System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and storing user preference and movement allowable range setting data; means for collecting and organizing event information from a plurality of external information sources; means for collating the stored user setting data and the collected event information and providing appropriate event information via a user interface; and feedback processing means for receiving feedback from a user and reflecting the feedback on the setting data and the event information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] There is a challenge in efficiently collecting information about nearby events and suggesting appropriate events based on the user's hobbies and travel allowance. Current event information searches often display uninteresting information and corporate workshops, making it difficult to find major events. Furthermore, data from multiple sources differs, and users often only realize the event after it has ended. This results in missed opportunities to spend their holidays and have new experiences. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. Specifically, the system includes a means for accepting and saving setting data on a user's hobbies, preferences, and allowable travel range, and a means for collecting and organizing event information from multiple external information sources. The system also includes a means for collating the saved user setting data with the collected event information and providing appropriate event information via a user interface. The system also includes a feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information. This means makes it possible to efficiently find and suggest events that suit the user's hobbies and travel range. Furthermore, by notifying event organizers of information using the check-in function, it is possible to increase the number of participants and generate loyalty.

[0006] "Setting data of user's interests and tastes and allowable range of movement" is information indicating the user's interests and concerns and the range within which the user can move.

[0007] "Means for storage" refers to a mechanism for recording and retaining input data in a database or storage device.

[0008] "Multiple external sources" refers to multiple different sources from which event information is provided, such as external websites, APIs, RSS feeds, etc.

[0009] "Event Information" is data about an event or function, including details about the specific date, time, location, and content.

[0010] "Means of collection" refers to the mechanism for obtaining data from external sources and incorporating it into the system.

[0011] "Means of organizing" refers to a system for organizing collected data by classifying, eliminating duplicates, and formatting it.

[0012] The "matching means" is a mechanism for comparing the saved user setting data with the collected event information and identifying a matching combination.

[0013] A "user interface" is an interface that includes graphical or text-based display and input means through which a user interacts with a system.

[0014] "Means for providing" refers to a mechanism for notifying or displaying appropriate information to the user.

[0015] The "feedback processing means" is a mechanism for collecting evaluations and opinions from users and reflecting them in the system.

[0016] "Check-in information" is data indicating that a user has participated in a particular event.

[0017] "Event Organizer" means the person or entity responsible for planning and executing a particular Event.

[0018] The "means of notification" is a mechanism for transmitting collected check-in information, etc. to the event organizer. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's smartphone or computer (hereinafter referred to as "terminal") and processing it on a server.

[0041] 1. User Registration and Settings

[0042] When a user launches the app for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server validates the data and stores it in the database.

[0043] 2. Collecting and organizing event data

[0044] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are combined into a single entry.

[0045] 3. Event Matching

[0046] The server retrieves user preference data from the database and compares it with collected event information. This comparison uses generative AI to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they like music events and can travel up to 30km by car, the server will suggest music events that match this preference.

[0047] 4. Suggestions and Feedback

[0048] Based on the suggestions from the server, the device will notify the user of the most suitable event information or display it within the app. The user can then flag the displayed event information as one they plan to attend. This feedback is sent to the server and reflected in future suggestions.

[0049] 5. Event Details and Check-in

[0050] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user joins an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0051] Specific examples

[0052] For example, suppose a user is interested in "music" and "movies" and has set the distance they can travel by car to 30km. The server also collects information on multiple music festivals and movie screenings from external sources. The server organizes this information and compares it with the user's settings to suggest "music festivals taking place this coming weekend" and "new movie screenings" to the user. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0053] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and then sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information to the server in JSON format.

[0057] Step 2:

[0058] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0059] Step 3:

[0060] The server periodically collects event information from multiple external sources (such as the event site's API or RSS feed), sends API requests, receives responses, and analyzes them.

[0061] Step 4:

[0062] The server organizes the event information it collects. This includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, it merges them into a single entry.

[0063] Step 5:

[0064] The server retrieves user configuration data from a database, including information such as hobbies, preferences, travel allowance, and means of transportation.

[0065] Step 6:

[0066] The server compares the user's settings data with the collected event information. Generative AI is used to select events that best fit the user's hobbies and travel range. For example, if a user likes music events and specifies that they can travel up to 30km by car, the server will suggest music events that match this.

[0067] Step 7:

[0068] The device will notify the user of the event information suggested by the server or display it within the app, and the user can flag the events they are interested in.

[0069] Step 8:

[0070] A user can input a flag of "plan to attend" or feedback for an event that the user is interested in. For example, the user can input comments about whether or not they will attend the event, or about their expectations and interest in the event.

[0071] Step 9:

[0072] The device sends the user's feedback to the server, which stores it in a database and uses it to suggest future events.

[0073] Step 10:

[0074] When a user selects a specific event, the device displays detailed information about the event, such as the date, time, location, participation fee, and background and history information.

[0075] Step 11:

[0076] When a user actually participates in an event, they press the check-in button on their device when they arrive at the event venue. This operation officially registers their intention to participate.

[0077] Step 12:

[0078] The device sends the user's check-in information to the server, which stores it in a database and notifies the event organizer. The organizer can use this information to track the number of participants and take measures to increase loyalty.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] Conventional event suggestion systems provide insufficient event information based on users' hobbies, preferences, and travel allowances, resulting in the risk of users missing events of interest. Furthermore, the lack of a system for effectively organizing collected event information and incorporating user feedback led to a problem of reduced suggestion accuracy. Furthermore, there was a lack of a means to effectively utilize check-in information from users participating in events and provide useful information to event organizers.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes means for accepting and storing setting data on user preferences and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for comparing the stored user setting data with the collected event information using a generative AI model and providing appropriate event information via a user interface, and feedback processing means for accepting feedback from users and reflecting it in the setting data and event information. This makes it possible to provide optimal event information based on the user setting data, improving the accuracy of proposals and providing useful information to organizers.

[0084] "User's interests and travel allowance" is data that indicates the types of activities and events in which the user is interested, as well as the distance and means of transportation that the user is able to travel.

[0085] "Setting data" is a collection of data including a user's hobbies, preferences, and permitted range of movement, as well as other information related to an individual user.

[0086] "External Source" refers to an external data source or service that provides event information (e.g., an event site API or RSS feed).

[0087] "Event information" is data that includes detailed information about an event, such as the date, time, location, and content.

[0088] "Means for storing" refers to a program and hardware for storing received data in a storage device such as a database.

[0089] "Means for collecting and organizing" refers to programs and hardware that have the functionality to obtain event information from external sources and perform deduplication and inconsistency organization.

[0090] A "generative AI model" is a model that uses artificial intelligence technology to analyze and collate data and select appropriate event information.

[0091] "Means for matching and providing" refers to the program and hardware for comparing the user's setting data with the collected event information using a generative AI model and providing the most appropriate event information via the user interface.

[0092] "User interface" refers to the screen and input means that allow a user to interact with a system.

[0093] "Feedback processing means" refers to a program and hardware that has the function of receiving feedback from the user and reflecting it in the system's setting data and event information.

[0094] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's terminal and processing it on a server.

[0095] User Registration and Settings

[0096] When a user launches the app for the first time, an account registration screen is displayed. The user enters information such as their name, email address, password, hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server then validates the received data and checks for any invalid data. Data that passes validation is then stored in the database.

[0097] Event data collection and organization

[0098] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is stored in a database after de-duplication and correction of inconsistencies. The APIs and RSS feeds used as external sources use standard communication protocols (e.g., HTTPS).

[0099] Event Matching

[0100] The server retrieves the user's preference data from the database and compares it with the collected event information. This comparison is performed using a generative AI model. The model uses prompts to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they prefer music events and can travel up to 30km by car, the following prompts can be input into the generative AI model:

[0101] User settings: Music, movies, 30km car travel

[0102] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0103] Based on this input, the generative AI model selects the most appropriate event information.

[0104] Suggestions and Feedback

[0105] The server sends the selected event information to the device. The device notifies the user of the received event information or displays it within the app. The user can flag the displayed event information as one they plan to attend. This feedback information is sent from the device to the server and stored in a database. The server uses this feedback to further optimize future suggestions.

[0106] Event details and check-in

[0107] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user attends an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0108] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1: User Registration and Setup

[0111] Input: The user launches the app on their device for the first time and enters information such as their name, email address, password, hobbies and interests, permitted range of travel, and mode of transportation (car, walking, etc.).

[0112] Processing: The terminal validates the user's input information, converts it into JSON format, and sends it to the server.

[0113] Output: User data sent from the device to the server in JSON format.

[0114] Specific operation: The user enters the required information into the application's registration form and presses the "Submit" button. The device then converts the data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0115] Step 2: Data validation and storage

[0116] Input: The server receives the user data sent from the terminal.

[0117] Processing: The server validates the format and content of the user data and stores it in the database if there are no problems.

[0118] Output: User configuration data stored on the server.

[0119] Specific operation: The server checks the received data based on the specified validation rules, and if there are no errors, it executes an insert query in the database (e.g., MySQL, PostgreSQL) and saves it.

[0120] Step 3: Collect event data

[0121] Input: The server accesses multiple external sources (e.g., event site API, RSS feed).

[0122] Processing: The server periodically calls the API to collect event information.

[0123] Output: A dataset of collected event information on a server.

[0124] Specific operation: The server executes scheduled tasks using Cron jobs, etc., and retrieves event information from API endpoints.

[0125] Step 4: Deduplicate and reshape the data

[0126] Input: A dataset of event information collected by the server.

[0127] Processing: The server eliminates duplicates in the dataset, extracts the necessary information, and stores it in a unified format.

[0128] Output: Event data organized and stored on the server.

[0129] Specific operation: The server eliminates duplicates based on the event ID, title, date and time, etc., and stores them in the database in a normalized format.

[0130] Step 5: Retrieving User Configuration Data

[0131] Input: The server retrieves the configuration data for the specified user from the database.

[0132] Processing: The server extracts the setting data of the relevant user using a select query.

[0133] Output: User configuration data.

[0134] Specific operation: The server queries the database for user setting information based on the user ID and retrieves the setting data.

[0135] Step 6: Event Matching

[0136] Input: The server retrieves user configuration data and organized event data.

[0137] Processing: The server inputs this data into the generative AI model as prompt sentences and selects the most appropriate event information.

[0138] Output: A list of the best event information.

[0139] Specific operation: The server inputs the following prompt sentence into the generative AI model and selects the most appropriate event information.

[0140] User settings: Music, movies, 30km car travel

[0141] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0142] Step 7: Event proposal

[0143] Input: The best event information selected by the server.

[0144] Processing: The server sends the appropriate event information to the device in JSON format.

[0145] Output: Notification of event information from the server to the terminal.

[0146] Specific operation: The server encodes the selected event information into JSON format and sends it to the terminal.

[0147] Step 8: Information Display and Feedback

[0148] Input: Event information received by the device from the server.

[0149] Processing: The device notifies the user of the event information or displays it within the app, and the user sets a flag indicating that they plan to attend.

[0150] Output: Feedback information from the user.

[0151] Specific operation: The device sends a push notification and displays the event information in the app. When the user presses the "Plan to Attend" button, feedback is generated and sent to the server.

[0152] Step 9: Incorporating feedback

[0153] Input: Feedback information sent by the user through the terminal.

[0154] Processing: The server stores the feedback information in a database and uses it as a reference for future suggestions.

[0155] Output: Feedback information stored in a database.

[0156] Specific operation: The server saves the feedback information in the database using an insert query and adds it to the training dataset of the generative AI model.

[0157] Step 10: Event Details and Check-in

[0158] Input: Specific event information selected by the user.

[0159] Processing: The terminal displays detailed information and sends check-in information to the server when the user joins.

[0160] Output: Check-in information sent to the server.

[0161] Specific operation: When the user presses the "Check-in" button on the event details screen, the device sends the check-in information to the server.

[0162] Step 11: Check-in information notification

[0163] Input: The server receives the check-in information sent by the user.

[0164] Processing: The server saves the check-in information in a database and notifies the event organizer.

[0165] Output: Notification to event organizer.

[0166] Specific operation: The server saves the check-in information in a database and calls an API to notify the organizer in real time.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] Conventional event suggestion systems have had problems with low suggestion accuracy and failure to meet user expectations when providing event information based on a user's hobbies, preferences, and travel allowances. They also lacked a mechanism for effectively utilizing user feedback to improve suggestion accuracy. Furthermore, event information collected from external information sources was insufficiently organized and duplicated, making it difficult to quickly provide users with appropriate information. The present invention aims to resolve these problems, provide users with more accurate event suggestions, and improve the user experience.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for accepting and saving setting data on a user's hobbies, preferences, and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for collating the saved user setting data with the collected event information and providing appropriate event information using a generative AI model, feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information, and means for improving the accuracy of the event information with prompt sentences using the generative AI model. This makes it possible to provide more accurate event suggestions to the user and provide information quickly and effectively by organizing the event information and utilizing prompt sentences.

[0172] "Setting data of user's interests, tastes and permissible range of movement" is information entered by the user regarding the types of activities and events in which the user is interested and the range in which the user is allowed to move.

[0173] "Multiple external information sources" refers to multiple information providers, including online event sites, news feeds, and APIs.

[0174] "Event information" refers to information about an event, such as the date, location, content, and participation conditions.

[0175] A "generative AI model" is an artificial intelligence model that selects and suggests optimal events based on user data and collected event information.

[0176] "User interface" refers to the interface through which a user interacts with a system, specifically a smartphone application or a web application.

[0177] The "feedback processing means" is a function that receives opinions and evaluations from users and reflects them in the next proposal.

[0178] A "prompt sentence" is an input sentence that a generative AI model uses to make accurate event suggestions.

[0179] In order to put the present invention into practice, it is necessary to build a system that collects setting data on a user's hobbies, tastes, and permissible range of movement and proposes appropriate event information based on this data. A specific embodiment of the system will be described below.

[0180] First, the user downloads and installs the application from their device. Then, when the user launches the application for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (e.g., car, walking). This information is sent in JSON format to the server, which validates the data and stores it in the database.

[0181] Next, the server periodically collects event information from multiple external sources (e.g., event websites, RSS feeds, APIs). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are merged into a single entry.

[0182] The server then uses the generative AI model to match the saved user preference data and select the most suitable event from the collected event information. The generative AI model then uses prompts to make highly accurate event suggestions. For example, the prompt might read, "Search for events related to music and food within 30km. The user's hobbies are music and food."

[0183] The suggested event information is notified to the user through the user interface or displayed within the app. The user can flag the displayed event information as one they plan to attend, and this feedback is sent to the server and reflected in future suggestions. Furthermore, if the user selects a specific event, detailed information is displayed, and when attending, participation information is sent to the server using the check-in function. This information is also notified to the event organizer.

[0184] The hardware and software that make up this system uses smartphones (Android or iOS), Python 3.x, the Requests library, etc.

[0185] In this way, event information that is most suited to the user's hobbies and travel area can be provided quickly and effectively.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] When a user launches the application on their device for the first time, they register an account. They enter their name, email address, and password, as well as their hobbies, travel allowance, and mode of transportation. This information is converted to JSON format on the device and sent to the server. The server validates this data and stores it in a database. The input is the user's registration information, and the output is the user setting data stored in the database.

[0189] Step 2:

[0190] The server periodically collects event information from multiple external sources, including event site APIs and RSS feeds. The server requests this information, parses the data obtained from the API or RSS, and extracts event information. The input is the data from the external sources, and the output is the parsed event information.

[0191] Step 3:

[0192] The server eliminates duplicates and organizes inconsistencies in the event information it collects. For example, if the same event is obtained from multiple sources, they are combined into a single entry. During this process, the server identifies duplicates based on key information such as the event ID, date and time, and location. The input is unorganized event information, and the output is organized event information.

[0193] Step 4:

[0194] The server uses a generative AI model to match the saved user setting data with the collected event information. As a specific example, the prompt sentence is "Search for event information related to music and food within 30 km. The user's hobbies are music and food." The server matches the results through the generative AI model and selects events that match the user's hobbies, preferences, and travel range. The input is the user setting data and event information, and the output is the event information that is most suitable for the user.

