6, and the output is the information display as a visual user interface. The terminal is designed to display the information in the most intuitive and easy-to-understand format for the user.

[0375] Step 8:

[0376] Users review event information displayed on their devices and consider participating. Input is the event proposal received from the server, and output is the user's participation actions and feedback. This feedback is collected by the server and used to further improve the service.

[0377] (Application Example 2)

[0378] 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".

[0379] There is a need for appropriate methods to promote participation in local community activities while providing personalized experiences for individual users. However, conventional systems have been unable to suggest the most suitable events while taking into account the user's emotional state. Therefore, the challenge is to enable users to access the information they need in a timely manner and participate more actively in local activities.

[0380] 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.

[0381] In this invention, the server includes means for collecting local event information, means for managing user interest information, means for analyzing the user's emotional state using an emotion engine, means for selecting and notifying event information based on the analysis results and the user's emotional state, and means for collecting and analyzing user feedback. This makes it possible to personalize and suggest the most suitable events according to the user's emotional state.

[0382] "Means for collecting local event information" refers to devices or software that have the function of acquiring and centrally managing data on various events and activities held within a region.

[0383] "Means for managing user interest information" refers to devices or software that acquire data on areas and activities that users are interested in and organize systematically.

[0384] "Means of analyzing user requests using natural language processing" refers to devices or software that use computers to analyze natural language in order to understand linguistic input from users and perform appropriate interpretations.

[0385] "Means for selecting and notifying event information based on analysis results and the user's emotional state" refers to devices or software that propose the most suitable events to the user based on the results of analysis and emotional evaluation, and communicate that information to the user.

[0386] "Means for collecting and analyzing user feedback" refers to devices and software that collect evaluations and opinions from users, analyze the data based on that information, and use it to improve services.

[0387] "Means of using an emotion engine" refers to a device equipped with algorithms and software for evaluating a user's emotional state and generating a response appropriate to that state.

[0388] The system that realizes this invention operates in cooperation with three parties: a server, a terminal, and a user. The server utilizes web scraping technology and public APIs to collect local event information, storing data such as event name, date and time, location, and participation requirements in a database. This data comprehensively covers diverse information regarding local community activities.

[0389] Users use their devices to set their interests and available times to generate a user profile. This profile information is sent to the server and stored in a database. When a request is received from a user via the device, the server uses natural language processing technology to analyze the request and precisely identifies the user's needs through a generative AI model.

[0390] Furthermore, the server uses an emotion engine to analyze the user's emotional state. Based on this, appropriate event information is selected for users who are determined to need relaxation. This selected information is sent to the device, and events that the user is likely to be interested in are suggested. For example, if a user requests to "refresh their mood this weekend," the server identifies and notifies them of events that can help relieve stress.

[0391] For example, if a user enters "I'm a little tired today," they can receive suggestions for refreshing events such as nature walks or yoga classes. In this way, users can receive personalized suggestions through their devices, actively participate in local community activities, and strengthen community ties.

[0392] An example of a prompt is: "Design an application that suggests what events might be helpful when a user feels more relaxed."

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The server collects local event information using web scraping techniques and public APIs. The input consists of raw data obtained from various websites and APIs, while the output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database.

[0396] Step 2:

[0397] Users create a user profile by entering their interests and available times using a device. The input includes areas of interest and available time slots, and the output is organized user profile information. This information is sent to the server and stored in a database.

[0398] Step 3:

[0399] The user enters questions or requests through the terminal. The input is in the user's natural language, and the output is the request data sent directly to the server. The request is then sent to the server.

[0400] Step 4:

[0401] The server uses a natural language processing engine to analyze the user's request. The input is the request data obtained in step 3, and the output is the result of the request analysis. A generative AI model is used to reveal specific needs.

[0402] Step 5:

[0403] The server uses an emotion engine to analyze the user's emotional state. The input here is the user's past interaction data and current request information, and the output is the user's emotional state. In this step, an algorithm is used to quantify the emotional state.

[0404] Step 6:

[0405] The server selects the most suitable event information based on the analysis results and emotional state, in comparison with the user profile. Inputs include the user profile, request analysis results, and emotional state, while output is recommended event information.

[0406] Step 7:

[0407] The selected event information is notified to the device. The input is the event information obtained in step 6, and the output is a notification displayed to the user on the device. The user receives this information and can participate in the appropriate event.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] The system of this invention operates in cooperation with servers, terminals, and users to support the smooth operation of local community activities. The system centrally manages local event information and provides optimal information based on the user's interests and participation history.

[0425] In this system, the server first collects local event information from the internet. For example, it uses web scraping technology to collect information from event calendars published by local governments and websites run by local non-profit organizations, and stores it in a database. This database includes event names, dates and times, locations, and participation requirements.

[0426] Next, the user logs into the system using their device and sets their areas of interest and available time slots. The device sends this information to the server, where it is stored in the database as a user profile. This allows the system to provide information tailored to the user's interests.

[0427] When a user makes a specific question or request to the system, the server uses natural language processing techniques to analyze the input. For example, if a user requests, "Are there any volunteer activities I can participate in this weekend?", the server will extract relevant event information from the database and select the most suitable one.

[0428] Selected event information is notified to the user's device, allowing them to view the details. Users who wish to register for an event complete the registration process on their device, and this information is sent to the community's event administrator via the server. This process ensures that registration is completed quickly and efficiently.

[0429] After the activity, users provide feedback through their devices. The server collects and analyzes this feedback and uses it to plan future events and optimize services. Specifically, it statistically processes participant evaluations and improvement requests and proposes feasible improvement measures to event organizers.

[0430] Thus, the system according to the present invention aims to enhance community cohesion by efficiently managing event information for local communities and providing a user-friendly participation platform.

[0431] The following describes the processing flow.

[0432] Step 1:

[0433] The server collects local event information. Using web scraping and public APIs, it retrieves data such as event names, dates, times, locations, and participation requirements from various internet sources and stores them in a database.

[0434] Step 2:

[0435] Users log in to the system using their device. Users set their interests and available time slots. The device sends this information to the server, and the user profile is stored in the database.

[0436] Step 3:

[0437] When a user sends a question or request to the system, the server uses natural language processing technology to analyze its content. For example, if a request asks about "volunteer activities this weekend," the server understands the content and extracts relevant keywords.

[0438] Step 4:

[0439] Based on the analysis results, the server searches the database for relevant event information. It prioritizes selecting events that match the user profile's interests and schedule.

[0440] Step 5:

[0441] The terminal notifies the user of event information selected by the server. The notification includes an overview of the event, date and time, location, and details on how to participate.

