system

Abstract

The system according to this embodiment aims to recommend appropriate venues and suppliers based on event requirements and to efficiently manage schedules and tasks. [Solution] The system according to the embodiment comprises a reception unit, a recommendation unit, a management unit, and a notification unit. The reception unit receives event requirements from users. The recommendation unit recommends event venues or event suppliers based on the requirements received by the reception unit. The management unit manages the event schedule or tasks. The notification unit sends reminders for tasks managed by the management unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully carried out to recommend an appropriate venue or supplier based on the requirements of an event and efficiently manage schedules and tasks, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an appropriate venue or supplier based on the requirements of an event and efficiently manage schedules and tasks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recommendation unit, a management unit, and a notification unit. The reception unit receives event requirements from users. The recommendation unit recommends event venues or event suppliers based on the requirements received by the reception unit. The management unit manages the event schedule or tasks. The notification unit sends reminders for tasks managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend appropriate venues and suppliers based on event requirements and efficiently manage schedules and tasks. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An event planning support system according to an embodiment of the present invention is a system that receives event requirements from a user, recommends venues and suppliers based on those requirements, and manages the event schedule and tasks. The event planning support system receives event requirements from a user, recommends venues and suppliers based on those requirements, and manages the event schedule and tasks. The event planning support system also collects and analyzes participant feedback and behavioral data and makes suggestions for improving the next event. For example, the event planning support system receives event requirements from a user. For example, the user enters information such as information about the products or services to be advertised at the event, the number of participants, the number of days of the event, and the location where it will be held. This information is received by the reception department. Next, based on the requirements received by the reception department, the recommendation department recommends venues and suppliers. For example, based on information about other events held at the same time, it recommends venues so that the venues of other events are not too close together. Furthermore, the management department manages the event schedule and tasks and sends reminders. For example, it automatically schedules the event schedule and tasks and sends reminders from the notification department. Furthermore, the data collection department gathers feedback and behavioral data from event participants, and the analysis department analyzes this data. Based on the analysis results, the proposal department makes suggestions for improving the next event. For example, based on participant feedback, it makes suggestions to improve the content and operation of the next event. In this way, an AI assistant is provided to support the entire event planning process. This allows the event planning support system to recommend event venues and suppliers based on user requirements, manage event dates and tasks, and send reminders.

[0029] The event planning support system according to this embodiment comprises a reception unit, a recommendation unit, a management unit, and a notification unit. The reception unit receives event requirements from users. User requirements include, but are not limited to, information about products or services to be advertised at the event, the number of participants, the number of days of the event, and the location where it will be held. The reception unit receives requirements, for example, through an online form. The reception unit can also receive requirements by telephone or in person. For example, the reception unit stores the information entered by the user in the online form in a database. In the case of receiving requirements by telephone, the reception unit has an operator listen to the user's requirements and enter them into the system. In the case of receiving requirements in person, the reception unit digitizes the paper form filled out by the user and enters it into the system. The recommendation unit recommends event venues or suppliers based on the requirements received by the reception unit. Recommendations are made, for example, based on evaluation criteria or algorithms, but are not limited to such examples. For example, the recommendation unit recommends the most suitable venue or supplier based on past event data. The recommendation unit can also generate a list of venues or suppliers based on user requirements. For example, the Recommendation Department searches a database for venues and suppliers that meet user requirements and generates a list. The Management Department manages event dates and tasks. Management is based on, but is not limited to, detailed scheduling and task management. For example, the Management Department registers event dates in a calendar and lists tasks. The Management Department can also track task progress and send reminders. For example, the Management Department sends reminders when task deadlines are approaching. The Notifications Department sends reminders for tasks managed by the Management Department. Reminders are sent by, but are not limited to, email, SMS, or push notifications. For example, the Notifications Department sends an email reminder the day before a task deadline. The Notifications Department can also send an SMS reminder on the day of the task deadline. Furthermore, the Notifications Department can also send reminders using push notifications. For example, the Notifications Department sends push notifications through a smartphone app.As a result, the event planning support system according to the embodiment can recommend event venues and suppliers based on user requirements, manage event schedules and tasks, and send reminders.