[0195] Step 5:

[0196] The proposed event information is displayed on the device via a user interface or within the app. The user flags the displayed event information as one they plan to attend and sends feedback to the server. The server receives this feedback and reflects it in the database. The input is the proposed event information, and the output is the user's feedback and the results of reflecting it.

[0197] Step 6:

[0198] When a user selects a specific event, the terminal displays detailed information. When a user wants to participate in an event, they check in through the terminal and send the check-in information to the server. The server notifies the event organizer of this check-in information and stores it in a database. The input is the user's check-in information, and the output is a notification to the organizer and storage of the result in the database.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] To put this invention into practice, it is necessary to build a system that collects user preferences and travel allowance data, and then uses an emotion engine that recognizes the user's emotions to suggest appropriate event information. This system consists of a server, a terminal, and an emotion engine, and provides event information through interaction with the user.

[0201] 1. User Registration and Settings

[0202] When a user launches the app for the first time, a registration screen appears. On this screen, the user enters their name, email address, password, as well as their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device then sends this information to the server. The server validates the data and, if there are no problems, stores it in a database.

[0203] 2. Collecting and organizing event data

[0204] The server periodically collects event information from external sources (such as the event site's API or RSS feed). The collected data is organized by the server and stored in a database. This organization process includes deduplication, formatting, and correcting inconsistent data.

[0205] 3. Operation of the Emotion Engine

[0206] The emotion engine is activated when the user is using the system or when certain trigger conditions are met. The emotion engine recognizes and analyzes emotions from the user's facial expressions, voice, and text input. This emotion data is sent to the server and integrated with the user's settings data.

[0207] 4. Event Matching

[0208] The server compares the collected event information with the user's preferences and emotional data. Using generative AI, it selects events that fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, it will prioritize relaxing music events.

[0209] 5. Suggestions and Feedback

[0210] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user can then flag the event as one they plan to attend or enter emotional feedback about the displayed event information. This feedback is sent to the server, stored in a database, and reflected in future suggestions.

[0211] 6. Event Details and Check-in

[0212] When a user selects a specific event, the device displays detailed information about the event, including background and history information. When a user joins an event, they press a check-in button, and the server saves this information in a database and notifies the event organizer.

[0213] Specific examples

[0214] For example, suppose a user is interested in "music" and "movies" and has set the range they can travel by car as 30km. If the emotion engine recognizes that the user's recent emotional data indicates "high stress," the server will suggest a relaxing music festival or a soothing movie screening. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0215] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, providing new experiences and opportunities to participate. Furthermore, by improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is possible to increase the number of participants and generate loyalty.

[0216] The processing flow will be explained below.

[0217] Step 1:

[0218] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and also sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information in JSON format to the server.

[0219] Step 2:

[0220] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0221] Step 3:

[0222] The server periodically collects event information from multiple external sources (event site APIs and RSS feeds), sends API requests, receives responses, and analyzes them.

[0223] Step 4:

[0224] The server organizes the collected event information, which includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, they are merged into a single entry.

[0225] Step 5:

[0226] When a user uses the system, the emotion engine periodically runs. The emotion engine recognizes and analyzes the user's current emotion from their facial expressions, voice, and text input. It generates emotion data and sends this information to the server.

[0227] Step 6:

[0228] The server retrieves and integrates the user's preference data and emotion data from the database, and updates the user's profile based on this integrated data.

[0229] Step 7:

[0230] The server compares the user's integrated data (settings and emotion data) with the collected event information to select appropriate events. Using generative AI, it selects events that best fit the user's hobbies, range of movement, and even emotional state.

[0231] Step 8:

[0232] The terminal notifies or displays the event information sent from the server to the user, and the user can input a "plan to attend" flag or feedback about the displayed event information.

[0233] Step 9:

[0234] The device sends user feedback, such as "planned attendance" information and emotional feedback, to the server, which stores this information in a database and reflects it in future event suggestions.

[0235] Step 10:

[0236] When a user selects a specific event, the device displays detailed information about that event, including the event's date, time, location, participation fee, and background and history information.

[0237] Step 11:

[0238] On the day of the event, the user presses the check-in button and sends the check-in information to the server via the terminal. The server stores the check-in information in a database and notifies the event organizer.

[0239] Step 12:

[0240] After the user returns home from the event, the emotion engine checks the user's emotional state again and sends the data to the server, which updates the user's profile based on the new emotional data and reflects it in future event suggestions.

[0241] Example 2

[0242] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0243] Conventional event suggestion systems have the problem of only considering the user's hobbies and travel range, and are unable to suggest events that reflect the user's emotional state. This makes it difficult to provide optimal event information to users, which leads to a decrease in user satisfaction. Furthermore, the lack of a means to efficiently reflect feedback makes it difficult to improve the accuracy of suggestions.

[0244] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0245] In this invention, the server includes means for accepting and saving setting data on the user's hobbies, preferences, and allowable range of movement; means for collecting and organizing event information from multiple external information sources; emotion engine means for acquiring and analyzing emotion data from the user's facial expressions, voice, and text input; means for collating the saved user setting data with the collected event information, integrating the emotion data, and providing appropriate event information via a user interface; and feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information. This makes it possible to suggest optimal events based on the user's hobbies, range of movement, and even emotional state, thereby improving user satisfaction. Furthermore, reflecting feedback can improve the accuracy of suggestions.

[0246] "Setting data of user's interests and preferences and permissible range of movement" is information relating to the user's fields of interest and permissible range of movement that the user provides to the system.

[0247] "Storage means" refers to a device or program that has the function of temporarily or permanently storing input data.

[0248] An "External Source" is an institution, site, or service that provides event information or data that exists outside the System and is accessible via an API, RSS feed, or similar.

[0249] "Event information" refers to information about the location, date and time, content, and participation conditions of the event.

[0250] The "organizing means" is a device or program that has the function of de-duping acquired data, reformatting it, correcting inconsistent data, and consolidating it into a specified format.

[0251] An "emotion engine" is an algorithm or software module that analyzes emotions from a user's facial expressions, voice, text input, etc., and obtains the results.

[0252] "Matching" is the process of comparing different data sets to find matching items or conditions.

[0253] The "means for providing" is a device or program for notifying or displaying the processed information to the user.

[0254] "Feedback" refers to information about impressions and reactions that users provide to the system after using it.

[0255] The "feedback processing means" is a device or program for collecting and analyzing feedback from users and reflecting the results in the system's setting data and event information.

[0256] To put this invention into practice, a system is constructed that combines a server, a terminal, and an emotion engine. First, when a user launches the app on their terminal for the first time, a registration screen is displayed. On this screen, the user enters their name, email address, password, hobbies and interests, allowable range of travel, and mode of transportation (car, walking, etc.). The terminal sends this information to the server, which then validates the received data and stores it in a database.

[0257] The server periodically collects event information from external sources (event site APIs and RSS feeds), performs processes such as deduplication, formatting, and correcting inconsistent data, and then stores the organized data in a database. When a user is using the system or certain trigger conditions are met, the emotion engine is activated and the device captures emotion data from the user's facial expressions, voice, and text input. This emotion data is sent to the server, analyzed by the emotion engine, and integrated with the user's settings data before being stored in the database.

[0258] The server compares the collected event information with the user's saved preference data and emotional data, and uses a generative AI model to select events that best fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, the system will prioritize relaxing music events. Based on the server's suggestions, the device will notify or display the most appropriate event information to the user.

[0259] Users can input plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database. This feedback is then reflected in future event suggestions. When the user selects a specific event, the device displays detailed information about that event, including information about the event's background and history. When the user actually attends the event, they press the check-in button, and the device sends this information to the server. The server stores the check-in information in a database and notifies the event organizer.

[0260] Specific examples

[0261] For example, if a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine recognizes the user's emotion as "high stress," the server will suggest relaxing music festivals and soothing movie screenings. An example of a prompt for this would be:

[0262] If a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine identifies the user as "high stress," what events should be suggested?

[0263] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, offering new experiences and opportunities to participate. By improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is expected that the number of participants will increase and loyalty will be generated.

[0264] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0265] Step 1:

[0266] User Registration and Settings

[0267] The terminal displays a registration screen to the user. The user enters their name, email address, password, hobbies, travel allowance, and mode of transportation. The entered data is sent from the terminal to the server. The server validates the received data and, if there are no problems, stores it in a database.

[0268] Input: Personal information and settings data that you enter into your device

[0269] Data processing: Data format confirmation and validation

[0270] Output: Saved user configuration data

[0271] Step 2:

[0272] Event data collection and organization

[0273] The server periodically collects event information from APIs and RSS feeds on external event sites. The collected data is organized by the server, and duplicates are eliminated, formatted, and inconsistent data is corrected. The organized data is then stored in a database.

[0274] Input: Event information obtained from an external event site

[0275] Data processing: Removing duplicate data, formatting, correcting inconsistent data

[0276] Output: Organized event data

[0277] Step 3:

[0278] Emotion Engine Operation

[0279] While the user is using the system or when certain trigger conditions are met, the device captures the user's facial expressions, voice, and text input. This emotion data is sent to the server, where the emotion engine analyzes the data and integrates the results into a database.

[0280] Input: User's facial expression, voice, and text data obtained from the device

[0281] Data processing: Emotion data analysis using an emotion engine

[0282] Output: Parsed emotion data

[0283] Step 4:

[0284] Event Matching

[0285] The server compares the collected event information with the user's preferences and emotional data, and uses a generative AI model to select the most suitable event based on the user's hobbies, travel range, and emotional state.

[0286] Input: User preference data, emotion data, and event information

[0287] Data processing: Matching with event information using generative AI models

[0288] Output: Matched event information

[0289] Step 5:

[0290] Suggestions and Feedback

[0291] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user inputs flags for plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database and reflects it in future suggestions.

[0292] Input: Event suggestions from the server, feedback from users

[0293] Data processing: Analysis of feedback and reflection on next proposal

[0294] Output: Updated user preference data and feedback data

[0295] Step 6:

[0296] Event details and check-in

[0297] When the user selects a specific event, the device displays detailed information about the event. When the user joins the event, they press the check-in button. The device sends the check-in information to the server, which stores it in a database. The organizer is notified of the check-in information.

[0298] Input: User selected event, check-in information

[0299] Data processing: Saving check-in information and notifying the organizer

[0300] Output: Database containing check-in information and notified host information

[0301] The above are the processing steps of the program for this system.

[0302] (Application example 2)

[0303] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] Modern logistics centers require management methods to improve worker efficiency, but simply managing work content and schedules is not enough. It is necessary to improve work efficiency and increase worker satisfaction by taking into account the emotions and physical condition of workers and assigning them appropriate tasks. There is also a need for a mechanism to flexibly adjust the system through feedback.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0306] In this invention, the server includes means for accepting and saving setting data on user preferences and allowable movement ranges, means for collecting and organizing event information from multiple external information sources, emotion recognition means for recognizing and saving the user's emotional state from facial expressions and voice, means for collating the saved user setting data, emotion data, and collected event information and providing appropriate event information using a generative AI model, and feedback processing means for accepting feedback from the user via a user interface and reflecting it in the setting data, emotion data, and event information. This makes it possible to assign optimal tasks taking into account the emotional state of workers and flexibly adjust the system based on the feedback.

[0307] "User" refers to a worker who uses this system.

[0308] "Hobbies and interests" refers to the activities and interests that a user likes.

[0309] The "permissible range of movement" refers to the range within which the user can move.

[0310] "Setting data" refers to setting information such as the user's hobbies and preferences, allowable range of movement, and the like.

[0311] "External information source" refers to an information provider that provides external event information.

[0312] "Event information" refers to information about various events obtained from external information sources.

[0313] "Emotion recognition means" refers to means for recognizing the emotional state of a user from their facial expressions and voice.

[0314] "Emotion data" refers to information on the user's emotional state recognized by the emotion recognition means.

[0315] A "generative AI model" refers to an artificial intelligence model that suggests appropriate event information to users based on collected data.

[0316] "User interface" refers to an interface that allows a user to input and output information through an electronic device.

[0317] "Feedback" refers to information collected from users based on their usage, opinions, and experiences.

[0318] "Feedback processing means" refers to means for receiving feedback from users and reflecting it in data.

[0319] The logistics center management system of the present invention is configured as follows in order to optimize the efficiency of users (workers) and improve their work satisfaction.

[0320] User Registration and Settings

[0321] When a user first uses the smart glasses, a registration screen appears on the glasses' display. The user enters their name, ID, job title, preferred work (e.g., picking, inventory), and skills. This information is sent to the server via Wi-Fi and stored in a database.

[0322] Collecting and organizing work data

[0323] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, work environment, etc., and stored in a database. This organization process includes eliminating duplicate tasks, formatting, and correcting inconsistent data.

[0324] emotion recognition

[0325] While the user is wearing the smart glasses, facial recognition and voice analysis are performed using the built-in camera and microphone. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state (e.g., stress, fatigue, concentration). This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[0326] Task Matching

[0327] The server matches the user's preferences and emotional data with ongoing or scheduled tasks. Using a generative AI model (e.g., OpenAI, GPT-4), it selects tasks that best fit the user's skills and emotional state. For example, it suggests lighter tasks to a user who is feeling fatigued.

[0328] Suggestions and Feedback

[0329] Appropriate task suggestions from the server are displayed on the smart glasses display. The user can then accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[0330] Task details and confirmation

[0331] When a user selects a specific task, the smart glasses display detailed information about the task, including the work procedure, work location, and required tools. After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[0332] Hardware used

[0333] Smart glasses (e.g. Google Glass, Vuzix Smart Glasses)

[0334] Emotion engines (e.g., Affectiva, Amazon Rekognition)

[0335] Server (Backend: Node.js, Database: MongoDB)

[0336] User interface (front end: React, display support: AR.js)

[0337] Specific examples

[0338] For example, if Worker A wants to do "picking" and is stressed, the server will suggest lighter work such as tidying shelves. If the feedback is entered as "this suits my current mood," the server will incorporate this feedback data so that similar suggestions will be made more frequently in the future.

[0339] Prompt Sentence Examples

[0340] "Please suggest the best distribution center task for when worker A wants to pick and is under stress."

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] When a user first uses the smart glasses, they enter their name, ID, job title, preferred work, and skills into the registration screen that appears on the glasses' display. This information is then sent via Wi-Fi to the server, which validates the data and stores it in a database.

[0344] Input: User registration information (name, ID, job title, preferred work, special skills)

[0345] Output: User preference data stored in the database

[0346] Step 2:

[0347] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, and work environment, and then duplicate tasks are eliminated, formatted, and inconsistent data is corrected before being stored in the database.

[0348] Input: Task information from WMS

[0349] Output: Organized task information stored in a database

[0350] Step 3:

[0351] While the user is wearing the smart glasses, the glasses' built-in camera and microphone are used for facial recognition and voice analysis. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state. This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[0352] Input: User's facial expression data, voice data

[0353] Output: Emotion data stored on the server

[0354] Step 4:

[0355] The server matches the user's preferences and emotional data with ongoing or scheduled tasks, and uses a generative AI model (e.g., OpenAI, GPT-4) to select the task that best suits the user's skills and emotional state.

[0356] Input: User preference data, emotion data, organized task information

[0357] Output: Optimal task suggestions for the user

[0358] Step 5:

[0359] Task suggestions from the server are displayed on the smart glasses' display. The user can either accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[0360] Input: Task suggestions from the server, user feedback

[0361] Output: Feedback data stored in a database

[0362] Step 6:

[0363] When a user selects a task, the smart glasses display detailed information about the task (such as the work procedure, work location, and required tools). After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[0364] Input: User selects task, confirms completion

[0365] Output: Completed task information stored in the database, notification to administrator

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0372] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0374] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0376] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0377] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0378] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0379] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0381] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0382] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's smartphone or computer (hereinafter referred to as "terminal") and processing it on a server.