[0442] Step 6:

[0443] If a user wishes to participate, they register for the event through their device. The device sends the registration information to the server, completes the necessary registration procedures, and sends a registration completion notification to the user.

[0444] Step 7:

[0445] After the event ends, the server sends a notification to the user requesting feedback. The user provides feedback via their device, and the server stores this feedback in a database.

[0446] Step 8:

[0447] The server analyzes the collected feedback to help plan future events and improve services, thereby enhancing the quality of local communication.

[0448] (Example 1)

[0449] 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."

[0450] Information about community activities and events tends to be scattered, making it difficult for residents to find events that suit their interests and schedules. Furthermore, there is no established process for quickly collecting feedback from participants after an event and using that feedback to improve the event. A system is needed to address these inconveniences and challenges and facilitate the smooth operation of community activities.

[0451] 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.

[0452] In this invention, the server includes means for acquiring local activity information using an information processing device, means for organizing data related to the user's interests, and means for understanding the user's inquiries using natural language processing technology. This enables the efficient aggregation of local activity information and the provision of information and incorporation of feedback that meets the user's needs.

[0453] An "information processing system" is a computer system used to acquire, organize, and manage information related to local community activities.

[0454] "Users" refers to individuals or groups who obtain information on local activities and seek event information that matches their interests.

[0455] "Interest-related data" refers to profile information that includes information such as the user's interests, preferences, and available times.

[0456] "Natural language processing technology" is a technique that analyzes text-based requests from users and accurately understands their intent, and generally uses machine learning or generative AI models.

[0457] "Feedback" refers to the impressions, evaluations, or suggestions for improvement that users provide after participating in an event, and this information is used to improve future services.

[0458] The system of this invention is designed to smoothly support community activities and operates in cooperation with servers, terminals, and users. The system aims to aggregate local activity information and provide users with personalized information.

[0459] The server uses an information processing device to acquire local activity information. Specifically, the server uses web scraping technology to collect event information from publicly available sources on the internet. The software used here includes libraries such as Beautiful Soup and Scrapy. This allows the server to efficiently collect data such as event name, date and time, location, and participation requirements, and store it in a database.

[0460] Users access the system using their own devices and enter their areas of interest and available time slots. The devices utilize dedicated applications or web portals to simplify initial setup and operation. Data related to the user's interests is sent to the server and stored in a database as a user profile.

[0461] Furthermore, when a user requests specific activity information, the server analyzes the user's request using natural language processing technology. The technologies used for analysis include generative AI models, such as GPT-based models. For example, if a user enters "Tell me about sports events I can participate in this month" into their device, the server analyzes this request and selects appropriate events based on the conditions.

[0462] The selected information is notified to the user's device. This allows the user to immediately check the details and register to participate. If the user provides feedback after participating in the event, the server will collect this information and use it to improve future events.

[0463] In this way, the system efficiently manages local activities and provides users with a highly convenient platform. An example of a prompt message would be, "Show me three of the latest art events that match the user's interests."

[0464] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0465] Step 1:

[0466] The server collects local activity information from publicly available sources on the internet. Using web scraping technology, the server retrieves event names, dates, times, locations, and participation requirements via an information processing device. The input is local event information websites, and the output is the storage of the retrieved event information in a database. Specifically, the server periodically accesses designated websites to check for new information.

[0467] Step 2:

[0468] Users log in to the system using their own devices and set their areas of interest and available time slots. The device sends this input data to the server, which stores it in a database as a user profile. The input consists of the user's interests and available time slots, while the output is the saved profile information. Specifically, users select their activity areas in a form on the app and set their available time slots using drag-and-drop.

[0469] Step 3:

[0470] When a user requests specific event information, they send a text-based request from their device. The server analyzes this request using a generative AI model. The input is the user's request text, and the output is a database query based on the analysis results. For example, the server analyzes a prompt such as "Tell me about art exhibitions I can go to this weekend" and extracts useful data.

[0471] Step 4:

[0472] Based on the analysis results, the server selects event information that meets the specified criteria from the database and sends it back to the terminal. The input is the analysis query, and the output is the selected event information. Specifically, the server sorts the results by the most recent event date and filters out the appropriate events.

[0473] Step 5:

[0474] Users check the event information notified on their device and register if they wish to participate. The device sends the participation information to the server, which then notifies the event administrator. The input is the user's indication of their intention to participate, and the output is the transmission of the registration information to the administrator. Specifically, the user taps the "Register" button and selects "Confirm" on the confirmation screen.

[0475] Step 6:

[0476] When users provide feedback after participating in an event, they submit ratings and comments using their devices. The server collects this information and statistically processes the rating data to help improve future events. The input is user feedback, and the output is analyzed rating data. Specifically, users write comments and assign star ratings in a rating form within the app.

[0477] (Application Example 1)

[0478] 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."

[0479] In local communities, there is a need to effectively collect information on activities and promote participation. However, providing personalized information based on users' diverse interests and participation history is difficult, and real-time information is not adequately provided. This may lead to a decline in community cohesion and participation.

[0480] 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.

[0481] In this invention, the server includes means for collecting local activity information, means for managing user interest information, and means for analyzing user inquiries using language analysis technology. This enables personalized provision of activity information tailored to the user's interests and real-time information presentation.

[0482] "Means for collecting local activity information" refers to technical means for efficiently collecting information on various activities in local communities from the internet and electronic platforms.

[0483] "Means for managing user interest information" refers to technical means of storing information about individual users' interests and preferences in a database and making it available as needed.

[0484] "Means of analyzing user inquiries using language analysis technology" refers to technical means of understanding and analyzing questions and requests from users using natural language processing technology and deriving appropriate information.

[0485] "Means of presenting activity information in real time" refers to technical means that present collected activity information to users in its most up-to-date state, thereby enabling rapid information sharing.

[0486] "Means of recommending activities based on user interests" refers to technical means of selecting and suggesting activities that users are likely to be interested in, based on their profile and past behavioral history.

[0487] This invention realizes a system that efficiently collects information on local community activities and provides personalized information to users. The system mainly consists of a server, terminals, and users who operate them.

[0488] The server uses a centralized database to centrally collect and manage local activity information. Web scraping techniques are employed for information gathering, targeting publicly available online information from local governments and non-profit organizations. During this process, the Python programming language and libraries such as BeautifulSoup and Requests are used to store the information as structured data.

[0489] Users access the system via a device and create a profile by entering their interests and available time slots. The device is expected to be a mobile device or personal computer, and will be operated through an internet browser or dedicated application.