[0030] The reception department receives event requirements from users. These requirements may include, but are not limited to, information about products or services to be advertised at the event, the number of participants, the duration of the event, and the location. The reception department may accept requirements through online forms, for example. These online forms are designed for easy user input and include guidelines and checklists to ensure all necessary information is collected. The forms feature input fields such as text boxes, dropdown menus, and checkboxes, allowing for intuitive user interaction. The reception department can also accept requirements by phone or in person. For example, the reception department can store information entered by users into a database. The database is securely managed to safely store users' personal information and event details. For phone inquiries, operators listen to the user's requirements and enter them into the system. These operators are trained to accurately understand user requirements and provide prompt and courteous service. For in-person inquiries, the reception department digitizes the user's completed paper forms and enters them into the system. This digitized information is shared with other departments, enabling efficient event planning. This allows the reception department to flexibly accept diverse user requirements and improve the overall efficiency of the system.

[0031] The recommendation department recommends event venues or suppliers based on requirements received by the reception department. Recommendations are based on, but are not limited to, evaluation criteria or algorithms. For example, the recommendation department can recommend the most suitable venues or suppliers based on past event data. This data includes participant feedback, success stories, and failure stories. By analyzing this data, the recommendation department can identify the venues or suppliers best suited to the user's requirements. The recommendation department can also generate lists of venues and suppliers based on user requirements. For example, it can search its database for venues and suppliers that match the user's requirements and generate a list. The list includes detailed information, ratings, pricing, and available services for each venue or supplier, organized for easy comparison. Furthermore, the recommendation department can use AI to analyze user requirements and provide optimal recommendations. The AI ​​analyzes user requirements using natural language processing techniques and identifies the most suitable venues or suppliers based on past data and evaluation criteria. This allows the recommendation department to quickly and accurately recommend the venues or suppliers best suited to the user's requirements.

[0032] The management team manages event dates and tasks. This management is based on, but not limited to, detailed scheduling and task management. For example, the management team registers event dates on a calendar and lists tasks. The calendar displays each event date and key milestones, allowing users to see the overall schedule at a glance. The management team can also track task progress and send reminders. For example, they send reminders when task deadlines are approaching. Reminders are sent via email, SMS, push notifications, etc., to help users remember important tasks. Furthermore, the management team can prioritize tasks and organize them according to importance and urgency. This allows users to efficiently manage tasks and smoothly prepare for events. The management team updates task progress in real time, ensuring users always stay up-to-date. The management team also provides shared calendars and task lists accessible to multiple users simultaneously, promoting collaboration across the team. This allows the management department to efficiently manage event schedules and tasks, and to support users in ensuring the smooth running of events.

[0033] The notification unit sends reminders for tasks managed by the management unit. Reminders are sent via methods such as email, SMS, and push notifications, but are not limited to these. For example, the notification unit might send an email reminder the day before a task's deadline. The email includes detailed task information, the deadline date and time, and relevant links, allowing the user to quickly access the necessary information. The notification unit can also send an SMS reminder on the day of the task's deadline. SMS messages are short and easy for the user to read immediately. Furthermore, the notification unit can send reminders using push notifications. For example, it can send push notifications through a smartphone app. Push notifications appear directly on the user's smartphone, reminding them not to forget important tasks. The notification unit can also allow users to customize the timing and frequency of reminder notifications. This allows users to set reminders according to their schedule and preferences, enabling efficient task management. Additionally, the notification unit saves a history of reminder transmissions, allowing users to review past notifications. This allows the notification unit to send reminders to the user at the appropriate time, supporting task management.

[0034] The data collection unit can collect feedback or behavioral data from event participants. For example, the data collection unit can collect feedback using questionnaires. For example, the data collection unit can send questionnaires to participants after the event and collect their responses. The data collection unit can also collect participant behavioral data during the event. For example, the data collection unit can collect participants' movement history and activity logs. Furthermore, the data collection unit can also collect participant comments. For example, the data collection unit can collect comments posted by participants during the event. This allows the data collection unit to use the collected feedback and behavioral data from event participants to improve future events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input questionnaire response data into a generating AI and have the generating AI perform feedback analysis.