[0383] 1. User Registration and Settings

[0384] When a user launches the app for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server validates the data and stores it in the database.

[0385] 2. Collecting and organizing event data

[0386] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are combined into a single entry.

[0387] 3. Event Matching

[0388] The server retrieves user preference data from the database and compares it with collected event information. This comparison uses generative AI to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they like music events and can travel up to 30km by car, the server will suggest music events that match this preference.

[0389] 4. Suggestions and Feedback

[0390] Based on the suggestions from the server, the device will notify the user of the most suitable event information or display it within the app. The user can then flag the displayed event information as one they plan to attend. This feedback is sent to the server and reflected in future suggestions.

[0391] 5. Event Details and Check-in

[0392] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user joins an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0393] Specific examples

[0394] For example, suppose a user is interested in "music" and "movies" and has set the distance they can travel by car to 30km. The server also collects information on multiple music festivals and movie screenings from external sources. The server organizes this information and compares it with the user's settings to suggest "music festivals taking place this coming weekend" and "new movie screenings" to the user. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0395] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0396] The processing flow will be explained below.

[0397] Step 1:

[0398] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and then sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information to the server in JSON format.

[0399] Step 2:

[0400] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0401] Step 3:

[0402] The server periodically collects event information from multiple external sources (such as the event site's API or RSS feed), sends API requests, receives responses, and analyzes them.

[0403] Step 4:

[0404] The server organizes the event information it collects. This includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, it merges them into a single entry.

[0405] Step 5:

[0406] The server retrieves user configuration data from a database, including information such as hobbies, preferences, travel allowance, and means of transportation.

[0407] Step 6:

[0408] The server compares the user's settings data with the collected event information. Generative AI is used to select events that best fit the user's hobbies and travel range. For example, if a user likes music events and specifies that they can travel up to 30km by car, the server will suggest music events that match this.

[0409] Step 7:

[0410] The device will notify the user of the event information suggested by the server or display it within the app, and the user can flag the events they are interested in.

[0411] Step 8:

[0412] A user can input a flag of "plan to attend" or feedback for an event that the user is interested in. For example, the user can input comments about whether or not they will attend the event, or about their expectations and interest in the event.

[0413] Step 9:

[0414] The device sends the user's feedback to the server, which stores it in a database and uses it to suggest future events.

[0415] Step 10:

[0416] When a user selects a specific event, the device displays detailed information about the event, such as the date, time, location, participation fee, and background and history information.

[0417] Step 11:

[0418] When a user actually participates in an event, they press the check-in button on their device when they arrive at the event venue. This operation officially registers their intention to participate.

[0419] Step 12:

[0420] The device sends the user's check-in information to the server, which stores it in a database and notifies the event organizer. The organizer can use this information to track the number of participants and take measures to increase loyalty.

[0421] Example 1

[0422] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0423] Conventional event suggestion systems provide insufficient event information based on users' hobbies, preferences, and travel allowances, resulting in the risk of users missing events of interest. Furthermore, the lack of a system for effectively organizing collected event information and incorporating user feedback led to a problem of reduced suggestion accuracy. Furthermore, there was a lack of a means to effectively utilize check-in information from users participating in events and provide useful information to event organizers.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0425] In this invention, the server includes means for accepting and storing setting data on user preferences and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for comparing the stored user setting data with the collected event information using a generative AI model and providing appropriate event information via a user interface, and feedback processing means for accepting feedback from users and reflecting it in the setting data and event information. This makes it possible to provide optimal event information based on the user setting data, improving the accuracy of proposals and providing useful information to organizers.

[0426] "User's interests and travel allowance" is data that indicates the types of activities and events in which the user is interested, as well as the distance and means of transportation that the user is able to travel.

[0427] "Setting data" is a collection of data including a user's hobbies, preferences, and permitted range of movement, as well as other information related to an individual user.

[0428] "External Source" refers to an external data source or service that provides event information (e.g., an event site API or RSS feed).

[0429] "Event information" is data that includes detailed information about an event, such as the date, time, location, and content.

[0430] "Means for storing" refers to a program and hardware for storing received data in a storage device such as a database.

[0431] "Means for collecting and organizing" refers to programs and hardware that have the functionality to obtain event information from external sources and perform deduplication and inconsistency organization.

[0432] A "generative AI model" is a model that uses artificial intelligence technology to analyze and collate data and select appropriate event information.

[0433] "Means for matching and providing" refers to the program and hardware for comparing the user's setting data with the collected event information using a generative AI model and providing the most appropriate event information via the user interface.

[0434] "User interface" refers to the screen and input means that allow a user to interact with a system.

[0435] "Feedback processing means" refers to a program and hardware that has the function of receiving feedback from the user and reflecting it in the system's setting data and event information.

[0436] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's terminal and processing it on a server.

[0437] User Registration and Settings

[0438] When a user launches the app for the first time, an account registration screen is displayed. The user enters information such as their name, email address, password, hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server then validates the received data and checks for any invalid data. Data that passes validation is then stored in the database.

[0439] Event data collection and organization

[0440] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is stored in a database after de-duplication and correction of inconsistencies. The APIs and RSS feeds used as external sources use standard communication protocols (e.g., HTTPS).

[0441] Event Matching

[0442] The server retrieves the user's preference data from the database and compares it with the collected event information. This comparison is performed using a generative AI model. The model uses prompts to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they prefer music events and can travel up to 30km by car, the following prompts can be input into the generative AI model:

[0443] User settings: Music, movies, 30km car travel

[0444] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0445] Based on this input, the generative AI model selects the most appropriate event information.

[0446] Suggestions and Feedback

[0447] The server sends the selected event information to the device. The device notifies the user of the received event information or displays it within the app. The user can flag the displayed event information as one they plan to attend. This feedback information is sent from the device to the server and stored in a database. The server uses this feedback to further optimize future suggestions.

[0448] Event details and check-in

[0449] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user attends an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0450] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1: User Registration and Setup

[0453] Input: The user launches the app on their device for the first time and enters information such as their name, email address, password, hobbies and interests, permitted range of travel, and mode of transportation (car, walking, etc.).

[0454] Processing: The terminal validates the user's input information, converts it into JSON format, and sends it to the server.

[0455] Output: User data sent from the device to the server in JSON format.

[0456] Specific operation: The user enters the required information into the application's registration form and presses the "Submit" button. The device then converts the data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0457] Step 2: Data validation and storage

[0458] Input: The server receives the user data sent from the terminal.

[0459] Processing: The server validates the format and content of the user data and stores it in the database if there are no problems.

[0460] Output: User configuration data stored on the server.

[0461] Specific operation: The server checks the received data based on the specified validation rules, and if there are no errors, it executes an insert query in the database (e.g., MySQL, PostgreSQL) and saves it.

[0462] Step 3: Collect event data

[0463] Input: The server accesses multiple external sources (e.g., event site API, RSS feed).

[0464] Processing: The server periodically calls the API to collect event information.

[0465] Output: A dataset of collected event information on a server.

[0466] Specific operation: The server executes scheduled tasks using Cron jobs, etc., and retrieves event information from API endpoints.

[0467] Step 4: Deduplicate and reshape the data

[0468] Input: A dataset of event information collected by the server.

[0469] Processing: The server eliminates duplicates in the dataset, extracts the necessary information, and stores it in a unified format.

[0470] Output: Event data organized and stored on the server.

[0471] Specific operation: The server eliminates duplicates based on the event ID, title, date and time, etc., and stores them in the database in a normalized format.

[0472] Step 5: Retrieving User Configuration Data

[0473] Input: The server retrieves the configuration data for the specified user from the database.

[0474] Processing: The server extracts the setting data of the relevant user using a select query.

[0475] Output: User configuration data.

[0476] Specific operation: The server queries the database for user setting information based on the user ID and retrieves the setting data.

[0477] Step 6: Event Matching

[0478] Input: The server retrieves user configuration data and organized event data.

[0479] Processing: The server inputs this data into the generative AI model as prompt sentences and selects the most appropriate event information.

[0480] Output: A list of the best event information.

[0481] Specific operation: The server inputs the following prompt sentence into the generative AI model and selects the most appropriate event information.

[0482] User settings: Music, movies, 30km car travel

[0483] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0484] Step 7: Event proposal

[0485] Input: The best event information selected by the server.

[0486] Processing: The server sends the appropriate event information to the device in JSON format.

[0487] Output: Notification of event information from the server to the terminal.

[0488] Specific operation: The server encodes the selected event information into JSON format and sends it to the terminal.

[0489] Step 8: Information Display and Feedback

[0490] Input: Event information received by the device from the server.

[0491] Processing: The device notifies the user of the event information or displays it within the app, and the user sets a flag indicating that they plan to attend.

[0492] Output: Feedback information from the user.

[0493] Specific operation: The device sends a push notification and displays the event information in the app. When the user presses the "Plan to Attend" button, feedback is generated and sent to the server.

[0494] Step 9: Incorporating feedback

[0495] Input: Feedback information sent by the user through the terminal.

[0496] Processing: The server stores the feedback information in a database and uses it as a reference for future suggestions.

[0497] Output: Feedback information stored in a database.

[0498] Specific operation: The server saves the feedback information in the database using an insert query and adds it to the training dataset of the generative AI model.

[0499] Step 10: Event Details and Check-in

[0500] Input: Specific event information selected by the user.

[0501] Processing: The terminal displays detailed information and sends check-in information to the server when the user joins.

[0502] Output: Check-in information sent to the server.

[0503] Specific operation: When the user presses the "Check-in" button on the event details screen, the device sends the check-in information to the server.

[0504] Step 11: Check-in information notification

[0505] Input: The server receives the check-in information sent by the user.

[0506] Processing: The server saves the check-in information in a database and notifies the event organizer.

[0507] Output: Notification to event organizer.

[0508] Specific operation: The server saves the check-in information in a database and calls an API to notify the organizer in real time.

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] Conventional event suggestion systems have had problems with low suggestion accuracy and failure to meet user expectations when providing event information based on a user's hobbies, preferences, and travel allowances. They also lacked a mechanism for effectively utilizing user feedback to improve suggestion accuracy. Furthermore, event information collected from external information sources was insufficiently organized and duplicated, making it difficult to quickly provide users with appropriate information. The present invention aims to resolve these problems, provide users with more accurate event suggestions, and improve the user experience.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0513] In this invention, the server includes means for accepting and saving setting data on a user's hobbies, preferences, and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for collating the saved user setting data with the collected event information and providing appropriate event information using a generative AI model, feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information, and means for improving the accuracy of the event information with prompt sentences using the generative AI model. This makes it possible to provide more accurate event suggestions to the user and provide information quickly and effectively by organizing the event information and utilizing prompt sentences.

[0514] "Setting data of user's interests, tastes and permissible range of movement" is information entered by the user regarding the types of activities and events in which the user is interested and the range in which the user is allowed to move.

[0515] "Multiple external information sources" refers to multiple information providers, including online event sites, news feeds, and APIs.

[0516] "Event information" refers to information about an event, such as the date, location, content, and participation conditions.

[0517] A "generative AI model" is an artificial intelligence model that selects and suggests optimal events based on user data and collected event information.

[0518] "User interface" refers to the interface through which a user interacts with a system, specifically a smartphone application or a web application.

[0519] The "feedback processing means" is a function that receives opinions and evaluations from users and reflects them in the next proposal.

[0520] A "prompt sentence" is an input sentence that a generative AI model uses to make accurate event suggestions.

[0521] In order to put the present invention into practice, it is necessary to build a system that collects setting data on a user's hobbies, tastes, and permissible range of movement and proposes appropriate event information based on this data. A specific embodiment of the system will be described below.

[0522] First, the user downloads and installs the application from their device. Then, when the user launches the application for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (e.g., car, walking). This information is sent in JSON format to the server, which validates the data and stores it in the database.

[0523] Next, the server periodically collects event information from multiple external sources (e.g., event websites, RSS feeds, APIs). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are merged into a single entry.

[0524] The server then uses the generative AI model to match the saved user preference data and select the most suitable event from the collected event information. The generative AI model then uses prompts to make highly accurate event suggestions. For example, the prompt might read, "Search for events related to music and food within 30km. The user's hobbies are music and food."

[0525] The suggested event information is notified to the user through the user interface or displayed within the app. The user can flag the displayed event information as one they plan to attend, and this feedback is sent to the server and reflected in future suggestions. Furthermore, if the user selects a specific event, detailed information is displayed, and when attending, participation information is sent to the server using the check-in function. This information is also notified to the event organizer.

[0526] The hardware and software that make up this system uses smartphones (Android or iOS), Python 3.x, the Requests library, etc.

[0527] In this way, event information that is most suited to the user's hobbies and travel area can be provided quickly and effectively.

[0528] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0529] Step 1:

[0530] When a user launches the application on their device for the first time, they register an account. They enter their name, email address, and password, as well as their hobbies, travel allowance, and mode of transportation. This information is converted to JSON format on the device and sent to the server. The server validates this data and stores it in a database. The input is the user's registration information, and the output is the user setting data stored in the database.

[0531] Step 2:

[0532] The server periodically collects event information from multiple external sources, including event site APIs and RSS feeds. The server requests this information, parses the data obtained from the API or RSS, and extracts event information. The input is the data from the external sources, and the output is the parsed event information.

[0533] Step 3:

[0534] The server eliminates duplicates and organizes inconsistencies in the event information it collects. For example, if the same event is obtained from multiple sources, they are combined into a single entry. During this process, the server identifies duplicates based on key information such as the event ID, date and time, and location. The input is unorganized event information, and the output is organized event information.

[0535] Step 4:

[0536] The server uses a generative AI model to match the saved user setting data with the collected event information. As a specific example, the prompt sentence is "Search for event information related to music and food within 30 km. The user's hobbies are music and food." The server matches the results through the generative AI model and selects events that match the user's hobbies, preferences, and travel range. The input is the user setting data and event information, and the output is the event information that is most suitable for the user.

[0537] Step 5:

[0538] The proposed event information is displayed on the device via a user interface or within the app. The user flags the displayed event information as one they plan to attend and sends feedback to the server. The server receives this feedback and reflects it in the database. The input is the proposed event information, and the output is the user's feedback and the results of reflecting it.

[0539] Step 6:

[0540] When a user selects a specific event, the terminal displays detailed information. When a user wants to participate in an event, they check in through the terminal and send the check-in information to the server. The server notifies the event organizer of this check-in information and stores it in a database. The input is the user's check-in information, and the output is a notification to the organizer and storage of the result in the database.

[0541] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0542] To put this invention into practice, it is necessary to build a system that collects user preferences and travel allowance data, and then uses an emotion engine that recognizes the user's emotions to suggest appropriate event information. This system consists of a server, a terminal, and an emotion engine, and provides event information through interaction with the user.

[0543] 1. User Registration and Settings

[0544] When a user launches the app for the first time, a registration screen appears. On this screen, the user enters their name, email address, password, as well as their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device then sends this information to the server. The server validates the data and, if there are no problems, stores it in a database.

[0545] 2. Collecting and organizing event data

[0546] The server periodically collects event information from external sources (such as the event site's API or RSS feed). The collected data is organized by the server and stored in a database. This organization process includes deduplication, formatting, and correcting inconsistent data.

[0547] 3. Operation of the Emotion Engine

[0548] The emotion engine is activated when the user is using the system or when certain trigger conditions are met. The emotion engine recognizes and analyzes emotions from the user's facial expressions, voice, and text input. This emotion data is sent to the server and integrated with the user's settings data.

[0549] 4. Event Matching

[0550] The server compares the collected event information with the user's preferences and emotional data. Using generative AI, it selects events that fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, it will prioritize relaxing music events.

[0551] 5. Suggestions and Feedback

[0552] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user can then flag the event as one they plan to attend or enter emotional feedback about the displayed event information. This feedback is sent to the server, stored in a database, and reflected in future suggestions.

[0553] 6. Event Details and Check-in

[0554] When a user selects a specific event, the device displays detailed information about the event, including background and history information. When a user joins an event, they press a check-in button, and the server saves this information in a database and notifies the event organizer.