[0490] The server analyzes user requests using natural language processing techniques that employ machine learning algorithms. This technique is essential for selecting appropriate event information in response to user questions and requests.

[0491] Activity information optimized according to the user's interests is provided in real time by the server. During this process, the user's profile is dynamically updated based on their browsing history and feedback, improving the accuracy of recommendations.

[0492] For example, when a user uses their smartphone to search for "weekend events for children," the system uses the user profile and local event data to provide information on "children's craft workshops" held nearby.

[0493] An example of a generated prompt is: "Create a prompt that recommends events that the user might be interested in. The conditions are 'weekend' and 'kid-friendly'."

[0494] This invention makes it easy for users to find meaningful local activities, thereby promoting the revitalization of local communities.

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The server automatically collects local activity information via the internet. It uses web scraping techniques to collect data, targeting publicly available online information from local governments and non-profit organizations. It accepts URLs as input, parses the HTML data using BeautifulSoup and the Requests library, and outputs structured data containing information such as event name, date, time, and location.

[0498] Step 2:

[0499] The server stores the collected local activity information in a database. Structured data obtained through web scraping is directly inserted into the database, enabling rapid searching and filtering in subsequent processing. The input is the output data from step 1, and the storage destination is the database.

[0500] Step 3:

[0501] Users access the system from their devices and create their own profiles by entering their interests and available times. The information entered from the device is sent to the server and stored in the user profile database. Based on this input information, information tailored to the user's interests will be provided in subsequent steps.

[0502] Step 4:

[0503] The server uses profile information submitted by the user to analyze it using natural language processing techniques. User requests are input as text data, and machine learning algorithms are used to decipher their content. The analysis results are used to select relevant event information.

[0504] Step 5:

[0505] The server selects the most relevant activity information based on the analysis results and notifies the terminal. Based on the user's profile information and the results of natural language processing, it searches the database, selects the most relevant activity information as output, and sends it to the user's terminal.

[0506] Step 6:

[0507] Users can view suggested activity information using their devices and register on the device if they wish to participate. The registration information is sent to a server and notified to the relevant event administrator. This process enables quick and efficient participation registration.

[0508] Step 7:

[0509] Users provide feedback after participating in an activity. Feedback is entered via a terminal, and the server collects and analyzes it. The evaluations and improvement requests obtained are then incorporated into the next event plan as suggestions using a generative AI model. The feedback data becomes output data that contributes to improving the quality of future activities.

[0510] 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.

[0511] This invention provides users with a more personalized experience by combining an emotion engine with a system that supports local community activities. The system works in cooperation with the server, terminals, and users, each fulfilling its respective role.

[0512] First, the server collects local event information and stores it in a database. This step involves using web scraping techniques or publicly available APIs to obtain the information. The information collected includes the event name, date and time, location, and participation requirements.

[0513] Next, the user creates a user profile by setting their interests and available times using their device. The device sends this information to the server, where it is stored in the database. This profile information is then used to customize the information provided later.

[0514] When a user submits a question or request to the system, the server uses natural language processing techniques to analyze its content and identify the user's needs. In addition, the server uses an emotion engine to analyze the user's emotional state. This emotional state is determined based on the user's input and past interaction data.

[0515] Based on the analysis results, the server selects the most relevant event information, reflecting the user's emotional state, and notifies the device. The device then presents this information to the user, suggesting events that are likely to interest them. This personalized suggestion allows the user to actively participate in activities that interest them.

[0516] For example, if a user requests to "refresh my mood this weekend," the server analyzes the request using natural language processing, and the emotion engine determines that the user needs relaxation. As a result, the server suggests events that can help relieve stress, such as yoga classes or nature walks, and notifies the user via their device.

[0517] Thus, the system of the present invention aims to enable users to engage with local communities in an effective and individually tailored manner, thereby deepening community ties.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The server collects local event information from the web. It uses web scraping and APIs to obtain information such as event name, date and time, location, and participation requirements, and stores it in a database.

[0521] Step 2:

[0522] Users log in to the system using their device and enter their preferred event categories and available time slots. The device sends this information to the server, where it is stored in the database as a user profile.

[0523] Step 3:

[0524] Users send requests from their devices to obtain specific information. For example, they might enter a request such as, "Please tell me about art events I can attend this weekend."

[0525] Step 4:

[0526] The server uses natural language processing technology to analyze user requests and understand what information the user needs.

[0527] Step 5:

[0528] The server uses an emotion engine to identify the user's emotional state from their input. It estimates emotions such as "I want to relax" based on the user's wording and the content of their requests.

[0529] Step 6:

[0530] The server searches the database for appropriate events based on the analysis results and emotional state. It prioritizes selecting events that are suitable for the identified emotional state, such as relaxation-related events.

[0531] Step 7:

[0532] The terminal notifies the user of event information sent from the server. The notification includes an overview of the event, its highlights, date and time, and location.

[0533] Step 8:

[0534] Users register for events they are interested in via their device. The device sends the registration information to the server, which processes it to complete the necessary procedures.

[0535] Step 9:

[0536] After the event ends, the server sends a notification to the user's device requesting feedback. The user submits the feedback from their device, and the server stores it in its database.

[0537] Step 10:

[0538] The server analyzes the collected feedback and uses it to suggest future events and improve the user experience. This process continuously improves user satisfaction.

[0539] (Example 2)

[0540] 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."

[0541] There are challenges in increasing motivation to participate in community activities and providing experiences that meet the individual needs and emotional states of users. These challenges stem from the sheer volume of information available, the inability to accurately reflect the diverse interests of users, and the difficulty in providing information that takes users' emotions into consideration.

[0542] 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.

[0543] In this invention, the server includes means for acquiring information on local activities from information resources, means for recording the user's interests and time, means for analyzing the content of communications using natural language processing, means for evaluating the user's emotional state using emotion analysis technology, and means for selecting and notifying activity information based on the analysis results and evaluated emotions. This makes it possible to suggest optimal activities that are tailored to the user's interests and emotions.

[0544] "Information resources" is a general term for data providers such as websites, databases, and APIs that can be used to obtain information on local activities.

[0545] "Community activities" refer to events, workshops, gatherings, and other activities held in a specific area, with the aim of deepening participants' interaction with the local community.

[0546] A "user" refers to an individual who obtains information about local activities through this system and participates in events that match their interests and needs.

[0547] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is a means of literally interpreting user requests and feedback.

[0548] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotions and psychological state based on their input data and interactions.