[0035] The analysis unit can analyze the data collected by the collection unit. The analysis unit analyzes the data based on data analysis methods and algorithms used, for example. For example, the analysis unit can analyze collected feedback data to find areas for improvement in the event. The analysis unit can also analyze behavioral data to identify participant behavior patterns. For example, the analysis unit can analyze participants' movement history to identify popular areas. Furthermore, the analysis unit can combine and analyze feedback data and behavioral data. For example, the analysis unit can integrate feedback data and behavioral data to evaluate participant satisfaction. This allows the analysis unit to find areas for improvement in the event by analyzing the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the data analysis.

[0036] The proposal department can make suggestions for improving the next event based on the analysis results obtained by the analysis department. For example, the proposal department can make improvement suggestions based on the evaluation criteria and details of the suggestions. For example, the proposal department can make suggestions to improve the content and operation of the next event based on participant feedback. The proposal department can also make suggestions to improve the layout and arrangement of the event based on behavioral data. For example, the proposal department can make suggestions to increase the number of popular areas based on participants' movement history. Furthermore, the proposal department can make improvement suggestions by combining feedback data and behavioral data. For example, the proposal department can integrate feedback data and behavioral data to make suggestions to improve participant satisfaction. In this way, the proposal department can improve the quality of the event by making improvement suggestions for the next event based on the analysis results. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate improvement suggestions.

[0037] The management department can automatically schedule event dates and tasks. For example, the management department schedules dates and tasks based on scheduling algorithms and priority settings. For instance, the management department automatically registers event dates in a calendar and lists tasks. The management department can also track task progress and send reminders. For example, it sends reminders when task deadlines are approaching. This allows the management department to reduce the user's burden by automatically scheduling event dates and tasks. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input event date and task data into a generating AI and have the generating AI generate the schedule.

[0038] The reception desk can receive, as event requirements, at least one of the following: information about the products or services to be advertised at the event, the number of participants allowed at the event, the number of days the event will be held, and information about the region where the event will be held. The reception desk can receive these requirements, for example, through an online form. For example, the reception desk can store the information entered by the user in a database. The reception desk can also receive requirements by telephone or in person. For example, the reception desk can have an operator listen to the user's requirements and enter them into the system. This allows the reception desk to receive detailed event requirements and recommend more suitable venues and suppliers. Some or all of the above processing in the reception desk may be performed, for example, using AI, or not using AI. For example, the reception desk can input the user's entered requirement data into a generating AI and have the generating AI perform the analysis of the requirements.

[0039] The recommendation system can recommend event venues based on information about other events scheduled to be held around the same time, ensuring that the venues are not located more than a predetermined distance apart from other events. For example, the recommendation system can retrieve information about other events from a database and recommend venues that are located more than a predetermined distance apart. For example, the recommendation system can calculate the distance to the venues of other events and list venues that are located more than a predetermined distance apart. The recommendation system can also narrow down venue candidates based on user requirements. For example, the recommendation system can recommend venues located more than a predetermined distance apart within a region specified by the user. This allows the recommendation system to avoid event conflicts by ensuring that venues are not located too close together. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input information about other events into a generating AI and have the generating AI perform venue recommendations.

[0040] The reception desk can analyze the user's past event requirements and select the optimal reception method. For example, the reception desk can retrieve and analyze past event requirement data from a database. For example, the reception desk can automatically display requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requirements to be used during a specific time period based on the user's past event requirements. In this way, the reception desk can provide the optimal reception method by analyzing the user's past event requirements. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past event requirement data into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception unit can filter requirements based on the user's current projects and areas of interest when receiving them. For example, the reception unit can retrieve the user's current project information and areas of interest data from a database and perform filtering. For example, the reception unit can prioritize displaying requirements related to the user's current ongoing projects. The reception unit can also filter and display relevant requirements based on the user's areas of interest. For example, the reception unit can refer to the user's past project history and suggest relevant requirements. This allows the reception unit to prioritize receiving highly relevant requirements by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input current project information and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize receiving highly relevant requirements by considering the user's geographical location when receiving requirements. For example, the reception desk can retrieve the user's geographical location from a database and display highly relevant requirements preferentially. For example, the reception desk can prioritize receiving requirements for venues or suppliers close to the user's current location. The reception desk can also filter and display relevant requirements based on the user's geographical location. For example, the reception desk can refer to the user's past travel history and suggest relevant requirements. This enables efficient requirement reception by prioritizing highly relevant requirements based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input geographical location information into a generating AI and have the generating AI perform filtering of highly relevant requirements.