[0555] Specific examples

[0556] For example, suppose a user is interested in "music" and "movies" and has set the range they can travel by car as 30km. If the emotion engine recognizes that the user's recent emotional data indicates "high stress," the server will suggest a relaxing music festival or a soothing movie screening. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0557] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, providing new experiences and opportunities to participate. Furthermore, by improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is possible to increase the number of participants and generate loyalty.

[0558] The processing flow will be explained below.

[0559] Step 1:

[0560] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and also sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information in JSON format to the server.

[0561] Step 2:

[0562] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0563] Step 3:

[0564] The server periodically collects event information from multiple external sources (event site APIs and RSS feeds), sends API requests, receives responses, and analyzes them.

[0565] Step 4:

[0566] The server organizes the collected event information, which includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, they are merged into a single entry.

[0567] Step 5:

[0568] When a user uses the system, the emotion engine periodically runs. The emotion engine recognizes and analyzes the user's current emotion from their facial expressions, voice, and text input. It generates emotion data and sends this information to the server.

[0569] Step 6:

[0570] The server retrieves and integrates the user's preference data and emotion data from the database, and updates the user's profile based on this integrated data.

[0571] Step 7:

[0572] The server compares the user's integrated data (settings and emotion data) with the collected event information to select appropriate events. Using generative AI, it selects events that best fit the user's hobbies, range of movement, and even emotional state.

[0573] Step 8:

[0574] The terminal notifies or displays the event information sent from the server to the user, and the user can input a "plan to attend" flag or feedback about the displayed event information.

[0575] Step 9:

[0576] The device sends user feedback, such as "planned attendance" information and emotional feedback, to the server, which stores this information in a database and reflects it in future event suggestions.

[0577] Step 10:

[0578] When a user selects a specific event, the device displays detailed information about that event, including the event's date, time, location, participation fee, and background and history information.

[0579] Step 11:

[0580] On the day of the event, the user presses the check-in button and sends the check-in information to the server via the terminal. The server stores the check-in information in a database and notifies the event organizer.

[0581] Step 12:

[0582] After the user returns home from the event, the emotion engine checks the user's emotional state again and sends the data to the server, which updates the user's profile based on the new emotional data and reflects it in future event suggestions.

[0583] Example 2

[0584] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0585] Conventional event suggestion systems have the problem of only considering the user's hobbies and travel range, and are unable to suggest events that reflect the user's emotional state. This makes it difficult to provide optimal event information to users, which leads to a decrease in user satisfaction. Furthermore, the lack of a means to efficiently reflect feedback makes it difficult to improve the accuracy of suggestions.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0587] In this invention, the server includes means for accepting and saving setting data on the user's hobbies, preferences, and allowable range of movement; means for collecting and organizing event information from multiple external information sources; emotion engine means for acquiring and analyzing emotion data from the user's facial expressions, voice, and text input; means for collating the saved user setting data with the collected event information, integrating the emotion data, and providing appropriate event information via a user interface; and feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information. This makes it possible to suggest optimal events based on the user's hobbies, range of movement, and even emotional state, thereby improving user satisfaction. Furthermore, reflecting feedback can improve the accuracy of suggestions.

[0588] "Setting data of user's interests and preferences and permissible range of movement" is information relating to the user's fields of interest and permissible range of movement that the user provides to the system.

[0589] "Storage means" refers to a device or program that has the function of temporarily or permanently storing input data.

[0590] An "External Source" is an institution, site, or service that provides event information or data that exists outside the System and is accessible via an API, RSS feed, or similar.

[0591] "Event information" refers to information about the location, date and time, content, and participation conditions of the event.

[0592] The "organizing means" is a device or program that has the function of de-duping acquired data, reformatting it, correcting inconsistent data, and consolidating it into a specified format.

[0593] An "emotion engine" is an algorithm or software module that analyzes emotions from a user's facial expressions, voice, text input, etc., and obtains the results.

[0594] "Matching" is the process of comparing different data sets to find matching items or conditions.

[0595] The "means for providing" is a device or program for notifying or displaying the processed information to the user.

[0596] "Feedback" refers to information about impressions and reactions that users provide to the system after using it.

[0597] The "feedback processing means" is a device or program for collecting and analyzing feedback from users and reflecting the results in the system's setting data and event information.

[0598] To put this invention into practice, a system is constructed that combines a server, a terminal, and an emotion engine. First, when a user launches the app on their terminal for the first time, a registration screen is displayed. On this screen, the user enters their name, email address, password, hobbies and interests, allowable range of travel, and mode of transportation (car, walking, etc.). The terminal sends this information to the server, which then validates the received data and stores it in a database.

[0599] The server periodically collects event information from external sources (event site APIs and RSS feeds), performs processes such as deduplication, formatting, and correcting inconsistent data, and then stores the organized data in a database. When a user is using the system or certain trigger conditions are met, the emotion engine is activated and the device captures emotion data from the user's facial expressions, voice, and text input. This emotion data is sent to the server, analyzed by the emotion engine, and integrated with the user's settings data before being stored in the database.

[0600] The server compares the collected event information with the user's saved preference data and emotional data, and uses a generative AI model to select events that best fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, the system will prioritize relaxing music events. Based on the server's suggestions, the device will notify or display the most appropriate event information to the user.

[0601] Users can input plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database. This feedback is then reflected in future event suggestions. When the user selects a specific event, the device displays detailed information about that event, including information about the event's background and history. When the user actually attends the event, they press the check-in button, and the device sends this information to the server. The server stores the check-in information in a database and notifies the event organizer.

[0602] Specific examples

[0603] For example, if a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine recognizes the user's emotion as "high stress," the server will suggest relaxing music festivals and soothing movie screenings. An example of a prompt for this would be:

[0604] If a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine identifies the user as "high stress," what events should be suggested?

[0605] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, offering new experiences and opportunities to participate. By improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is expected that the number of participants will increase and loyalty will be generated.

[0606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0607] Step 1:

[0608] User Registration and Settings

[0609] The terminal displays a registration screen to the user. The user enters their name, email address, password, hobbies, travel allowance, and mode of transportation. The entered data is sent from the terminal to the server. The server validates the received data and, if there are no problems, stores it in a database.

[0610] Input: Personal information and settings data that you enter into your device

[0611] Data processing: Data format confirmation and validation

[0612] Output: Saved user configuration data

[0613] Step 2:

[0614] Event data collection and organization

[0615] The server periodically collects event information from APIs and RSS feeds on external event sites. The collected data is organized by the server, and duplicates are eliminated, formatted, and inconsistent data is corrected. The organized data is then stored in a database.

[0616] Input: Event information obtained from an external event site

[0617] Data processing: Removing duplicate data, formatting, correcting inconsistent data

[0618] Output: Organized event data

[0619] Step 3:

[0620] Emotion Engine Operation

[0621] While the user is using the system or when certain trigger conditions are met, the device captures the user's facial expressions, voice, and text input. This emotion data is sent to the server, where the emotion engine analyzes the data and integrates the results into a database.

[0622] Input: User's facial expression, voice, and text data obtained from the device

[0623] Data processing: Emotion data analysis using an emotion engine

[0624] Output: Parsed emotion data

[0625] Step 4:

[0626] Event Matching

[0627] The server compares the collected event information with the user's preferences and emotional data, and uses a generative AI model to select the most suitable event based on the user's hobbies, travel range, and emotional state.

[0628] Input: User preference data, emotion data, and event information

[0629] Data processing: Matching with event information using generative AI models

[0630] Output: Matched event information

[0631] Step 5:

[0632] Suggestions and Feedback

[0633] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user inputs flags for plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database and reflects it in future suggestions.

[0634] Input: Event suggestions from the server, feedback from users

[0635] Data processing: Analysis of feedback and reflection on next proposal

[0636] Output: Updated user preference data and feedback data

[0637] Step 6:

[0638] Event details and check-in

[0639] When the user selects a specific event, the device displays detailed information about the event. When the user joins the event, they press the check-in button. The device sends the check-in information to the server, which stores it in a database. The organizer is notified of the check-in information.

[0640] Input: User selected event, check-in information

[0641] Data processing: Saving check-in information and notifying the organizer

[0642] Output: Database containing check-in information and notified host information

[0643] The above are the processing steps of the program for this system.

[0644] (Application example 2)

[0645] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] Modern logistics centers require management methods to improve worker efficiency, but simply managing work content and schedules is not enough. It is necessary to improve work efficiency and increase worker satisfaction by taking into account the emotions and physical condition of workers and assigning them appropriate tasks. There is also a need for a mechanism to flexibly adjust the system through feedback.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0648] In this invention, the server includes means for accepting and saving setting data on user preferences and allowable movement ranges, means for collecting and organizing event information from multiple external information sources, emotion recognition means for recognizing and saving the user's emotional state from facial expressions and voice, means for collating the saved user setting data, emotion data, and collected event information and providing appropriate event information using a generative AI model, and feedback processing means for accepting feedback from the user via a user interface and reflecting it in the setting data, emotion data, and event information. This makes it possible to assign optimal tasks taking into account the emotional state of workers and flexibly adjust the system based on the feedback.

[0649] "User" refers to a worker who uses this system.

[0650] "Hobbies and interests" refers to the activities and interests that a user likes.

[0651] The "permissible range of movement" refers to the range within which the user can move.

[0652] "Setting data" refers to setting information such as the user's hobbies and preferences, allowable range of movement, and the like.

[0653] "External information source" refers to an information provider that provides external event information.

[0654] "Event information" refers to information about various events obtained from external information sources.

[0655] "Emotion recognition means" refers to means for recognizing the emotional state of a user from their facial expressions and voice.

[0656] "Emotion data" refers to information on the user's emotional state recognized by the emotion recognition means.

[0657] A "generative AI model" refers to an artificial intelligence model that suggests appropriate event information to users based on collected data.

[0658] "User interface" refers to an interface that allows a user to input and output information through an electronic device.

[0659] "Feedback" refers to information collected from users based on their usage, opinions, and experiences.

[0660] "Feedback processing means" refers to means for receiving feedback from users and reflecting it in data.

[0661] The logistics center management system of the present invention is configured as follows in order to optimize the efficiency of users (workers) and improve their work satisfaction.

[0662] User Registration and Settings

[0663] When a user first uses the smart glasses, a registration screen appears on the glasses' display. The user enters their name, ID, job title, preferred work (e.g., picking, inventory), and skills. This information is sent to the server via Wi-Fi and stored in a database.

[0664] Collecting and organizing work data

[0665] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, work environment, etc., and stored in a database. This organization process includes eliminating duplicate tasks, formatting, and correcting inconsistent data.

[0666] emotion recognition

[0667] While the user is wearing the smart glasses, facial recognition and voice analysis are performed using the built-in camera and microphone. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state (e.g., stress, fatigue, concentration). This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[0668] Task Matching

[0669] The server matches the user's preferences and emotional data with ongoing or scheduled tasks. Using a generative AI model (e.g., OpenAI, GPT-4), it selects tasks that best fit the user's skills and emotional state. For example, it suggests lighter tasks to a user who is feeling fatigued.

[0670] Suggestions and Feedback

[0671] Appropriate task suggestions from the server are displayed on the smart glasses display. The user can then accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[0672] Task details and confirmation

[0673] When a user selects a specific task, the smart glasses display detailed information about the task, including the work procedure, work location, and required tools. After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[0674] Hardware used

[0675] Smart glasses (e.g. Google Glass, Vuzix Smart Glasses)

[0676] Emotion engines (e.g., Affectiva, Amazon Rekognition)

[0677] Server (Backend: Node.js, Database: MongoDB)

[0678] User interface (front end: React, display support: AR.js)

[0679] Specific examples

[0680] For example, if Worker A wants to do "picking" and is stressed, the server will suggest lighter work such as tidying shelves. If the feedback is entered as "this suits my current mood," the server will incorporate this feedback data so that similar suggestions will be made more frequently in the future.

[0681] Prompt Sentence Examples

[0682] "Please suggest the best distribution center task for when worker A wants to pick and is under stress."

[0683] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0684] Step 1:

[0685] When a user first uses the smart glasses, they enter their name, ID, job title, preferred work, and skills into the registration screen that appears on the glasses' display. This information is then sent via Wi-Fi to the server, which validates the data and stores it in a database.

[0686] Input: User registration information (name, ID, job title, preferred work, special skills)

[0687] Output: User preference data stored in the database

[0688] Step 2:

[0689] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, and work environment, and then duplicate tasks are eliminated, formatted, and inconsistent data is corrected before being stored in the database.

[0690] Input: Task information from WMS

[0691] Output: Organized task information stored in a database

[0692] Step 3:

[0693] While the user is wearing the smart glasses, the glasses' built-in camera and microphone are used for facial recognition and voice analysis. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state. This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[0694] Input: User's facial expression data, voice data

[0695] Output: Emotion data stored on the server

[0696] Step 4:

[0697] The server matches the user's preferences and emotional data with ongoing or scheduled tasks, and uses a generative AI model (e.g., OpenAI, GPT-4) to select the task that best suits the user's skills and emotional state.

[0698] Input: User preference data, emotion data, organized task information

[0699] Output: Optimal task suggestions for the user

[0700] Step 5:

[0701] Task suggestions from the server are displayed on the smart glasses' display. The user can either accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[0702] Input: Task suggestions from the server, user feedback

[0703] Output: Feedback data stored in a database

[0704] Step 6:

[0705] When a user selects a task, the smart glasses display detailed information about the task (such as the work procedure, work location, and required tools). After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[0706] Input: User selects task, confirms completion

[0707] Output: Completed task information stored in the database, notification to administrator

[0708] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0719] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0720] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0721] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's smartphone or computer (hereinafter referred to as "terminal") and processing it on a server.

[0725] 1. User Registration and Settings

[0726] When a user launches the app for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server validates the data and stores it in the database.

[0727] 2. Collecting and organizing event data

[0728] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are combined into a single entry.

[0729] 3. Event Matching

[0730] The server retrieves user preference data from the database and compares it with collected event information. This comparison uses generative AI to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they like music events and can travel up to 30km by car, the server will suggest music events that match this preference.

[0731] 4. Suggestions and Feedback

[0732] Based on the suggestions from the server, the device will notify the user of the most suitable event information or display it within the app. The user can then flag the displayed event information as one they plan to attend. This feedback is sent to the server and reflected in future suggestions.

[0733] 5. Event Details and Check-in

[0734] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user joins an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0735] Specific examples

[0736] For example, suppose a user is interested in "music" and "movies" and has set the distance they can travel by car to 30km. The server also collects information on multiple music festivals and movie screenings from external sources. The server organizes this information and compares it with the user's settings to suggest "music festivals taking place this coming weekend" and "new movie screenings" to the user. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0737] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0738] The processing flow will be explained below.

[0739] Step 1:

[0740] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and then sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information to the server in JSON format.

[0741] Step 2:

[0742] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0743] Step 3:

[0744] The server periodically collects event information from multiple external sources (such as the event site's API or RSS feed), sends API requests, receives responses, and analyzes them.

[0745] Step 4:

[0746] The server organizes the event information it collects. This includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, it merges them into a single entry.

[0747] Step 5:

[0748] The server retrieves user configuration data from a database, including information such as hobbies, preferences, travel allowance, and means of transportation.

[0749] Step 6:

[0750] The server compares the user's settings data with the collected event information. Generative AI is used to select events that best fit the user's hobbies and travel range. For example, if a user likes music events and specifies that they can travel up to 30km by car, the server will suggest music events that match this.

[0751] Step 7:

[0752] The device will notify the user of the event information suggested by the server or display it within the app, and the user can flag the events they are interested in.

[0753] Step 8:

[0754] A user can input a flag of "plan to attend" or feedback for an event that the user is interested in. For example, the user can input comments about whether or not they will attend the event, or about their expectations and interest in the event.

[0755] Step 9:

[0756] The device sends the user's feedback to the server, which stores it in a database and uses it to suggest future events.