[0549] A "predictive model" is a mathematical or machine learning method used to predict future events based on past data and current input information.

[0550] "Personal information" refers to data that includes information associated with a specific individual, such as a user's interests, lifestyle patterns, and past participation history.

[0551] This system aims to deepen relationships with local communities by providing users with information on local activities and encouraging their participation. The system mainly consists of three parties: a server, terminals, and users, each fulfilling its respective role.

[0552] The server is responsible for collecting and managing local activity information from information resources. Here, it obtains information from the internet using web scraping techniques and public APIs. The server stores the collected information in a database and prepares to provide information according to user requests. The server utilizes generative AI models and natural language processing techniques to analyze user requests, and also uses sentiment analysis techniques to evaluate the user's emotional state. By using these technologies, the server provides optimal information tailored to the user's interests and mood.

[0553] The terminal functions as an interface for information entered by the user. The user enters their interests and available time to create a profile. The terminal sends this information to the server, and the server displays the user the most suitable event suggestions received from the server.

[0554] Users can use their devices to enter their interests and available times and send requests to the system. For example, if a user enters, "Please tell me about events I can attend this weekend. I want to refresh my mind," the system will suggest the most suitable events based on that request.

[0555] By repeating this process, the system provides an environment where users can efficiently participate in local activities that they are likely to be interested in, thereby strengthening community ties.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The server collects local activity information from information resources. Specifically, it uses web scraping techniques and public APIs to retrieve event information from the internet. Inputs are URLs of information sources and API request parameters, while output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database on the server.

[0559] Step 2:

[0560] Users create a profile by entering their interests and available times using a device. The device then transmits this input information to the server. The input consists of the user's interest categories and time slots, and the output is user profile data containing this information. The server stores this data in a database, which serves as the basis for providing personalized information.

[0561] Step 3:

[0562] The user sends a "prompt message" via the terminal. For example, a request such as, "Please tell me about any events I can attend this weekend. I want to refresh my mood." The terminal sends this message to the server. The input is the text representing the user's request, and the output is the prepared data for parsing in subsequent processing steps.

[0563] Step 4:

[0564] The server analyzes the user's request using a generation AI model and natural language processing technology. The input is the prompt sent in step 3, and the output is the analysis result that extracts the user's needs and intentions. This process involves keyword extraction and contextual understanding.

[0565] Step 5:

[0566] The server uses emotion analysis technology to evaluate the user's emotional state. The input is the needs information analyzed in step 4 and past user data, and the output is data indicating the user's emotional state at that time. The server uses a predictive model to determine the emotion with high accuracy.

[0567] Step 6:

[0568] The server selects appropriate activity information based on analysis results and sentiment evaluations, while referring to user profile information. Inputs are user profile, needs, and sentiment data, and output is selected event information. This information selects activities considered optimal for the user.

[0569] Step 7:

[0570] The server sends the selected event information to the terminal, and the terminal notifies the user. The input is the event information determined in step 6, and the output is the information display as a visual user interface. The terminal is designed to display the information in the most intuitive and easy-to-understand format for the user.

[0571] Step 8:

[0572] Users review event information displayed on their devices and consider participating. Input is the event proposal received from the server, and output is the user's participation actions and feedback. This feedback is collected by the server and used to further improve the service.

[0573] (Application Example 2)

[0574] 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."

[0575] There is a need for appropriate methods to promote participation in local community activities while providing personalized experiences for individual users. However, conventional systems have been unable to suggest the most suitable events while taking into account the user's emotional state. Therefore, the challenge is to enable users to access the information they need in a timely manner and participate more actively in local activities.

[0576] 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.

[0577] In this invention, the server includes means for collecting local event information, means for managing user interest information, means for analyzing the user's emotional state using an emotion engine, means for selecting and notifying event information based on the analysis results and the user's emotional state, and means for collecting and analyzing user feedback. This makes it possible to personalize and suggest the most suitable events according to the user's emotional state.

[0578] "Means for collecting local event information" refers to devices or software that have the function of acquiring and centrally managing data on various events and activities held within a region.

[0579] "Means for managing user interest information" refers to devices or software that acquire data on areas and activities that users are interested in and organize systematically.

[0580] "Means of analyzing user requests using natural language processing" refers to devices or software that use computers to analyze natural language in order to understand linguistic input from users and perform appropriate interpretations.

[0581] "Means for selecting and notifying event information based on analysis results and the user's emotional state" refers to devices or software that propose the most suitable events to the user based on the results of analysis and emotional evaluation, and communicate that information to the user.

[0582] "Means for collecting and analyzing user feedback" refers to devices and software that collect evaluations and opinions from users, analyze the data based on that information, and use it to improve services.

[0583] "Means of using an emotion engine" refers to a device equipped with algorithms and software for evaluating a user's emotional state and generating a response appropriate to that state.

[0584] The system that realizes this invention operates in cooperation with three parties: a server, a terminal, and a user. The server utilizes web scraping technology and public APIs to collect local event information, storing data such as event name, date and time, location, and participation requirements in a database. This data comprehensively covers diverse information regarding local community activities.

[0585] Users use their devices to set their interests and available times to generate a user profile. This profile information is sent to the server and stored in a database. When a request is received from a user via the device, the server uses natural language processing technology to analyze the request and precisely identifies the user's needs through a generative AI model.

[0586] Furthermore, the server uses an emotion engine to analyze the user's emotional state. Based on this, appropriate event information is selected for users who are determined to need relaxation. This selected information is sent to the device, and events that the user is likely to be interested in are suggested. For example, if a user requests to "refresh their mood this weekend," the server identifies and notifies them of events that can help relieve stress.

[0587] For example, if a user enters "I'm a little tired today," they can receive suggestions for refreshing events such as nature walks or yoga classes. In this way, users can receive personalized suggestions through their devices, actively participate in local community activities, and strengthen community ties.

[0588] An example of a prompt is: "Design an application that suggests what events might be helpful when a user feels more relaxed."

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The server collects local event information using web scraping techniques and public APIs. The input consists of raw data obtained from various websites and APIs, while the output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database.

[0592] Step 2:

[0593] Users create a user profile by entering their interests and available times using a device. The input includes areas of interest and available time slots, and the output is organized user profile information. This information is sent to the server and stored in a database.

[0594] Step 3:

[0595] The user enters questions or requests through the terminal. The input is in the user's natural language, and the output is the request data sent directly to the server. The request is then sent to the server.

[0596] Step 4:

[0597] The server uses a natural language processing engine to analyze the user's request. The input is the request data obtained in step 3, and the output is the result of the request analysis. A generative AI model is used to reveal specific needs.