[0043] The reception unit can analyze the user's social media activity and accept relevant requirements when receiving a request. For example, the reception unit can retrieve and analyze the user's social media activity data from a database. For example, the reception unit can suggest relevant requirements based on the content of the user's social media posts. The reception unit can also refer to the user's social media activity history and prioritize accepting relevant requirements. For example, the reception unit can filter and display relevant requirements based on the user's areas of interest on social media. This allows the reception unit to accept requirements tailored to the user's interests by accepting relevant requirements based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI perform the analysis of relevant requirements.

[0044] The recommendation system can adjust the level of detail in recommendations based on the importance of venues and suppliers. For example, the recommendation system can evaluate the importance of venues and suppliers and adjust the level of detail. For example, the recommendation system can provide detailed recommendation information for important venues and suppliers. It can also provide concise recommendation information for less important venues and suppliers. For example, the recommendation system can adjust the display order of recommendation information according to the importance of venues and suppliers. In this way, the recommendation system can provide the user with the most suitable recommendation information by adjusting the level of detail in recommendations according to the importance of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input venue and supplier importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0045] The recommendation system can apply different recommendation algorithms depending on the event category during the recommendation process. For example, the recommendation system can retrieve the event category from a database and select the appropriate recommendation algorithm. For instance, for business events, the recommendation system might apply a business-oriented recommendation algorithm. Similarly, for entertainment events, it could apply an entertainment-oriented recommendation algorithm. For educational events, it might apply an educational-oriented recommendation algorithm. By applying different recommendation algorithms depending on the event category, the recommendation system can provide users with the most suitable recommendation information. Some or all of the above-described processes in the recommendation system may be performed using AI, or not. For example, the recommendation system could input event category data into a generating AI and have the generating AI select the recommendation algorithm.

[0046] The recommendation system can determine the priority of recommendations based on the availability of venues and suppliers. For example, the recommendation system can retrieve the availability of venues and suppliers from a database and determine the priority. For example, the recommendation system will prioritize recommending venues and suppliers with upcoming availability dates. It can also postpone recommending venues and suppliers with later availability dates. For example, the recommendation system can adjust the display order of recommendation information according to the availability dates. In this way, the recommendation system can provide the user with the most suitable recommendation information by determining the priority of recommendations based on the availability of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input venue and supplier availability data into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0047] The recommendation system can adjust the order of recommendations based on the relevance of venues and suppliers. For example, the recommendation system can evaluate the relevance of venues and suppliers and adjust their order. For example, the recommendation system can prioritize recommending venues and suppliers with high relevance. It can also postpone recommending venues and suppliers with low relevance. For example, the recommendation system can adjust the display order of recommendation information according to relevance. In this way, the recommendation system can provide users with the most suitable recommendation information by adjusting the order of recommendations based on the relevance of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input venue and supplier relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0048] The management department can analyze a user's past task history to select the optimal task management method during task management. For example, the management department can retrieve and analyze past task history data from a database. For example, the management department can prioritize suggesting task management methods that the user has frequently used in the past. The management department can also predict and suggest the optimal task management method based on the user's past task history. For example, the management department can analyze the user's past task history and suggest an efficient task management method. In this way, the management department can provide the optimal task management method by analyzing the user's past task history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past task history data into a generating AI and have the generating AI select the optimal task management method.

[0049] The management department can customize task priorities based on the user's current life circumstances when managing tasks. For example, the management department can retrieve user life circumstances data from a database and customize priorities. For instance, if the user is busy, the management department will prioritize important tasks. Conversely, if the user is relaxed, the management department can provide detailed task management methods. For example, the management department adjusts task priorities based on the user's current life circumstances. This enables efficient task management by allowing the management department to customize task priorities based on the user's current life circumstances. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input life circumstances data into a generating AI and have the generating AI perform the task priority customization.