[0757] Step 10:

[0758] When a user selects a specific event, the device displays detailed information about the event, such as the date, time, location, participation fee, and background and history information.

[0759] Step 11:

[0760] When a user actually participates in an event, they press the check-in button on their device when they arrive at the event venue. This operation officially registers their intention to participate.

[0761] Step 12:

[0762] The device sends the user's check-in information to the server, which stores it in a database and notifies the event organizer. The organizer can use this information to track the number of participants and take measures to increase loyalty.

[0763] Example 1

[0764] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0765] Conventional event suggestion systems provide insufficient event information based on users' hobbies, preferences, and travel allowances, resulting in the risk of users missing events of interest. Furthermore, the lack of a system for effectively organizing collected event information and incorporating user feedback led to a problem of reduced suggestion accuracy. Furthermore, there was a lack of a means to effectively utilize check-in information from users participating in events and provide useful information to event organizers.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0767] In this invention, the server includes means for accepting and storing setting data on user preferences and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for comparing the stored user setting data with the collected event information using a generative AI model and providing appropriate event information via a user interface, and feedback processing means for accepting feedback from users and reflecting it in the setting data and event information. This makes it possible to provide optimal event information based on the user setting data, improving the accuracy of proposals and providing useful information to organizers.

[0768] "User's interests and travel allowance" is data that indicates the types of activities and events in which the user is interested, as well as the distance and means of transportation that the user is able to travel.

[0769] "Setting data" is a collection of data including a user's hobbies, preferences, and permitted range of movement, as well as other information related to an individual user.

[0770] "External Source" refers to an external data source or service that provides event information (e.g., an event site API or RSS feed).

[0771] "Event information" is data that includes detailed information about an event, such as the date, time, location, and content.

[0772] "Means for storing" refers to a program and hardware for storing received data in a storage device such as a database.

[0773] "Means for collecting and organizing" refers to programs and hardware that have the functionality to obtain event information from external sources and perform deduplication and inconsistency organization.

[0774] A "generative AI model" is a model that uses artificial intelligence technology to analyze and collate data and select appropriate event information.

[0775] "Means for matching and providing" refers to the program and hardware for comparing the user's setting data with the collected event information using a generative AI model and providing the most appropriate event information via the user interface.

[0776] "User interface" refers to the screen and input means that allow a user to interact with a system.

[0777] "Feedback processing means" refers to a program and hardware that has the function of receiving feedback from the user and reflecting it in the system's setting data and event information.

[0778] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's terminal and processing it on a server.

[0779] User Registration and Settings

[0780] When a user launches the app for the first time, an account registration screen is displayed. The user enters information such as their name, email address, password, hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server then validates the received data and checks for any invalid data. Data that passes validation is then stored in the database.

[0781] Event data collection and organization

[0782] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is stored in a database after de-duplication and correction of inconsistencies. The APIs and RSS feeds used as external sources use standard communication protocols (e.g., HTTPS).

[0783] Event Matching

[0784] The server retrieves the user's preference data from the database and compares it with the collected event information. This comparison is performed using a generative AI model. The model uses prompts to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they prefer music events and can travel up to 30km by car, the following prompts can be input into the generative AI model:

[0785] User settings: Music, movies, 30km car travel

[0786] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0787] Based on this input, the generative AI model selects the most appropriate event information.

[0788] Suggestions and Feedback

[0789] The server sends the selected event information to the device. The device notifies the user of the received event information or displays it within the app. The user can flag the displayed event information as one they plan to attend. This feedback information is sent from the device to the server and stored in a database. The server uses this feedback to further optimize future suggestions.

[0790] Event details and check-in

[0791] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user attends an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[0792] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[0793] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0794] Step 1: User Registration and Setup

[0795] Input: The user launches the app on their device for the first time and enters information such as their name, email address, password, hobbies and interests, permitted range of travel, and mode of transportation (car, walking, etc.).

[0796] Processing: The terminal validates the user's input information, converts it into JSON format, and sends it to the server.

[0797] Output: User data sent from the device to the server in JSON format.

[0798] Specific operation: The user enters the required information into the application's registration form and presses the "Submit" button. The device then converts the data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[0799] Step 2: Data validation and storage

[0800] Input: The server receives the user data sent from the terminal.

[0801] Processing: The server validates the format and content of the user data and stores it in the database if there are no problems.

[0802] Output: User configuration data stored on the server.

[0803] Specific operation: The server checks the received data based on the specified validation rules, and if there are no errors, it executes an insert query in the database (e.g., MySQL, PostgreSQL) and saves it.

[0804] Step 3: Collect event data

[0805] Input: The server accesses multiple external sources (e.g., event site API, RSS feed).

[0806] Processing: The server periodically calls the API to collect event information.

[0807] Output: A dataset of collected event information on a server.

[0808] Specific operation: The server executes scheduled tasks using Cron jobs, etc., and retrieves event information from API endpoints.

[0809] Step 4: Deduplicate and reshape the data

[0810] Input: A dataset of event information collected by the server.

[0811] Processing: The server eliminates duplicates in the dataset, extracts the necessary information, and stores it in a unified format.

[0812] Output: Event data organized and stored on the server.

[0813] Specific operation: The server eliminates duplicates based on the event ID, title, date and time, etc., and stores them in the database in a normalized format.

[0814] Step 5: Retrieving User Configuration Data

[0815] Input: The server retrieves the configuration data for the specified user from the database.

[0816] Processing: The server extracts the setting data of the relevant user using a select query.

[0817] Output: User configuration data.

[0818] Specific operation: The server queries the database for user setting information based on the user ID and retrieves the setting data.

[0819] Step 6: Event Matching

[0820] Input: The server retrieves user configuration data and organized event data.

[0821] Processing: The server inputs this data into the generative AI model as prompt sentences and selects the most appropriate event information.

[0822] Output: A list of the best event information.

[0823] Specific operation: The server inputs the following prompt sentence into the generative AI model and selects the most appropriate event information.

[0824] User settings: Music, movies, 30km car travel

[0825] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[0826] Step 7: Event proposal

[0827] Input: The best event information selected by the server.

[0828] Processing: The server sends the appropriate event information to the device in JSON format.

[0829] Output: Notification of event information from the server to the terminal.

[0830] Specific operation: The server encodes the selected event information into JSON format and sends it to the terminal.

[0831] Step 8: Information Display and Feedback

[0832] Input: Event information received by the device from the server.

[0833] Processing: The device notifies the user of the event information or displays it within the app, and the user sets a flag indicating that they plan to attend.

[0834] Output: Feedback information from the user.

[0835] Specific operation: The device sends a push notification and displays the event information in the app. When the user presses the "Plan to Attend" button, feedback is generated and sent to the server.

[0836] Step 9: Incorporating feedback

[0837] Input: Feedback information sent by the user through the terminal.

[0838] Processing: The server stores the feedback information in a database and uses it as a reference for future suggestions.

[0839] Output: Feedback information stored in a database.

[0840] Specific operation: The server saves the feedback information in the database using an insert query and adds it to the training dataset of the generative AI model.

[0841] Step 10: Event Details and Check-in

[0842] Input: Specific event information selected by the user.

[0843] Processing: The terminal displays detailed information and sends check-in information to the server when the user joins.

[0844] Output: Check-in information sent to the server.

[0845] Specific operation: When the user presses the "Check-in" button on the event details screen, the device sends the check-in information to the server.

[0846] Step 11: Check-in information notification

[0847] Input: The server receives the check-in information sent by the user.

[0848] Processing: The server saves the check-in information in a database and notifies the event organizer.

[0849] Output: Notification to event organizer.

[0850] Specific operation: The server saves the check-in information in a database and calls an API to notify the organizer in real time.

[0851] (Application example 1)

[0852] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0853] Conventional event suggestion systems have had problems with low suggestion accuracy and failure to meet user expectations when providing event information based on a user's hobbies, preferences, and travel allowances. They also lacked a mechanism for effectively utilizing user feedback to improve suggestion accuracy. Furthermore, event information collected from external information sources was insufficiently organized and duplicated, making it difficult to quickly provide users with appropriate information. The present invention aims to resolve these problems, provide users with more accurate event suggestions, and improve the user experience.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0855] In this invention, the server includes means for accepting and saving setting data on a user's hobbies, preferences, and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for collating the saved user setting data with the collected event information and providing appropriate event information using a generative AI model, feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information, and means for improving the accuracy of the event information with prompt sentences using the generative AI model. This makes it possible to provide more accurate event suggestions to the user and provide information quickly and effectively by organizing the event information and utilizing prompt sentences.

[0856] "Setting data of user's interests, tastes and permissible range of movement" is information entered by the user regarding the types of activities and events in which the user is interested and the range in which the user is allowed to move.

[0857] "Multiple external information sources" refers to multiple information providers, including online event sites, news feeds, and APIs.

[0858] "Event information" refers to information about an event, such as the date, location, content, and participation conditions.

[0859] A "generative AI model" is an artificial intelligence model that selects and suggests optimal events based on user data and collected event information.

[0860] "User interface" refers to the interface through which a user interacts with a system, specifically a smartphone application or a web application.

[0861] The "feedback processing means" is a function that receives opinions and evaluations from users and reflects them in the next proposal.

[0862] A "prompt sentence" is an input sentence that a generative AI model uses to make accurate event suggestions.

[0863] In order to put the present invention into practice, it is necessary to build a system that collects setting data on a user's hobbies, tastes, and permissible range of movement and proposes appropriate event information based on this data. A specific embodiment of the system will be described below.

[0864] First, the user downloads and installs the application from their device. Then, when the user launches the application for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (e.g., car, walking). This information is sent in JSON format to the server, which validates the data and stores it in the database.

[0865] Next, the server periodically collects event information from multiple external sources (e.g., event websites, RSS feeds, APIs). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are merged into a single entry.

[0866] The server then uses the generative AI model to match the saved user preference data and select the most suitable event from the collected event information. The generative AI model then uses prompts to make highly accurate event suggestions. For example, the prompt might read, "Search for events related to music and food within 30km. The user's hobbies are music and food."

[0867] The suggested event information is notified to the user through the user interface or displayed within the app. The user can flag the displayed event information as one they plan to attend, and this feedback is sent to the server and reflected in future suggestions. Furthermore, if the user selects a specific event, detailed information is displayed, and when attending, participation information is sent to the server using the check-in function. This information is also notified to the event organizer.

[0868] The hardware and software that make up this system uses smartphones (Android or iOS), Python 3.x, the Requests library, etc.

[0869] In this way, event information that is most suited to the user's hobbies and travel area can be provided quickly and effectively.

[0870] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0871] Step 1:

[0872] When a user launches the application on their device for the first time, they register an account. They enter their name, email address, and password, as well as their hobbies, travel allowance, and mode of transportation. This information is converted to JSON format on the device and sent to the server. The server validates this data and stores it in a database. The input is the user's registration information, and the output is the user setting data stored in the database.

[0873] Step 2:

[0874] The server periodically collects event information from multiple external sources, including event site APIs and RSS feeds. The server requests this information, parses the data obtained from the API or RSS, and extracts event information. The input is the data from the external sources, and the output is the parsed event information.

[0875] Step 3:

[0876] The server eliminates duplicates and organizes inconsistencies in the event information it collects. For example, if the same event is obtained from multiple sources, they are combined into a single entry. During this process, the server identifies duplicates based on key information such as the event ID, date and time, and location. The input is unorganized event information, and the output is organized event information.

[0877] Step 4:

[0878] The server uses a generative AI model to match the saved user setting data with the collected event information. As a specific example, the prompt sentence is "Search for event information related to music and food within 30 km. The user's hobbies are music and food." The server matches the results through the generative AI model and selects events that match the user's hobbies, preferences, and travel range. The input is the user setting data and event information, and the output is the event information that is most suitable for the user.

[0879] Step 5:

[0880] The proposed event information is displayed on the device via a user interface or within the app. The user flags the displayed event information as one they plan to attend and sends feedback to the server. The server receives this feedback and reflects it in the database. The input is the proposed event information, and the output is the user's feedback and the results of reflecting it.

[0881] Step 6:

[0882] When a user selects a specific event, the terminal displays detailed information. When a user wants to participate in an event, they check in through the terminal and send the check-in information to the server. The server notifies the event organizer of this check-in information and stores it in a database. The input is the user's check-in information, and the output is a notification to the organizer and storage of the result in the database.

[0883] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0884] To put this invention into practice, it is necessary to build a system that collects user preferences and travel allowance data, and then uses an emotion engine that recognizes the user's emotions to suggest appropriate event information. This system consists of a server, a terminal, and an emotion engine, and provides event information through interaction with the user.

[0885] 1. User Registration and Settings

[0886] When a user launches the app for the first time, a registration screen appears. On this screen, the user enters their name, email address, password, as well as their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device then sends this information to the server. The server validates the data and, if there are no problems, stores it in a database.

[0887] 2. Collecting and organizing event data

[0888] The server periodically collects event information from external sources (such as the event site's API or RSS feed). The collected data is organized by the server and stored in a database. This organization process includes deduplication, formatting, and correcting inconsistent data.

[0889] 3. Operation of the Emotion Engine

[0890] The emotion engine is activated when the user is using the system or when certain trigger conditions are met. The emotion engine recognizes and analyzes emotions from the user's facial expressions, voice, and text input. This emotion data is sent to the server and integrated with the user's settings data.

[0891] 4. Event Matching

[0892] The server compares the collected event information with the user's preferences and emotional data. Using generative AI, it selects events that fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, it will prioritize relaxing music events.

[0893] 5. Suggestions and Feedback

[0894] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user can then flag the event as one they plan to attend or enter emotional feedback about the displayed event information. This feedback is sent to the server, stored in a database, and reflected in future suggestions.

[0895] 6. Event Details and Check-in

[0896] When a user selects a specific event, the device displays detailed information about the event, including background and history information. When a user joins an event, they press a check-in button, and the server saves this information in a database and notifies the event organizer.

[0897] Specific examples

[0898] For example, suppose a user is interested in "music" and "movies" and has set the range they can travel by car as 30km. If the emotion engine recognizes that the user's recent emotional data indicates "high stress," the server will suggest a relaxing music festival or a soothing movie screening. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[0899] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, providing new experiences and opportunities to participate. Furthermore, by improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is possible to increase the number of participants and generate loyalty.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and also sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information in JSON format to the server.

[0903] Step 2:

[0904] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[0905] Step 3:

[0906] The server periodically collects event information from multiple external sources (event site APIs and RSS feeds), sends API requests, receives responses, and analyzes them.

[0907] Step 4:

[0908] The server organizes the collected event information, which includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, they are merged into a single entry.

[0909] Step 5:

[0910] When a user uses the system, the emotion engine periodically runs. The emotion engine recognizes and analyzes the user's current emotion from their facial expressions, voice, and text input. It generates emotion data and sends this information to the server.

[0911] Step 6:

[0912] The server retrieves and integrates the user's preference data and emotion data from the database, and updates the user's profile based on this integrated data.

[0913] Step 7:

[0914] The server compares the user's integrated data (settings and emotion data) with the collected event information to select appropriate events. Using generative AI, it selects events that best fit the user's hobbies, range of movement, and even emotional state.

[0915] Step 8:

[0916] The terminal notifies or displays the event information sent from the server to the user, and the user can input a "plan to attend" flag or feedback about the displayed event information.

[0917] Step 9:

[0918] The device sends user feedback, such as "planned attendance" information and emotional feedback, to the server, which stores this information in a database and reflects it in future event suggestions.

[0919] Step 10:

[0920] When a user selects a specific event, the device displays detailed information about that event, including the event's date, time, location, participation fee, and background and history information.

[0921] Step 11:

[0922] On the day of the event, the user presses the check-in button and sends the check-in information to the server via the terminal. The server stores the check-in information in a database and notifies the event organizer.

[0923] Step 12:

[0924] After the user returns home from the event, the emotion engine checks the user's emotional state again and sends the data to the server, which updates the user's profile based on the new emotional data and reflects it in future event suggestions.