[0598] Step 5:

[0599] The server uses an emotion engine to analyze the user's emotional state. The input here is the user's past interaction data and current request information, and the output is the user's emotional state. In this step, an algorithm is used to quantify the emotional state.

[0600] Step 6:

[0601] The server selects the most suitable event information based on the analysis results and emotional state, in comparison with the user profile. Inputs include the user profile, request analysis results, and emotional state, while output is recommended event information.

[0602] Step 7:

[0603] The selected event information is notified to the device. The input is the event information obtained in step 6, and the output is a notification displayed to the user on the device. The user receives this information and can participate in the appropriate event.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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".

[0621] The system of this invention operates in cooperation with servers, terminals, and users to support the smooth operation of local community activities. The system centrally manages local event information and provides optimal information based on the user's interests and participation history.

[0622] In this system, the server first collects local event information from the internet. For example, it uses web scraping technology to collect information from event calendars published by local governments and websites run by local non-profit organizations, and stores it in a database. This database includes event names, dates and times, locations, and participation requirements.

[0623] Next, the user logs into the system using their device and sets their areas of interest and available time slots. The device sends this information to the server, where it is stored in the database as a user profile. This allows the system to provide information tailored to the user's interests.

[0624] When a user makes a specific question or request to the system, the server uses natural language processing techniques to analyze the input. For example, if a user requests, "Are there any volunteer activities I can participate in this weekend?", the server will extract relevant event information from the database and select the most suitable one.

[0625] Selected event information is notified to the user's device, allowing them to view the details. Users who wish to register for an event complete the registration process on their device, and this information is sent to the community's event administrator via the server. This process ensures that registration is completed quickly and efficiently.

[0626] After the activity, users provide feedback through their devices. The server collects and analyzes this feedback and uses it to plan future events and optimize services. Specifically, it statistically processes participant evaluations and improvement requests and proposes feasible improvement measures to event organizers.

[0627] Thus, the system according to the present invention aims to enhance community cohesion by efficiently managing event information for local communities and providing a user-friendly participation platform.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The server collects local event information. Using web scraping and public APIs, it retrieves data such as event names, dates, times, locations, and participation requirements from various internet sources and stores them in a database.

[0631] Step 2:

[0632] Users log in to the system using their device. Users set their interests and available time slots. The device sends this information to the server, and the user profile is stored in the database.

[0633] Step 3:

[0634] When a user sends a question or request to the system, the server uses natural language processing technology to analyze its content. For example, if a request asks about "volunteer activities this weekend," the server understands the content and extracts relevant keywords.

[0635] Step 4:

[0636] Based on the analysis results, the server searches the database for relevant event information. It prioritizes selecting events that match the user profile's interests and schedule.

[0637] Step 5:

[0638] The terminal notifies the user of event information selected by the server. The notification includes an overview of the event, date and time, location, and details on how to participate.

[0639] Step 6:

[0640] If a user wishes to participate, they register for the event through their device. The device sends the registration information to the server, completes the necessary registration procedures, and sends a registration completion notification to the user.

[0641] Step 7:

[0642] After the event ends, the server sends a notification to the user requesting feedback. The user provides feedback via their device, and the server stores this feedback in a database.

[0643] Step 8:

[0644] The server analyzes the collected feedback to help plan future events and improve services, thereby enhancing the quality of local communication.

[0645] (Example 1)

[0646] 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".

[0647] Information about community activities and events tends to be scattered, making it difficult for residents to find events that suit their interests and schedules. Furthermore, there is no established process for quickly collecting feedback from participants after an event and using that feedback to improve the event. A system is needed to address these inconveniences and challenges and facilitate the smooth operation of community activities.

[0648] 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.

[0649] In this invention, the server includes means for acquiring local activity information using an information processing device, means for organizing data related to the user's interests, and means for understanding the user's inquiries using natural language processing technology. This enables the efficient aggregation of local activity information and the provision of information and incorporation of feedback that meets the user's needs.

[0650] An "information processing system" is a computer system used to acquire, organize, and manage information related to local community activities.

[0651] "Users" refers to individuals or groups who obtain information on local activities and seek event information that matches their interests.

[0652] "Interest-related data" refers to profile information that includes information such as the user's interests, preferences, and available times.

[0653] "Natural language processing technology" is a technique that analyzes text-based requests from users and accurately understands their intent, and generally uses machine learning or generative AI models.

[0654] "Feedback" refers to the impressions, evaluations, or suggestions for improvement that users provide after participating in an event, and this information is used to improve future services.

[0655] The system of this invention is designed to smoothly support community activities and operates in cooperation with servers, terminals, and users. The system aims to aggregate local activity information and provide users with personalized information.

[0656] The server uses an information processing device to acquire local activity information. Specifically, the server uses web scraping technology to collect event information from publicly available sources on the internet. The software used here includes libraries such as Beautiful Soup and Scrapy. This allows the server to efficiently collect data such as event name, date and time, location, and participation requirements, and store it in a database.

[0657] Users access the system using their own devices and enter their areas of interest and available time slots. The devices utilize dedicated applications or web portals to simplify initial setup and operation. Data related to the user's interests is sent to the server and stored in a database as a user profile.

[0658] Furthermore, when a user requests specific activity information, the server analyzes the user's request using natural language processing technology. The technologies used for analysis include generative AI models, such as GPT-based models. For example, if a user enters "Tell me about sports events I can participate in this month" into their device, the server analyzes this request and selects appropriate events based on the conditions.

[0659] The selected information is notified to the user's device. This allows the user to immediately check the details and register to participate. If the user provides feedback after participating in the event, the server will collect this information and use it to improve future events.

[0660] In this way, the system efficiently manages local activities and provides users with a highly convenient platform. An example of a prompt message would be, "Show me three of the latest art events that match the user's interests."

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] The server collects local activity information from publicly available sources on the internet. Using web scraping technology, the server retrieves event names, dates, times, locations, and participation requirements via an information processing device. The input is local event information websites, and the output is the storage of the retrieved event information in a database. Specifically, the server periodically accesses designated websites to check for new information.

[0664] Step 2:

[0665] Users log in to the system using their own devices and set their areas of interest and available time slots. The device sends this input data to the server, which stores it in a database as a user profile. The input consists of the user's interests and available time slots, while the output is the saved profile information. Specifically, users select their activity areas in a form on the app and set their available time slots using drag-and-drop.