[0050] The management department can select the optimal task management method when managing tasks, taking into account the user's geographical location information. For example, the management department can obtain the user's geographical location information from a database and select the optimal task management method. For example, the management department can propose the optimal task management method based on the user's current location. The management department can also prioritize the management of relevant tasks based on the user's geographical location information. For example, the management department can refer to the user's past travel history and propose the optimal task management method. This enables efficient task management by providing the optimal task management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location information into a generating AI and have the generating AI select the optimal task management method.

[0051] The management department can analyze users' social media activity and adjust task priorities during task management. For example, the management department can retrieve and analyze users' social media activity data from a database. For example, the management department can prioritize tasks based on the content of users' social media posts. The management department can also refer to users' social media activity history and prioritize tasks based on that. For example, the management department can prioritize tasks based on users' social media interests. This allows the management department to manage tasks according to users' interests by adjusting task priorities based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input social media activity data into a generating AI and have the generating AI adjust task priorities.

[0052] The notification unit can select the optimal reminder sending method by referring to the user's past notification history when sending a reminder. For example, the notification unit can retrieve and refer to past notification history data from a database. For example, the notification unit can prioritize suggesting reminder sending methods that the user has frequently used in the past. The notification unit can also predict and suggest the optimal reminder sending method based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and suggest an efficient reminder sending method. In this way, the notification unit can provide the optimal reminder sending method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal reminder sending method.

[0053] The notification unit can adjust the timing of reminder transmission based on the user's current situation. For example, the notification unit can retrieve the user's current situation data from a database and adjust the transmission timing. For example, if the user is busy, the notification unit will prioritize sending important reminders. Conversely, if the user is relaxed, the notification unit can also send detailed reminders. For example, the notification unit adjusts the timing of reminder transmission based on the user's current situation. This allows the notification unit to provide reminders at the optimal time for the user by adjusting the timing of reminder transmission based on the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input current situation data into a generating AI and have the generating AI perform the adjustment of transmission timing.

[0054] The notification unit can select the optimal sending method when sending a reminder, taking into account the user's device information. For example, the notification unit can retrieve the user's device information from a database and select the optimal sending method. For example, if the user is using a smartphone, the notification unit can send a reminder that is sized to fit the screen. It can also send a reminder optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the notification unit can send a concise and highly visible reminder. In this way, the notification unit can provide the user with the best possible reminder by offering the optimal reminder sending method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input device information into a generating AI and have the generating AI select the optimal sending method.

[0055] The notification unit can provide multilingual reminders according to the user's language settings when sending reminders. For example, the notification unit can retrieve the user's device language settings from a database and automatically set the language of the reminder. For example, the notification unit can provide a language switching function if the user uses multiple languages. The notification unit can also provide reminders in a specific language if the user selects that language. For example, the notification unit can automatically set the language of the reminder based on the user's device language settings. In this way, the notification unit can provide the user with the most suitable reminder by providing multilingual reminders according to the user's language settings. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input language setting data into a generating AI and have the generating AI perform the task of providing multilingual reminders.

[0056] The data collection unit can select the optimal data collection method by referring to the user's past feedback history when collecting feedback. For example, the data collection unit can retrieve and refer to past feedback history data from a database. For example, the data collection unit can prioritize suggesting feedback collection methods that the user has frequently used in the past. The data collection unit can also predict and suggest the optimal data collection method based on the user's past feedback history. For example, the data collection unit can analyze the user's past feedback history and suggest an efficient data collection method. In this way, the data collection unit can provide the optimal data collection method by referring to the user's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past feedback history data into a generating AI and have the generating AI select the optimal data collection method.

[0057] The data collection unit can adjust the timing of feedback collection based on the user's current situation. For example, the data collection unit can retrieve the user's current situation data from a database and adjust the collection timing. For example, if the user is busy, the data collection unit will prioritize collecting important feedback. Conversely, if the user is relaxed, the data collection unit can also collect detailed feedback. For example, the data collection unit adjusts the timing of feedback collection based on the user's current situation. This allows the data collection unit to collect feedback at the optimal time for the user by adjusting the timing of feedback collection based on the user's current situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current situation data into a generating AI and have the generating AI perform the adjustment of the collection timing.