[0925] Example 2

[0926] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0927] Conventional event suggestion systems have the problem of only considering the user's hobbies and travel range, and are unable to suggest events that reflect the user's emotional state. This makes it difficult to provide optimal event information to users, which leads to a decrease in user satisfaction. Furthermore, the lack of a means to efficiently reflect feedback makes it difficult to improve the accuracy of suggestions.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0929] In this invention, the server includes means for accepting and saving setting data on the user's hobbies, preferences, and allowable range of movement; means for collecting and organizing event information from multiple external information sources; emotion engine means for acquiring and analyzing emotion data from the user's facial expressions, voice, and text input; means for collating the saved user setting data with the collected event information, integrating the emotion data, and providing appropriate event information via a user interface; and feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information. This makes it possible to suggest optimal events based on the user's hobbies, range of movement, and even emotional state, thereby improving user satisfaction. Furthermore, reflecting feedback can improve the accuracy of suggestions.

[0930] "Setting data of user's interests and preferences and permissible range of movement" is information relating to the user's fields of interest and permissible range of movement that the user provides to the system.

[0931] "Storage means" refers to a device or program that has the function of temporarily or permanently storing input data.

[0932] An "External Source" is an institution, site, or service that provides event information or data that exists outside the System and is accessible via an API, RSS feed, or similar.

[0933] "Event information" refers to information about the location, date and time, content, and participation conditions of the event.

[0934] The "organizing means" is a device or program that has the function of de-duping acquired data, reformatting it, correcting inconsistent data, and consolidating it into a specified format.

[0935] An "emotion engine" is an algorithm or software module that analyzes emotions from a user's facial expressions, voice, text input, etc., and obtains the results.

[0936] "Matching" is the process of comparing different data sets to find matching items or conditions.

[0937] The "means for providing" is a device or program for notifying or displaying the processed information to the user.

[0938] "Feedback" refers to information about impressions and reactions that users provide to the system after using it.

[0939] The "feedback processing means" is a device or program for collecting and analyzing feedback from users and reflecting the results in the system's setting data and event information.

[0940] To put this invention into practice, a system is constructed that combines a server, a terminal, and an emotion engine. First, when a user launches the app on their terminal for the first time, a registration screen is displayed. On this screen, the user enters their name, email address, password, hobbies and interests, allowable range of travel, and mode of transportation (car, walking, etc.). The terminal sends this information to the server, which then validates the received data and stores it in a database.

[0941] The server periodically collects event information from external sources (event site APIs and RSS feeds), performs processes such as deduplication, formatting, and correcting inconsistent data, and then stores the organized data in a database. When a user is using the system or certain trigger conditions are met, the emotion engine is activated and the device captures emotion data from the user's facial expressions, voice, and text input. This emotion data is sent to the server, analyzed by the emotion engine, and integrated with the user's settings data before being stored in the database.

[0942] The server compares the collected event information with the user's saved preference data and emotional data, and uses a generative AI model to select events that best fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, the system will prioritize relaxing music events. Based on the server's suggestions, the device will notify or display the most appropriate event information to the user.

[0943] Users can input plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database. This feedback is then reflected in future event suggestions. When the user selects a specific event, the device displays detailed information about that event, including information about the event's background and history. When the user actually attends the event, they press the check-in button, and the device sends this information to the server. The server stores the check-in information in a database and notifies the event organizer.

[0944] Specific examples

[0945] For example, if a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine recognizes the user's emotion as "high stress," the server will suggest relaxing music festivals and soothing movie screenings. An example of a prompt for this would be:

[0946] If a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine identifies the user as "high stress," what events should be suggested?

[0947] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, offering new experiences and opportunities to participate. By improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is expected that the number of participants will increase and loyalty will be generated.

[0948] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] User Registration and Settings

[0951] The terminal displays a registration screen to the user. The user enters their name, email address, password, hobbies, travel allowance, and mode of transportation. The entered data is sent from the terminal to the server. The server validates the received data and, if there are no problems, stores it in a database.

[0952] Input: Personal information and settings data that you enter into your device

[0953] Data processing: Data format confirmation and validation

[0954] Output: Saved user configuration data

[0955] Step 2:

[0956] Event data collection and organization

[0957] The server periodically collects event information from APIs and RSS feeds on external event sites. The collected data is organized by the server, and duplicates are eliminated, formatted, and inconsistent data is corrected. The organized data is then stored in a database.

[0958] Input: Event information obtained from an external event site

[0959] Data processing: Removing duplicate data, formatting, correcting inconsistent data

[0960] Output: Organized event data

[0961] Step 3:

[0962] Emotion Engine Operation

[0963] While the user is using the system or when certain trigger conditions are met, the device captures the user's facial expressions, voice, and text input. This emotion data is sent to the server, where the emotion engine analyzes the data and integrates the results into a database.

[0964] Input: User's facial expression, voice, and text data obtained from the device

[0965] Data processing: Emotion data analysis using an emotion engine

[0966] Output: Parsed emotion data

[0967] Step 4:

[0968] Event Matching

[0969] The server compares the collected event information with the user's preferences and emotional data, and uses a generative AI model to select the most suitable event based on the user's hobbies, travel range, and emotional state.

[0970] Input: User preference data, emotion data, and event information

[0971] Data processing: Matching with event information using generative AI models

[0972] Output: Matched event information

[0973] Step 5:

[0974] Suggestions and Feedback

[0975] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user inputs flags for plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database and reflects it in future suggestions.

[0976] Input: Event suggestions from the server, feedback from users

[0977] Data processing: Analysis of feedback and reflection on next proposal

[0978] Output: Updated user preference data and feedback data

[0979] Step 6:

[0980] Event details and check-in

[0981] When the user selects a specific event, the device displays detailed information about the event. When the user joins the event, they press the check-in button. The device sends the check-in information to the server, which stores it in a database. The organizer is notified of the check-in information.

[0982] Input: User selected event, check-in information

[0983] Data processing: Saving check-in information and notifying the organizer

[0984] Output: Database containing check-in information and notified host information

[0985] The above are the processing steps of the program for this system.

[0986] (Application example 2)

[0987] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0988] Modern logistics centers require management methods to improve worker efficiency, but simply managing work content and schedules is not enough. It is necessary to improve work efficiency and increase worker satisfaction by taking into account the emotions and physical condition of workers and assigning them appropriate tasks. There is also a need for a mechanism to flexibly adjust the system through feedback.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0990] In this invention, the server includes means for accepting and saving setting data on user preferences and allowable movement ranges, means for collecting and organizing event information from multiple external information sources, emotion recognition means for recognizing and saving the user's emotional state from facial expressions and voice, means for collating the saved user setting data, emotion data, and collected event information and providing appropriate event information using a generative AI model, and feedback processing means for accepting feedback from the user via a user interface and reflecting it in the setting data, emotion data, and event information. This makes it possible to assign optimal tasks taking into account the emotional state of workers and flexibly adjust the system based on the feedback.

[0991] "User" refers to a worker who uses this system.

[0992] "Hobbies and interests" refers to the activities and interests that a user likes.

[0993] The "permissible range of movement" refers to the range within which the user can move.

[0994] "Setting data" refers to setting information such as the user's hobbies and preferences, allowable range of movement, and the like.

[0995] "External information source" refers to an information provider that provides external event information.

[0996] "Event information" refers to information about various events obtained from external information sources.

[0997] "Emotion recognition means" refers to means for recognizing the emotional state of a user from their facial expressions and voice.

[0998] "Emotion data" refers to information on the user's emotional state recognized by the emotion recognition means.

[0999] A "generative AI model" refers to an artificial intelligence model that suggests appropriate event information to users based on collected data.

[1000] "User interface" refers to an interface that allows a user to input and output information through an electronic device.

[1001] "Feedback" refers to information collected from users based on their usage, opinions, and experiences.

[1002] "Feedback processing means" refers to means for receiving feedback from users and reflecting it in data.

[1003] The logistics center management system of the present invention is configured as follows in order to optimize the efficiency of users (workers) and improve their work satisfaction.

[1004] User Registration and Settings

[1005] When a user first uses the smart glasses, a registration screen appears on the glasses' display. The user enters their name, ID, job title, preferred work (e.g., picking, inventory), and skills. This information is sent to the server via Wi-Fi and stored in a database.

[1006] Collecting and organizing work data

[1007] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, work environment, etc., and stored in a database. This organization process includes eliminating duplicate tasks, formatting, and correcting inconsistent data.

[1008] emotion recognition

[1009] While the user is wearing the smart glasses, facial recognition and voice analysis are performed using the built-in camera and microphone. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state (e.g., stress, fatigue, concentration). This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[1010] Task Matching

[1011] The server matches the user's preferences and emotional data with ongoing or scheduled tasks. Using a generative AI model (e.g., OpenAI, GPT-4), it selects tasks that best fit the user's skills and emotional state. For example, it suggests lighter tasks to a user who is feeling fatigued.

[1012] Suggestions and Feedback

[1013] Appropriate task suggestions from the server are displayed on the smart glasses display. The user can then accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[1014] Task details and confirmation

[1015] When a user selects a specific task, the smart glasses display detailed information about the task, including the work procedure, work location, and required tools. After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[1016] Hardware used

[1017] Smart glasses (e.g. Google Glass, Vuzix Smart Glasses)

[1018] Emotion engines (e.g., Affectiva, Amazon Rekognition)

[1019] Server (Backend: Node.js, Database: MongoDB)

[1020] User interface (front end: React, display support: AR.js)

[1021] Specific examples

[1022] For example, if Worker A wants to do "picking" and is stressed, the server will suggest lighter work such as tidying shelves. If the feedback is entered as "this suits my current mood," the server will incorporate this feedback data so that similar suggestions will be made more frequently in the future.

[1023] Prompt Sentence Examples

[1024] "Please suggest the best distribution center task for when worker A wants to pick and is under stress."

[1025] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1026] Step 1:

[1027] When a user first uses the smart glasses, they enter their name, ID, job title, preferred work, and skills into the registration screen that appears on the glasses' display. This information is then sent via Wi-Fi to the server, which validates the data and stores it in a database.

[1028] Input: User registration information (name, ID, job title, preferred work, special skills)

[1029] Output: User preference data stored in the database

[1030] Step 2:

[1031] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, and work environment, and then duplicate tasks are eliminated, formatted, and inconsistent data is corrected before being stored in the database.

[1032] Input: Task information from WMS

[1033] Output: Organized task information stored in a database

[1034] Step 3:

[1035] While the user is wearing the smart glasses, the glasses' built-in camera and microphone are used for facial recognition and voice analysis. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state. This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[1036] Input: User's facial expression data, voice data

[1037] Output: Emotion data stored on the server

[1038] Step 4:

[1039] The server matches the user's preferences and emotional data with ongoing or scheduled tasks, and uses a generative AI model (e.g., OpenAI, GPT-4) to select the task that best suits the user's skills and emotional state.

[1040] Input: User preference data, emotion data, organized task information

[1041] Output: Optimal task suggestions for the user

[1042] Step 5:

[1043] Task suggestions from the server are displayed on the smart glasses' display. The user can either accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[1044] Input: Task suggestions from the server, user feedback

[1045] Output: Feedback data stored in a database

[1046] Step 6:

[1047] When a user selects a task, the smart glasses display detailed information about the task (such as the work procedure, work location, and required tools). After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[1048] Input: User selects task, confirms completion

[1049] Output: Completed task information stored in the database, notification to administrator

[1050] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1052] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1053] [Fourth embodiment]

[1054] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1055] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1056] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1057] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1058] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1059] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1060] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1061] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1062] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1063] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1064] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1065] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1066] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1067] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's smartphone or computer (hereinafter referred to as "terminal") and processing it on a server.

[1068] 1. User Registration and Settings

[1069] When a user launches the app for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server validates the data and stores it in the database.

[1070] 2. Collecting and organizing event data

[1071] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are combined into a single entry.

[1072] 3. Event Matching

[1073] The server retrieves user preference data from the database and compares it with collected event information. This comparison uses generative AI to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they like music events and can travel up to 30km by car, the server will suggest music events that match this preference.

[1074] 4. Suggestions and Feedback

[1075] Based on the suggestions from the server, the device will notify the user of the most suitable event information or display it within the app. The user can then flag the displayed event information as one they plan to attend. This feedback is sent to the server and reflected in future suggestions.

[1076] 5. Event Details and Check-in

[1077] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user joins an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[1078] Specific examples

[1079] For example, suppose a user is interested in "music" and "movies" and has set the distance they can travel by car to 30km. The server also collects information on multiple music festivals and movie screenings from external sources. The server organizes this information and compares it with the user's settings to suggest "music festivals taking place this coming weekend" and "new movie screenings" to the user. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[1080] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[1081] The processing flow will be explained below.

[1082] Step 1:

[1083] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and then sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information to the server in JSON format.

[1084] Step 2:

[1085] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[1086] Step 3:

[1087] The server periodically collects event information from multiple external sources (such as the event site's API or RSS feed), sends API requests, receives responses, and analyzes them.

[1088] Step 4:

[1089] The server organizes the event information it collects. This includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, it merges them into a single entry.

[1090] Step 5:

[1091] The server retrieves user configuration data from a database, including information such as hobbies, preferences, travel allowance, and means of transportation.

[1092] Step 6:

[1093] The server compares the user's settings data with the collected event information. Generative AI is used to select events that best fit the user's hobbies and travel range. For example, if a user likes music events and specifies that they can travel up to 30km by car, the server will suggest music events that match this.

[1094] Step 7:

[1095] The device will notify the user of the event information suggested by the server or display it within the app, and the user can flag the events they are interested in.

[1096] Step 8:

[1097] A user can input a flag of "plan to attend" or feedback for an event that the user is interested in. For example, the user can input comments about whether or not they will attend the event, or about their expectations and interest in the event.

[1098] Step 9:

[1099] The device sends the user's feedback to the server, which stores it in a database and uses it to suggest future events.

[1100] Step 10:

[1101] When a user selects a specific event, the device displays detailed information about the event, such as the date, time, location, participation fee, and background and history information.

[1102] Step 11:

[1103] When a user actually participates in an event, they press the check-in button on their device when they arrive at the event venue. This operation officially registers their intention to participate.

[1104] Step 12:

[1105] The device sends the user's check-in information to the server, which stores it in a database and notifies the event organizer. The organizer can use this information to track the number of participants and take measures to increase loyalty.

[1106] Example 1

[1107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1108] Conventional event suggestion systems provide insufficient event information based on users' hobbies, preferences, and travel allowances, resulting in the risk of users missing events of interest. Furthermore, the lack of a system for effectively organizing collected event information and incorporating user feedback led to a problem of reduced suggestion accuracy. Furthermore, there was a lack of a means to effectively utilize check-in information from users participating in events and provide useful information to event organizers.

[1109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1110] In this invention, the server includes means for accepting and storing setting data on user preferences and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for comparing the stored user setting data with the collected event information using a generative AI model and providing appropriate event information via a user interface, and feedback processing means for accepting feedback from users and reflecting it in the setting data and event information. This makes it possible to provide optimal event information based on the user setting data, improving the accuracy of proposals and providing useful information to organizers.

[1111] "User's interests and travel allowance" is data that indicates the types of activities and events in which the user is interested, as well as the distance and means of transportation that the user is able to travel.

[1112] "Setting data" is a collection of data including a user's hobbies, preferences, and permitted range of movement, as well as other information related to an individual user.

[1113] "External Source" refers to an external data source or service that provides event information (e.g., an event site API or RSS feed).

[1114] "Event information" is data that includes detailed information about an event, such as the date, time, location, and content.

[1115] "Means for storing" refers to a program and hardware for storing received data in a storage device such as a database.

[1116] "Means for collecting and organizing" refers to programs and hardware that have the functionality to obtain event information from external sources and perform deduplication and inconsistency organization.

[1117] A "generative AI model" is a model that uses artificial intelligence technology to analyze and collate data and select appropriate event information.