[0666] Step 3:

[0667] When a user requests specific event information, they send a text-based request from their device. The server analyzes this request using a generative AI model. The input is the user's request text, and the output is a database query based on the analysis results. For example, the server analyzes a prompt such as "Tell me about art exhibitions I can go to this weekend" and extracts useful data.

[0668] Step 4:

[0669] Based on the analysis results, the server selects event information that meets the specified criteria from the database and sends it back to the terminal. The input is the analysis query, and the output is the selected event information. Specifically, the server sorts the results by the most recent event date and filters out the appropriate events.

[0670] Step 5:

[0671] Users check the event information notified on their device and register if they wish to participate. The device sends the participation information to the server, which then notifies the event administrator. The input is the user's indication of their intention to participate, and the output is the transmission of the registration information to the administrator. Specifically, the user taps the "Register" button and selects "Confirm" on the confirmation screen.

[0672] Step 6:

[0673] When users provide feedback after participating in an event, they submit ratings and comments using their devices. The server collects this information and statistically processes the rating data to help improve future events. The input is user feedback, and the output is analyzed rating data. Specifically, users write comments and assign star ratings in a rating form within the app.

[0674] (Application Example 1)

[0675] 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".

[0676] In local communities, there is a need to effectively collect information on activities and promote participation. However, providing personalized information based on users' diverse interests and participation history is difficult, and real-time information is not adequately provided. This may lead to a decline in community cohesion and participation.

[0677] 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.

[0678] In this invention, the server includes means for collecting local activity information, means for managing user interest information, and means for analyzing user inquiries using language analysis technology. This enables personalized provision of activity information tailored to the user's interests and real-time information presentation.

[0679] "Means for collecting local activity information" refers to technical means for efficiently collecting information on various activities in local communities from the internet and electronic platforms.

[0680] "Means for managing user interest information" refers to technical means of storing information about individual users' interests and preferences in a database and making it available as needed.

[0681] "Means of analyzing user inquiries using language analysis technology" refers to technical means of understanding and analyzing questions and requests from users using natural language processing technology and deriving appropriate information.

[0682] "Means of presenting activity information in real time" refers to technical means that present collected activity information to users in its most up-to-date state, thereby enabling rapid information sharing.

[0683] "Means of recommending activities based on user interests" refers to technical means of selecting and suggesting activities that users are likely to be interested in, based on their profile and past behavioral history.

[0684] This invention realizes a system that efficiently collects information on local community activities and provides personalized information to users. The system mainly consists of a server, terminals, and users who operate them.

[0685] The server uses a centralized database to centrally collect and manage local activity information. Web scraping techniques are employed for information gathering, targeting publicly available online information from local governments and non-profit organizations. During this process, the Python programming language and libraries such as BeautifulSoup and Requests are used to store the information as structured data.

[0686] Users access the system via a device and create a profile by entering their interests and available time slots. The device is expected to be a mobile device or personal computer, and will be operated through an internet browser or dedicated application.

[0687] The server analyzes user requests using natural language processing techniques that employ machine learning algorithms. This technique is essential for selecting appropriate event information in response to user questions and requests.

[0688] Activity information optimized according to the user's interests is provided in real time by the server. During this process, the user's profile is dynamically updated based on their browsing history and feedback, improving the accuracy of recommendations.

[0689] For example, when a user uses their smartphone to search for "weekend events for children," the system uses the user profile and local event data to provide information on "children's craft workshops" held nearby.

[0690] An example of a generated prompt is: "Create a prompt that recommends events that the user might be interested in. The conditions are 'weekend' and 'kid-friendly'."

[0691] This invention makes it easy for users to find meaningful local activities, thereby promoting the revitalization of local communities.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The server automatically collects local activity information via the internet. It uses web scraping techniques to collect data, targeting publicly available online information from local governments and non-profit organizations. It accepts URLs as input, parses the HTML data using BeautifulSoup and the Requests library, and outputs structured data containing information such as event name, date, time, and location.

[0695] Step 2:

[0696] The server stores the collected local activity information in a database. Structured data obtained through web scraping is directly inserted into the database, enabling rapid searching and filtering in subsequent processing. The input is the output data from step 1, and the storage destination is the database.

[0697] Step 3:

[0698] Users access the system from their devices and create their own profiles by entering their interests and available times. The information entered from the device is sent to the server and stored in the user profile database. Based on this input information, information tailored to the user's interests will be provided in subsequent steps.

[0699] Step 4:

[0700] The server uses profile information submitted by the user to analyze it using natural language processing techniques. User requests are input as text data, and machine learning algorithms are used to decipher their content. The analysis results are used to select relevant event information.

[0701] Step 5:

[0702] The server selects the most relevant activity information based on the analysis results and notifies the terminal. Based on the user's profile information and the results of natural language processing, it searches the database, selects the most relevant activity information as output, and sends it to the user's terminal.

[0703] Step 6:

[0704] Users can view suggested activity information using their devices and register on the device if they wish to participate. The registration information is sent to a server and notified to the relevant event administrator. This process enables quick and efficient participation registration.

[0705] Step 7:

[0706] Users provide feedback after participating in an activity. Feedback is entered via a terminal, and the server collects and analyzes it. The evaluations and improvement requests obtained are then incorporated into the next event plan as suggestions using a generative AI model. The feedback data becomes output data that contributes to improving the quality of future activities.

[0707] 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.

[0708] This invention provides users with a more personalized experience by combining an emotion engine with a system that supports local community activities. The system works in cooperation with the server, terminals, and users, each fulfilling its respective role.

[0709] First, the server collects local event information and stores it in a database. This step involves using web scraping techniques or publicly available APIs to obtain the information. The information collected includes the event name, date and time, location, and participation requirements.

[0710] Next, the user creates a user profile by setting their interests and available times using their device. The device sends this information to the server, where it is stored in the database. This profile information is then used to customize the information provided later.

[0711] When a user submits a question or request to the system, the server uses natural language processing techniques to analyze its content and identify the user's needs. In addition, the server uses an emotion engine to analyze the user's emotional state. This emotional state is determined based on the user's input and past interaction data.

[0712] Based on the analysis results, the server selects the most relevant event information, reflecting the user's emotional state, and notifies the device. The device then presents this information to the user, suggesting events that are likely to interest them. This personalized suggestion allows the user to actively participate in activities that interest them.

[0713] For example, if a user requests to "refresh my mood this weekend," the server analyzes the request using natural language processing, and the emotion engine determines that the user needs relaxation. As a result, the server suggests events that can help relieve stress, such as yoga classes or nature walks, and notifies the user via their device.

[0714] Thus, the system of the present invention aims to enable users to engage with local communities in an effective and individually tailored manner, thereby deepening community ties.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The server collects local event information from the web. It uses web scraping and APIs to obtain information such as event name, date and time, location, and participation requirements, and stores it in a database.

[0718] Step 2:

[0719] Users log in to the system using their device and enter their preferred event categories and available time slots. The device sends this information to the server, where it is stored in the database as a user profile.

[0720] Step 3:

[0721] Users send requests from their devices to obtain specific information. For example, they might enter a request such as, "Please tell me about art events I can attend this weekend."

[0722] Step 4:

[0723] The server uses natural language processing technology to analyze user requests and understand what information the user needs.

[0724] Step 5:

[0725] The server uses an emotion engine to identify the user's emotional state from their input. It estimates emotions such as "I want to relax" based on the user's wording and the content of their requests.

[0726] Step 6:

[0727] The server searches the database for appropriate events based on the analysis results and emotional state. It prioritizes selecting events that are suitable for the identified emotional state, such as relaxation-related events.

[0728] Step 7:

[0729] The terminal notifies the user of event information sent from the server. The notification includes an overview of the event, its highlights, date and time, and location.

[0730] Step 8:

[0731] Users register for events they are interested in via their device. The device sends the registration information to the server, which processes it to complete the necessary procedures.

[0732] Step 9:

[0733] After the event ends, the server sends a notification to the user's device requesting feedback. The user submits the feedback from their device, and the server stores it in its database.

[0734] Step 10:

[0735] The server analyzes the collected feedback and uses it to suggest future events and improve the user experience. This process continuously improves user satisfaction.

[0736] (Example 2)

[0737] 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".

[0738] There are challenges in increasing motivation to participate in community activities and providing experiences that meet the individual needs and emotional states of users. These challenges stem from the sheer volume of information available, the inability to accurately reflect the diverse interests of users, and the difficulty in providing information that takes users' emotions into consideration.

[0739] 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.

[0740] In this invention, the server includes means for acquiring information on local activities from information resources, means for recording the user's interests and time, means for analyzing the content of communications using natural language processing, means for evaluating the user's emotional state using emotion analysis technology, and means for selecting and notifying activity information based on the analysis results and evaluated emotions. This makes it possible to suggest optimal activities that are tailored to the user's interests and emotions.

[0741] "Information resources" is a general term for data providers such as websites, databases, and APIs that can be used to obtain information on local activities.

[0742] "Community activities" refer to events, workshops, gatherings, and other activities held in a specific area, with the aim of deepening participants' interaction with the local community.

[0743] A "user" refers to an individual who obtains information about local activities through this system and participates in events that match their interests and needs.

[0744] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is a means of literally interpreting user requests and feedback.

[0745] "Emotion analysis technology" refers to algorithms and methods for inferring a user's emotions and psychological state based on their input data and interactions.

[0746] A "predictive model" is a mathematical or machine learning method used to predict future events based on past data and current input information.

[0747] "Personal information" refers to data that includes information associated with a specific individual, such as a user's interests, lifestyle patterns, and past participation history.

[0748] This system aims to deepen relationships with local communities by providing users with information on local activities and encouraging their participation. The system mainly consists of three parties: a server, terminals, and users, each fulfilling its respective role.

[0749] The server is responsible for collecting and managing local activity information from information resources. Here, it obtains information from the internet using web scraping techniques and public APIs. The server stores the collected information in a database and prepares to provide information according to user requests. The server utilizes generative AI models and natural language processing techniques to analyze user requests, and also uses sentiment analysis techniques to evaluate the user's emotional state. By using these technologies, the server provides optimal information tailored to the user's interests and mood.

[0750] The terminal functions as an interface for information entered by the user. The user enters their interests and available time to create a profile. The terminal sends this information to the server, and the server displays the user the most suitable event suggestions received from the server.

[0751] Users can use their devices to enter their interests and available times and send requests to the system. For example, if a user enters, "Please tell me about events I can attend this weekend. I want to refresh my mind," the system will suggest the most suitable events based on that request.

[0752] By repeating this process, the system provides an environment where users can efficiently participate in local activities that they are likely to be interested in, thereby strengthening community ties.

[0753] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0754] Step 1:

[0755] The server collects local activity information from information resources. Specifically, it uses web scraping techniques and public APIs to retrieve event information from the internet. Inputs are URLs of information sources and API request parameters, while output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database on the server.

[0756] Step 2:

[0757] Users create a profile by entering their interests and available times using a device. The device then transmits this input information to the server. The input consists of the user's interest categories and time slots, and the output is user profile data containing this information. The server stores this data in a database, which serves as the basis for providing personalized information.

[0758] Step 3:

[0759] The user sends a "prompt message" via the terminal. For example, a request such as, "Please tell me about any events I can attend this weekend. I want to refresh my mood." The terminal sends this message to the server. The input is the text representing the user's request, and the output is the prepared data for parsing in subsequent processing steps.

[0760] Step 4:

[0761] The server analyzes the user's request using a generation AI model and natural language processing technology. The input is the prompt sent in step 3, and the output is the analysis result that extracts the user's needs and intentions. This process involves keyword extraction and contextual understanding.

[0762] Step 5:

[0763] The server uses emotion analysis technology to evaluate the user's emotional state. The input is the needs information analyzed in step 4 and past user data, and the output is data indicating the user's emotional state at that time. The server uses a predictive model to determine the emotion with high accuracy.

[0764] Step 6:

[0765] The server selects appropriate activity information based on analysis results and sentiment evaluations, while referring to user profile information. Inputs are user profile, needs, and sentiment data, and output is selected event information. This information selects activities considered optimal for the user.

[0766] Step 7:

[0767] The server sends the selected event information to the terminal, and the terminal notifies the user. The input is the event information determined in step 6, and the output is the information display as a visual user interface. The terminal is designed to display the information in the most intuitive and easy-to-understand format for the user.

[0768] Step 8:

[0769] Users review event information displayed on their devices and consider participating. Input is the event proposal received from the server, and output is the user's participation actions and feedback. This feedback is collected by the server and used to further improve the service.

[0770] (Application Example 2)

[0771] 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".

[0772] There is a need for appropriate methods to promote participation in local community activities while providing personalized experiences for individual users. However, conventional systems have been unable to suggest the most suitable events while taking into account the user's emotional state. Therefore, the challenge is to enable users to access the information they need in a timely manner and participate more actively in local activities.

[0773] 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.