[1118] "Means for matching and providing" refers to the program and hardware for comparing the user's setting data with the collected event information using a generative AI model and providing the most appropriate event information via the user interface.

[1119] "User interface" refers to the screen and input means that allow a user to interact with a system.

[1120] "Feedback processing means" refers to a program and hardware that has the function of receiving feedback from the user and reflecting it in the system's setting data and event information.

[1121] To implement this invention, it is necessary to build a system that collects user preferences and travel allowance data and then proposes appropriate event information based on this data. This system is realized by receiving data input from the user's terminal and processing it on a server.

[1122] User Registration and Settings

[1123] When a user launches the app for the first time, an account registration screen is displayed. The user enters information such as their name, email address, password, hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device sends this information in JSON format to the server. The server then validates the received data and checks for any invalid data. Data that passes validation is then stored in the database.

[1124] Event data collection and organization

[1125] The server periodically collects event information from multiple external sources (e.g., event site APIs and RSS feeds). The collected data is stored in a database after de-duplication and correction of inconsistencies. The APIs and RSS feeds used as external sources use standard communication protocols (e.g., HTTPS).

[1126] Event Matching

[1127] The server retrieves the user's preference data from the database and compares it with the collected event information. This comparison is performed using a generative AI model. The model uses prompts to select events that best fit the user's hobbies and travel range. For example, if a user specifies that they prefer music events and can travel up to 30km by car, the following prompts can be input into the generative AI model:

[1128] User settings: Music, movies, 30km car travel

[1129] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[1130] Based on this input, the generative AI model selects the most appropriate event information.

[1131] Suggestions and Feedback

[1132] The server sends the selected event information to the device. The device notifies the user of the received event information or displays it within the app. The user can flag the displayed event information as one they plan to attend. This feedback information is sent from the device to the server and stored in a database. The server uses this feedback to further optimize future suggestions.

[1133] Event details and check-in

[1134] When a user selects a specific event, the device displays detailed information about the event, including background and history information about the event. When a user attends an event, they check in through the device. This check-in information is sent to the server and stored in a database. The server then notifies the event organizer of this check-in information.

[1135] As described above, the present invention realizes a system that efficiently and effectively provides event information that is best suited to a user's hobbies and travel area. It also improves the accuracy of suggestions through feedback, providing useful information to event organizers. This allows users to easily discover new experiences, and organizers can increase the number of participants and generate loyalty.

[1136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1137] Step 1: User Registration and Setup

[1138] Input: The user launches the app on their device for the first time and enters information such as their name, email address, password, hobbies and interests, permitted range of travel, and mode of transportation (car, walking, etc.).

[1139] Processing: The terminal validates the user's input information, converts it into JSON format, and sends it to the server.

[1140] Output: User data sent from the device to the server in JSON format.

[1141] Specific operation: The user enters the required information into the application's registration form and presses the "Submit" button. The device then converts the data into JSON format and sends it to the server using a secure communication protocol (HTTPS).

[1142] Step 2: Data validation and storage

[1143] Input: The server receives the user data sent from the terminal.

[1144] Processing: The server validates the format and content of the user data and stores it in the database if there are no problems.

[1145] Output: User configuration data stored on the server.

[1146] Specific operation: The server checks the received data based on the specified validation rules, and if there are no errors, it executes an insert query in the database (e.g., MySQL, PostgreSQL) and saves it.

[1147] Step 3: Collect event data

[1148] Input: The server accesses multiple external sources (e.g., event site API, RSS feed).

[1149] Processing: The server periodically calls the API to collect event information.

[1150] Output: A dataset of collected event information on a server.

[1151] Specific operation: The server executes scheduled tasks using Cron jobs, etc., and retrieves event information from API endpoints.

[1152] Step 4: Deduplicate and reshape the data

[1153] Input: A dataset of event information collected by the server.

[1154] Processing: The server eliminates duplicates in the dataset, extracts the necessary information, and stores it in a unified format.

[1155] Output: Event data organized and stored on the server.

[1156] Specific operation: The server eliminates duplicates based on the event ID, title, date and time, etc., and stores them in the database in a normalized format.

[1157] Step 5: Retrieving User Configuration Data

[1158] Input: The server retrieves the configuration data for the specified user from the database.

[1159] Processing: The server extracts the setting data of the relevant user using a select query.

[1160] Output: User configuration data.

[1161] Specific operation: The server queries the database for user setting information based on the user ID and retrieves the setting data.

[1162] Step 6: Event Matching

[1163] Input: The server retrieves user configuration data and organized event data.

[1164] Processing: The server inputs this data into the generative AI model as prompt sentences and selects the most appropriate event information.

[1165] Output: A list of the best event information.

[1166] Specific operation: The server inputs the following prompt sentence into the generative AI model and selects the most appropriate event information.

[1167] User settings: Music, movies, 30km car travel

[1168] Possible events: ["Music festival happening this coming weekend," "New movie screening"]

[1169] Step 7: Event proposal

[1170] Input: The best event information selected by the server.

[1171] Processing: The server sends the appropriate event information to the device in JSON format.

[1172] Output: Notification of event information from the server to the terminal.

[1173] Specific operation: The server encodes the selected event information into JSON format and sends it to the terminal.

[1174] Step 8: Information Display and Feedback

[1175] Input: Event information received by the device from the server.

[1176] Processing: The device notifies the user of the event information or displays it within the app, and the user sets a flag indicating that they plan to attend.

[1177] Output: Feedback information from the user.

[1178] Specific operation: The device sends a push notification and displays the event information in the app. When the user presses the "Plan to Attend" button, feedback is generated and sent to the server.

[1179] Step 9: Incorporating feedback

[1180] Input: Feedback information sent by the user through the terminal.

[1181] Processing: The server stores the feedback information in a database and uses it as a reference for future suggestions.

[1182] Output: Feedback information stored in a database.

[1183] Specific operation: The server saves the feedback information in the database using an insert query and adds it to the training dataset of the generative AI model.

[1184] Step 10: Event Details and Check-in

[1185] Input: Specific event information selected by the user.

[1186] Processing: The terminal displays detailed information and sends check-in information to the server when the user joins.

[1187] Output: Check-in information sent to the server.

[1188] Specific operation: When the user presses the "Check-in" button on the event details screen, the device sends the check-in information to the server.

[1189] Step 11: Check-in information notification

[1190] Input: The server receives the check-in information sent by the user.

[1191] Processing: The server saves the check-in information in a database and notifies the event organizer.

[1192] Output: Notification to event organizer.

[1193] Specific operation: The server saves the check-in information in a database and calls an API to notify the organizer in real time.

[1194] (Application example 1)

[1195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1196] Conventional event suggestion systems have had problems with low suggestion accuracy and failure to meet user expectations when providing event information based on a user's hobbies, preferences, and travel allowances. They also lacked a mechanism for effectively utilizing user feedback to improve suggestion accuracy. Furthermore, event information collected from external information sources was insufficiently organized and duplicated, making it difficult to quickly provide users with appropriate information. The present invention aims to resolve these problems, provide users with more accurate event suggestions, and improve the user experience.

[1197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1198] In this invention, the server includes means for accepting and saving setting data on a user's hobbies, preferences, and allowable range of movement, means for collecting and organizing event information from multiple external information sources, means for collating the saved user setting data with the collected event information and providing appropriate event information using a generative AI model, feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information, and means for improving the accuracy of the event information with prompt sentences using the generative AI model. This makes it possible to provide more accurate event suggestions to the user and provide information quickly and effectively by organizing the event information and utilizing prompt sentences.

[1199] "Setting data of user's interests, tastes and permissible range of movement" is information entered by the user regarding the types of activities and events in which the user is interested and the range in which the user is allowed to move.

[1200] "Multiple external information sources" refers to multiple information providers, including online event sites, news feeds, and APIs.

[1201] "Event information" refers to information about an event, such as the date, location, content, and participation conditions.

[1202] A "generative AI model" is an artificial intelligence model that selects and suggests optimal events based on user data and collected event information.

[1203] "User interface" refers to the interface through which a user interacts with a system, specifically a smartphone application or a web application.

[1204] The "feedback processing means" is a function that receives opinions and evaluations from users and reflects them in the next proposal.

[1205] A "prompt sentence" is an input sentence that a generative AI model uses to make accurate event suggestions.

[1206] In order to put the present invention into practice, it is necessary to build a system that collects setting data on a user's hobbies, tastes, and permissible range of movement and proposes appropriate event information based on this data. A specific embodiment of the system will be described below.

[1207] First, the user downloads and installs the application from their device. Then, when the user launches the application for the first time, an account registration screen appears. Here, the user enters their name, email address, and password, and also sets their hobbies, travel allowance, and mode of transportation (e.g., car, walking). This information is sent in JSON format to the server, which validates the data and stores it in the database.

[1208] Next, the server periodically collects event information from multiple external sources (e.g., event websites, RSS feeds, APIs). The collected data is then deduplicated and sorted for inconsistencies before being stored in a database. For example, if the same event is obtained from multiple sources, they are merged into a single entry.

[1209] The server then uses the generative AI model to match the saved user preference data and select the most suitable event from the collected event information. The generative AI model then uses prompts to make highly accurate event suggestions. For example, the prompt might read, "Search for events related to music and food within 30km. The user's hobbies are music and food."

[1210] The suggested event information is notified to the user through the user interface or displayed within the app. The user can flag the displayed event information as one they plan to attend, and this feedback is sent to the server and reflected in future suggestions. Furthermore, if the user selects a specific event, detailed information is displayed, and when attending, participation information is sent to the server using the check-in function. This information is also notified to the event organizer.

[1211] The hardware and software that make up this system uses smartphones (Android or iOS), Python 3.x, the Requests library, etc.

[1212] In this way, event information that is most suited to the user's hobbies and travel area can be provided quickly and effectively.

[1213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1214] Step 1:

[1215] When a user launches the application on their device for the first time, they register an account. They enter their name, email address, and password, as well as their hobbies, travel allowance, and mode of transportation. This information is converted to JSON format on the device and sent to the server. The server validates this data and stores it in a database. The input is the user's registration information, and the output is the user setting data stored in the database.

[1216] Step 2:

[1217] The server periodically collects event information from multiple external sources, including event site APIs and RSS feeds. The server requests this information, parses the data obtained from the API or RSS, and extracts event information. The input is the data from the external sources, and the output is the parsed event information.

[1218] Step 3:

[1219] The server eliminates duplicates and organizes inconsistencies in the event information it collects. For example, if the same event is obtained from multiple sources, they are combined into a single entry. During this process, the server identifies duplicates based on key information such as the event ID, date and time, and location. The input is unorganized event information, and the output is organized event information.

[1220] Step 4:

[1221] The server uses a generative AI model to match the saved user setting data with the collected event information. As a specific example, the prompt sentence is "Search for event information related to music and food within 30 km. The user's hobbies are music and food." The server matches the results through the generative AI model and selects events that match the user's hobbies, preferences, and travel range. The input is the user setting data and event information, and the output is the event information that is most suitable for the user.

[1222] Step 5:

[1223] The proposed event information is displayed on the device via a user interface or within the app. The user flags the displayed event information as one they plan to attend and sends feedback to the server. The server receives this feedback and reflects it in the database. The input is the proposed event information, and the output is the user's feedback and the results of reflecting it.

[1224] Step 6:

[1225] When a user selects a specific event, the terminal displays detailed information. When a user wants to participate in an event, they check in through the terminal and send the check-in information to the server. The server notifies the event organizer of this check-in information and stores it in a database. The input is the user's check-in information, and the output is a notification to the organizer and storage of the result in the database.

[1226] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1227] To put this invention into practice, it is necessary to build a system that collects user preferences and travel allowance data, and then uses an emotion engine that recognizes the user's emotions to suggest appropriate event information. This system consists of a server, a terminal, and an emotion engine, and provides event information through interaction with the user.

[1228] 1. User Registration and Settings

[1229] When a user launches the app for the first time, a registration screen appears. On this screen, the user enters their name, email address, password, as well as their hobbies, travel allowance, and mode of transportation (car, walking, etc.). The device then sends this information to the server. The server validates the data and, if there are no problems, stores it in a database.

[1230] 2. Collecting and organizing event data

[1231] The server periodically collects event information from external sources (such as the event site's API or RSS feed). The collected data is organized by the server and stored in a database. This organization process includes deduplication, formatting, and correcting inconsistent data.

[1232] 3. Operation of the Emotion Engine

[1233] The emotion engine is activated when the user is using the system or when certain trigger conditions are met. The emotion engine recognizes and analyzes emotions from the user's facial expressions, voice, and text input. This emotion data is sent to the server and integrated with the user's settings data.

[1234] 4. Event Matching

[1235] The server compares the collected event information with the user's preferences and emotional data. Using generative AI, it selects events that fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, it will prioritize relaxing music events.

[1236] 5. Suggestions and Feedback

[1237] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user can then flag the event as one they plan to attend or enter emotional feedback about the displayed event information. This feedback is sent to the server, stored in a database, and reflected in future suggestions.

[1238] 6. Event Details and Check-in

[1239] When a user selects a specific event, the device displays detailed information about the event, including background and history information. When a user joins an event, they press a check-in button, and the server saves this information in a database and notifies the event organizer.

[1240] Specific examples

[1241] For example, suppose a user is interested in "music" and "movies" and has set the range they can travel by car as 30km. If the emotion engine recognizes that the user's recent emotional data indicates "high stress," the server will suggest a relaxing music festival or a soothing movie screening. The user sets a flag in the app indicating that they plan to attend a music festival, and checks in when they actually attend. The check-in information is sent to the server and notified to the organizer as participant information.

[1242] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, providing new experiences and opportunities to participate. Furthermore, by improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is possible to increase the number of participants and generate loyalty.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] When a user launches the app for the first time, an account registration screen appears on the user interface. The user enters their name, email address, and password, and also sets their hobbies, preferences, travel allowance, and mode of transportation. The device sends this information in JSON format to the server.

[1246] Step 2:

[1247] The server validates the user registration data received, checking that there are no inconsistencies or errors in the data, and if it is correct, saving it to the database.

[1248] Step 3:

[1249] The server periodically collects event information from multiple external sources (event site APIs and RSS feeds), sends API requests, receives responses, and analyzes them.

[1250] Step 4:

[1251] The server organizes the collected event information, which includes de-duplication, formatting, and correcting inconsistencies. For example, if the same event comes from multiple sources, they are merged into a single entry.

[1252] Step 5:

[1253] When a user uses the system, the emotion engine periodically runs. The emotion engine recognizes and analyzes the user's current emotion from their facial expressions, voice, and text input. It generates emotion data and sends this information to the server.

[1254] Step 6:

[1255] The server retrieves and integrates the user's preference data and emotion data from the database, and updates the user's profile based on this integrated data.

[1256] Step 7:

[1257] The server compares the user's integrated data (settings and emotion data) with the collected event information to select appropriate events. Using generative AI, it selects events that best fit the user's hobbies, range of movement, and even emotional state.

[1258] Step 8:

[1259] The terminal notifies or displays the event information sent from the server to the user, and the user can input a "plan to attend" flag or feedback about the displayed event information.

[1260] Step 9:

[1261] The device sends user feedback, such as "planned attendance" information and emotional feedback, to the server, which stores this information in a database and reflects it in future event suggestions.

[1262] Step 10:

[1263] When a user selects a specific event, the device displays detailed information about that event, including the event's date, time, location, participation fee, and background and history information.

[1264] Step 11:

[1265] On the day of the event, the user presses the check-in button and sends the check-in information to the server via the terminal. The server stores the check-in information in a database and notifies the event organizer.

[1266] Step 12:

[1267] After the user returns home from the event, the emotion engine checks the user's emotional state again and sends the data to the server, which updates the user's profile based on the new emotional data and reflects it in future event suggestions.

[1268] Example 2

[1269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1270] Conventional event suggestion systems have the problem of only considering the user's hobbies and travel range, and are unable to suggest events that reflect the user's emotional state. This makes it difficult to provide optimal event information to users, which leads to a decrease in user satisfaction. Furthermore, the lack of a means to efficiently reflect feedback makes it difficult to improve the accuracy of suggestions.