[0774] In this invention, the server includes means for collecting local event information, means for managing user interest information, means for analyzing the user's emotional state using an emotion engine, means for selecting and notifying event information based on the analysis results and the user's emotional state, and means for collecting and analyzing user feedback. This makes it possible to personalize and suggest the most suitable events according to the user's emotional state.

[0775] "Means for collecting local event information" refers to devices or software that have the function of acquiring and centrally managing data on various events and activities held within a region.

[0776] "Means for managing user interest information" refers to devices or software that acquire data on areas and activities that users are interested in and organize systematically.

[0777] "Means of analyzing user requests using natural language processing" refers to devices or software that use computers to analyze natural language in order to understand linguistic input from users and perform appropriate interpretations.

[0778] "Means for selecting and notifying event information based on analysis results and the user's emotional state" refers to devices or software that propose the most suitable events to the user based on the results of analysis and emotional evaluation, and communicate that information to the user.

[0779] "Means for collecting and analyzing user feedback" refers to devices and software that collect evaluations and opinions from users, analyze the data based on that information, and use it to improve services.

[0780] "Means of using an emotion engine" refers to a device equipped with algorithms and software for evaluating a user's emotional state and generating a response appropriate to that state.

[0781] The system that realizes this invention operates in cooperation with three parties: a server, a terminal, and a user. The server utilizes web scraping technology and public APIs to collect local event information, storing data such as event name, date and time, location, and participation requirements in a database. This data comprehensively covers diverse information regarding local community activities.

[0782] Users use their devices to set their interests and available times to generate a user profile. This profile information is sent to the server and stored in a database. When a request is received from a user via the device, the server uses natural language processing technology to analyze the request and precisely identifies the user's needs through a generative AI model.

[0783] Furthermore, the server uses an emotion engine to analyze the user's emotional state. Based on this, appropriate event information is selected for users who are determined to need relaxation. This selected information is sent to the device, and events that the user is likely to be interested in are suggested. For example, if a user requests to "refresh their mood this weekend," the server identifies and notifies them of events that can help relieve stress.

[0784] For example, if a user enters "I'm a little tired today," they can receive suggestions for refreshing events such as nature walks or yoga classes. In this way, users can receive personalized suggestions through their devices, actively participate in local community activities, and strengthen community ties.

[0785] An example of a prompt is: "Design an application that suggests what events might be helpful when a user feels more relaxed."

[0786] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0787] Step 1:

[0788] The server collects local event information using web scraping techniques and public APIs. The input consists of raw data obtained from various websites and APIs, while the output is structured data including event name, date and time, location, and participation requirements. This data is stored in a database.

[0789] Step 2:

[0790] Users create a user profile by entering their interests and available times using a device. The input includes areas of interest and available time slots, and the output is organized user profile information. This information is sent to the server and stored in a database.

[0791] Step 3:

[0792] The user enters questions or requests through the terminal. The input is in the user's natural language, and the output is the request data sent directly to the server. The request is then sent to the server.

[0793] Step 4:

[0794] The server uses a natural language processing engine to analyze the user's request. The input is the request data obtained in step 3, and the output is the result of the request analysis. A generative AI model is used to reveal specific needs.

[0795] Step 5:

[0796] The server uses an emotion engine to analyze the user's emotional state. The input here is the user's past interaction data and current request information, and the output is the user's emotional state. In this step, an algorithm is used to quantify the emotional state.

[0797] Step 6:

[0798] The server selects the most suitable event information based on the analysis results and emotional state, in comparison with the user profile. Inputs include the user profile, request analysis results, and emotional state, while output is recommended event information.

[0799] Step 7:

[0800] The selected event information is notified to the device. The input is the event information obtained in step 6, and the output is a notification displayed to the user on the device. The user receives this information and can participate in the appropriate event.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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."

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] The following is further disclosed regarding the embodiments described above.

[0823] (Claim 1)

[0824] Means of collecting local event information,

[0825] A means of managing user interest information,

[0826] A method for analyzing user requests using natural language processing,

[0827] A means of selecting and notifying event information based on the analysis results,

[0828] Means for collecting and analyzing user feedback,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the means for performing the natural language processing uses a machine learning algorithm.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for personalizing notifications based on the user's profile information.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A means of acquiring local activity information using an information processing device,

[0837] A means of organizing data related to user interests,

[0838] A means of understanding user inquiries using natural language processing technology,

[0839] A means of selecting and notifying event information based on the analysis results,

[0840] A means of collecting and evaluating user opinions,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the means for performing natural language analysis uses a learning algorithm.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for personalizing notification content based on the user's profile data.

[0846] "Application Example 1"

[0847] (Claim 1)

[0848] Means of collecting information on local activities,

[0849] A means of managing user interest information,

[0850] A means of analyzing user inquiries using language analysis technology,

[0851] A means of selecting and notifying activity information based on the analysis results,

[0852] A means of collecting and analyzing user reviews,

[0853] A means of presenting activity information in real time,

[0854] A means of recommending activities based on user interests,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the means for performing the language analysis technique is a learning algorithm.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising means for personalizing notifications based on the user's profile information.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] Means of obtaining information on local activities from information resources,

[0863] A means of recording users' interests and time,

[0864] A method for analyzing the content of communications using natural language processing,

[0865] A means of evaluating the emotional state of a user using emotion analysis technology,

[0866] A means of selecting and notifying activity information based on the analysis results and evaluated emotions,

[0867] A means of collecting and analyzing user reviews,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the means for performing natural language processing and sentiment analysis uses a predictive model.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising means for personalizing notifications based on the user's personal information.

[0873] "Application example 2 when combining with an emotional engine"

[0874] (Claim 1)

[0875] Means of collecting local event information,

[0876] A means of managing user interest information,

[0877] A method for analyzing user requests using natural language processing,

[0878] A means for selecting and notifying event information based on analysis results and the user's emotional state,

[0879] Means for collecting and analyzing user feedback,

[0880] A method using an emotion engine to analyze the user's emotional state,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, wherein the means for performing the natural language processing uses a machine learning algorithm.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising means for personalizing notifications based on the user's profile information and emotional state. [Explanation of symbols]

[0886] 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. Means of collecting local event information, A means of managing user interest information, A method for analyzing user requests using natural language processing, A means of selecting and notifying event information based on the analysis results, Means for collecting and analyzing user feedback, A system that includes this.

2. The system according to claim 1, wherein the means for performing the natural language processing uses a machine learning algorithm.

3. The system according to claim 1, further comprising means for personalizing notifications based on the user's profile information.

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