[1271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1272] In this invention, the server includes means for accepting and saving setting data on the user's hobbies, preferences, and allowable range of movement; means for collecting and organizing event information from multiple external information sources; emotion engine means for acquiring and analyzing emotion data from the user's facial expressions, voice, and text input; means for collating the saved user setting data with the collected event information, integrating the emotion data, and providing appropriate event information via a user interface; and feedback processing means for accepting feedback from the user and reflecting it in the setting data and event information. This makes it possible to suggest optimal events based on the user's hobbies, range of movement, and even emotional state, thereby improving user satisfaction. Furthermore, reflecting feedback can improve the accuracy of suggestions.

[1273] "Setting data of user's interests and preferences and permissible range of movement" is information relating to the user's fields of interest and permissible range of movement that the user provides to the system.

[1274] "Storage means" refers to a device or program that has the function of temporarily or permanently storing input data.

[1275] An "External Source" is an institution, site, or service that provides event information or data that exists outside the System and is accessible via an API, RSS feed, or similar.

[1276] "Event information" refers to information about the location, date and time, content, and participation conditions of the event.

[1277] The "organizing means" is a device or program that has the function of de-duping acquired data, reformatting it, correcting inconsistent data, and consolidating it into a specified format.

[1278] An "emotion engine" is an algorithm or software module that analyzes emotions from a user's facial expressions, voice, text input, etc., and obtains the results.

[1279] "Matching" is the process of comparing different data sets to find matching items or conditions.

[1280] The "means for providing" is a device or program for notifying or displaying the processed information to the user.

[1281] "Feedback" refers to information about impressions and reactions that users provide to the system after using it.

[1282] The "feedback processing means" is a device or program for collecting and analyzing feedback from users and reflecting the results in the system's setting data and event information.

[1283] To put this invention into practice, a system is constructed that combines a server, a terminal, and an emotion engine. First, when a user launches the app on their terminal for the first time, a registration screen is displayed. On this screen, the user enters their name, email address, password, hobbies and interests, allowable range of travel, and mode of transportation (car, walking, etc.). The terminal sends this information to the server, which then validates the received data and stores it in a database.

[1284] The server periodically collects event information from external sources (event site APIs and RSS feeds), performs processes such as deduplication, formatting, and correcting inconsistent data, and then stores the organized data in a database. When a user is using the system or certain trigger conditions are met, the emotion engine is activated and the device captures emotion data from the user's facial expressions, voice, and text input. This emotion data is sent to the server, analyzed by the emotion engine, and integrated with the user's settings data before being stored in the database.

[1285] The server compares the collected event information with the user's saved preference data and emotional data, and uses a generative AI model to select events that best fit the user's hobbies, travel range, and even emotional state. For example, if a user likes music and has recently been feeling stressed, the system will prioritize relaxing music events. Based on the server's suggestions, the device will notify or display the most appropriate event information to the user.

[1286] Users can input plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database. This feedback is then reflected in future event suggestions. When the user selects a specific event, the device displays detailed information about that event, including information about the event's background and history. When the user actually attends the event, they press the check-in button, and the device sends this information to the server. The server stores the check-in information in a database and notifies the event organizer.

[1287] Specific examples

[1288] For example, if a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine recognizes the user's emotion as "high stress," the server will suggest relaxing music festivals and soothing movie screenings. An example of a prompt for this would be:

[1289] If a user is interested in "music" and "movies," has a travelable range of 30km, and the emotion engine identifies the user as "high stress," what events should be suggested?

[1290] As described above, the present invention realizes a system that efficiently and effectively provides optimal event information based on a user's hobbies, travel range, and emotional state, offering new experiences and opportunities to participate. By improving the accuracy of suggestions through feedback and providing useful information to event organizers, it is expected that the number of participants will increase and loyalty will be generated.

[1291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] User Registration and Settings

[1294] The terminal displays a registration screen to the user. The user enters their name, email address, password, hobbies, travel allowance, and mode of transportation. The entered data is sent from the terminal to the server. The server validates the received data and, if there are no problems, stores it in a database.

[1295] Input: Personal information and settings data that you enter into your device

[1296] Data processing: Data format confirmation and validation

[1297] Output: Saved user configuration data

[1298] Step 2:

[1299] Event data collection and organization

[1300] The server periodically collects event information from APIs and RSS feeds on external event sites. The collected data is organized by the server, and duplicates are eliminated, formatted, and inconsistent data is corrected. The organized data is then stored in a database.

[1301] Input: Event information obtained from an external event site

[1302] Data processing: Removing duplicate data, formatting, correcting inconsistent data

[1303] Output: Organized event data

[1304] Step 3:

[1305] Emotion Engine Operation

[1306] While the user is using the system or when certain trigger conditions are met, the device captures the user's facial expressions, voice, and text input. This emotion data is sent to the server, where the emotion engine analyzes the data and integrates the results into a database.

[1307] Input: User's facial expression, voice, and text data obtained from the device

[1308] Data processing: Emotion data analysis using an emotion engine

[1309] Output: Parsed emotion data

[1310] Step 4:

[1311] Event Matching

[1312] The server compares the collected event information with the user's preferences and emotional data, and uses a generative AI model to select the most suitable event based on the user's hobbies, travel range, and emotional state.

[1313] Input: User preference data, emotion data, and event information

[1314] Data processing: Matching with event information using generative AI models

[1315] Output: Matched event information

[1316] Step 5:

[1317] Suggestions and Feedback

[1318] Based on the suggestions from the server, the device notifies or displays the most suitable event information to the user. The user inputs flags for plans to attend and emotional feedback about the displayed event information. The device sends this input information to the server, which stores it in a database and reflects it in future suggestions.

[1319] Input: Event suggestions from the server, feedback from users

[1320] Data processing: Analysis of feedback and reflection on next proposal

[1321] Output: Updated user preference data and feedback data

[1322] Step 6:

[1323] Event details and check-in

[1324] When the user selects a specific event, the device displays detailed information about the event. When the user joins the event, they press the check-in button. The device sends the check-in information to the server, which stores it in a database. The organizer is notified of the check-in information.

[1325] Input: User selected event, check-in information

[1326] Data processing: Saving check-in information and notifying the organizer

[1327] Output: Database containing check-in information and notified host information

[1328] The above are the processing steps of the program for this system.

[1329] (Application example 2)

[1330] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1331] Modern logistics centers require management methods to improve worker efficiency, but simply managing work content and schedules is not enough. It is necessary to improve work efficiency and increase worker satisfaction by taking into account the emotions and physical condition of workers and assigning them appropriate tasks. There is also a need for a mechanism to flexibly adjust the system through feedback.

[1332] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1333] In this invention, the server includes means for accepting and saving setting data on user preferences and allowable movement ranges, means for collecting and organizing event information from multiple external information sources, emotion recognition means for recognizing and saving the user's emotional state from facial expressions and voice, means for collating the saved user setting data, emotion data, and collected event information and providing appropriate event information using a generative AI model, and feedback processing means for accepting feedback from the user via a user interface and reflecting it in the setting data, emotion data, and event information. This makes it possible to assign optimal tasks taking into account the emotional state of workers and flexibly adjust the system based on the feedback.

[1334] "User" refers to a worker who uses this system.

[1335] "Hobbies and interests" refers to the activities and interests that a user likes.

[1336] The "permissible range of movement" refers to the range within which the user can move.

[1337] "Setting data" refers to setting information such as the user's hobbies and preferences, allowable range of movement, and the like.

[1338] "External information source" refers to an information provider that provides external event information.

[1339] "Event information" refers to information about various events obtained from external information sources.

[1340] "Emotion recognition means" refers to means for recognizing the emotional state of a user from their facial expressions and voice.

[1341] "Emotion data" refers to information on the user's emotional state recognized by the emotion recognition means.

[1342] A "generative AI model" refers to an artificial intelligence model that suggests appropriate event information to users based on collected data.

[1343] "User interface" refers to an interface that allows a user to input and output information through an electronic device.

[1344] "Feedback" refers to information collected from users based on their usage, opinions, and experiences.

[1345] "Feedback processing means" refers to means for receiving feedback from users and reflecting it in data.

[1346] The logistics center management system of the present invention is configured as follows in order to optimize the efficiency of users (workers) and improve their work satisfaction.

[1347] User Registration and Settings

[1348] When a user first uses the smart glasses, a registration screen appears on the glasses' display. The user enters their name, ID, job title, preferred work (e.g., picking, inventory), and skills. This information is sent to the server via Wi-Fi and stored in a database.

[1349] Collecting and organizing work data

[1350] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, work environment, etc., and stored in a database. This organization process includes eliminating duplicate tasks, formatting, and correcting inconsistent data.

[1351] emotion recognition

[1352] While the user is wearing the smart glasses, facial recognition and voice analysis are performed using the built-in camera and microphone. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state (e.g., stress, fatigue, concentration). This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[1353] Task Matching

[1354] The server matches the user's preferences and emotional data with ongoing or scheduled tasks. Using a generative AI model (e.g., OpenAI, GPT-4), it selects tasks that best fit the user's skills and emotional state. For example, it suggests lighter tasks to a user who is feeling fatigued.

[1355] Suggestions and Feedback

[1356] Appropriate task suggestions from the server are displayed on the smart glasses display. The user can then accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[1357] Task details and confirmation

[1358] When a user selects a specific task, the smart glasses display detailed information about the task, including the work procedure, work location, and required tools. After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[1359] Hardware used

[1360] Smart glasses (e.g. Google Glass, Vuzix Smart Glasses)

[1361] Emotion engines (e.g., Affectiva, Amazon Rekognition)

[1362] Server (Backend: Node.js, Database: MongoDB)

[1363] User interface (front end: React, display support: AR.js)

[1364] Specific examples

[1365] For example, if Worker A wants to do "picking" and is stressed, the server will suggest lighter work such as tidying shelves. If the feedback is entered as "this suits my current mood," the server will incorporate this feedback data so that similar suggestions will be made more frequently in the future.

[1366] Prompt Sentence Examples

[1367] "Please suggest the best distribution center task for when worker A wants to pick and is under stress."

[1368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1369] Step 1:

[1370] When a user first uses the smart glasses, they enter their name, ID, job title, preferred work, and skills into the registration screen that appears on the glasses' display. This information is then sent via Wi-Fi to the server, which validates the data and stores it in a database.

[1371] Input: User registration information (name, ID, job title, preferred work, special skills)

[1372] Output: User preference data stored in the database

[1373] Step 2:

[1374] The server periodically collects task information from the warehouse management system (WMS). The collected data is organized based on the work content, priority, and work environment, and then duplicate tasks are eliminated, formatted, and inconsistent data is corrected before being stored in the database.

[1375] Input: Task information from WMS

[1376] Output: Organized task information stored in a database

[1377] Step 3:

[1378] While the user is wearing the smart glasses, the glasses' built-in camera and microphone are used for facial recognition and voice analysis. The emotion engine analyzes facial expressions and voice tone to recognize the user's current emotional state. This emotion data is sent to the server in real time and integrated with past emotion data and task history.

[1379] Input: User's facial expression data, voice data

[1380] Output: Emotion data stored on the server

[1381] Step 4:

[1382] The server matches the user's preferences and emotional data with ongoing or scheduled tasks, and uses a generative AI model (e.g., OpenAI, GPT-4) to select the task that best suits the user's skills and emotional state.

[1383] Input: User preference data, emotion data, organized task information

[1384] Output: Optimal task suggestions for the user

[1385] Step 5:

[1386] Task suggestions from the server are displayed on the smart glasses' display. The user can either accept the suggested task or provide feedback by voice or touch. This feedback is sent to the server and reflected in future suggestions.

[1387] Input: Task suggestions from the server, user feedback

[1388] Output: Feedback data stored in a database

[1389] Step 6:

[1390] When a user selects a task, the smart glasses display detailed information about the task (such as the work procedure, work location, and required tools). After completing the task, the user presses a confirmation button, and the server saves the completed task information in the database and notifies the administrator.

[1391] Input: User selects task, confirms completion

[1392] Output: Completed task information stored in the database, notification to administrator

[1393] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1394] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1395] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1396] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1397] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1398] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1399] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1400] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1401] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1402] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1403] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1404] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1405] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1406] 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.

[1407] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1408] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1409] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1410] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1411] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1412] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1413] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1414] The following is further disclosed regarding the above embodiment.

[1415] (Claim 1)

[1416] means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement;

[1417] a means for collecting and organizing event information from multiple external sources;

[1418] means for matching the stored user setting data with the collected event information and providing appropriate event information via a user interface;

[1419] a feedback processing means for receiving feedback from a user and reflecting the feedback in the setting data and the event information;

[1420] A system including:

[1421] (Claim 2)

[1422] 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's hobbies, tastes, and travel range based on the user's setting data.

[1423] (Claim 3)

[1424] 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer.

[1425] "Example 1"

[1426] (Claim 1)

[1427] means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement;

[1428] a means for collecting and organizing event information from multiple external sources;

[1429] means for comparing the stored user setting data and the collected event information using a generative AI model and providing appropriate event information via a user interface;

[1430] a feedback processing means for receiving feedback from a user and reflecting the feedback in the setting data and the event information;

[1431] A system including:

[1432] (Claim 2)

[1433] 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's preferences and movement range based on the user's setting data using a prompt sentence.

[1434] (Claim 3)

[1435] 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer.

[1436] "Application Example 1"

[1437] (Claim 1)

[1438] means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement;

[1439] a means for collecting and organizing event information from multiple external sources;

[1440] a means for comparing the stored user setting data with the collected event information and providing appropriate event information using a generative AI model;

[1441] a feedback processing means for receiving feedback from a user and reflecting the feedback in the setting data and the event information;

[1442] A means to improve the accuracy of event information by prompting sentences using generative AI models;

[1443] A system including:

[1444] (Claim 2)

[1445] 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's hobbies, tastes, and travel range based on the user's setting data.

[1446] (Claim 3)

[1447] 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer.

[1448] "Example 2: Combining Emotion Engines"

[1449] (Claim 1)

[1450] means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement;

[1451] a means for collecting and organizing event information from multiple external sources;

[1452] emotion engine means for acquiring and analyzing emotion data from a user's facial expressions, voice, and text input;

[1453] means for collating the stored user setting data with the collected event information, integrating the emotion data, and providing appropriate event information via a user interface;

[1454] a feedback processing means for receiving feedback from a user and reflecting the feedback in the setting data and the event information;

[1455] A system including:

[1456] (Claim 2)

[1457] 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's preferences and movement range based on the user's setting data and emotion data.

[1458] (Claim 3)

[1459] 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer.

[1460] "Application example 2 when combining emotion engines"

[1461] (Claim 1)

[1462] means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement;

[1463] a means for collecting and organizing event information from multiple external sources;

[1464] An emotion recognition means for recognizing and storing an emotional state of a user from facial expressions and voice;

[1465] means for collating the stored user setting data, emotion data, and the collected event information and providing appropriate event information using a generative AI model;

[1466] a feedback processing means for receiving feedback from a user via a user interface and reflecting the feedback in the setting data, emotion data, and event information;

[1467] A system including:

[1468] (Claim 2)

[1469] 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's preferences, travel range, and emotional state based on the user's setting data and emotional data.

[1470] (Claim 3)

[1471] 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer. [Explanation of symbols]

[1472] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving and storing setting data of a user's hobbies, preferences and permitted range of movement; a means for collecting and organizing event information from multiple external sources; means for matching the stored user setting data with the collected event information and providing appropriate event information via a user interface; a feedback processing means for receiving feedback from a user and reflecting the feedback in the setting data and the event information; A system including:

2. 2. The system according to claim 1, wherein the event information is proposed as event information that matches the user's hobbies, tastes, and travel range based on the user's setting data.

3. 2. The system according to claim 1, wherein the feedback processing means receives check-in information from users and notifies the event organizer.

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