[0058] The data collection unit can select the optimal data collection method when collecting feedback, taking into account the user's device information. For example, the data collection unit can obtain the user's device information from a database and select the optimal data collection method. For example, if the user is using a smartphone, the data collection unit can provide a feedback collection method that is adapted to the screen size. The data collection unit can also provide a feedback collection method optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the data collection unit can provide a concise and highly visible feedback collection method. In this way, the data collection unit can provide the optimal data collection method based on the user's device information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device information into a generating AI and have the generating AI select the optimal data collection method.

[0059] The data collection unit can provide multilingual feedback collection according to the user's language settings when collecting feedback. For example, the data collection unit can obtain the user's device language settings from a database and automatically set the language for feedback collection. For example, the data collection unit can provide a language switching function if the user uses multiple languages. The data collection unit can also provide feedback collection in a specific language if the user selects a particular language. For example, the data collection unit can automatically set the language for feedback collection based on the user's device language settings. In this way, the data collection unit can provide the user with the optimal feedback collection method by providing multilingual feedback collection according to the user's language settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input language setting data into a generating AI and have the generating AI perform the provision of multilingual feedback collection.

[0060] The analysis unit can select the optimal analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can retrieve and refer to past analysis data from a database. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also predict and propose an efficient analysis algorithm from past analysis data. For example, the analysis unit can analyze past analysis data and select the most effective analysis algorithm. In this way, the analysis unit can provide the optimal analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0061] The analysis unit can adjust its analysis algorithms while considering real-time data during analysis. For example, the analysis unit can acquire real-time data from a database and adjust the analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on real-time data. The analysis unit can also predict and propose efficient analysis algorithms from real-time data. For example, the analysis unit can analyze real-time data and select the most effective analysis algorithm. In this way, the analysis unit can provide the optimal analysis algorithm by considering real-time data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0062] The analysis unit can weight the analysis data based on the timing of feedback submissions during the analysis. For example, the analysis unit can retrieve the timing of feedback submissions from a database and weight the analysis data. For example, the analysis unit can prioritize analyzing data with recent feedback submission dates. Alternatively, the analysis unit can postpone analyzing data with later feedback submission dates. For example, the analysis unit can adjust the weighting of the analysis data according to the timing of feedback submissions. This allows the analysis unit to prioritize the analysis of the most recent data by weighting the analysis data based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the feedback submission date data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0063] The analysis unit can adjust the analysis algorithm while considering the user's health condition during analysis. For example, the analysis unit can obtain user health condition data from a database and adjust the analysis algorithm. For example, if the user is tired, the analysis unit can provide a simple analysis method. Alternatively, if the user is healthy, the analysis unit can provide a detailed analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's health condition. In this way, the analysis unit can provide the optimal analysis method for the user by adjusting the analysis algorithm based on the user's health condition. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health condition data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0064] The suggestion unit can provide optimal suggestions by referring to the user's past event history when proposing improvements. For example, the suggestion unit can retrieve and refer to past event history data from a database. For example, the suggestion unit can provide optimal improvement suggestions based on the history of events the user has held in the past. The suggestion unit can also predict and propose efficient improvement suggestions based on the user's past event history. For example, the suggestion unit can analyze the user's past event history and provide the most effective improvement suggestions. In this way, the suggestion unit can provide optimal improvement suggestions by referring to the user's past event history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past event history data into a generating AI and have the generating AI perform the task of providing optimal improvement suggestions.

[0065] The proposal department can adjust its proposals by considering real-time data when making improvement suggestions. For example, the proposal department can obtain real-time data from a database and adjust the proposal content. For example, the proposal department can provide the optimal improvement suggestion based on real-time data. The proposal department can also predict and propose efficient improvement suggestions from real-time data. For example, the proposal department can analyze real-time data and provide the most effective improvement suggestion. In this way, the proposal department can provide the optimal improvement suggestion by considering real-time data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input real-time data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0066] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making improvement suggestions. For example, the suggestion unit can retrieve the user's geographical location information from a database and provide optimal suggestions. For example, the suggestion unit can provide optimal improvement suggestions based on the user's current location. The suggestion unit can also prioritize providing relevant improvement suggestions based on the user's geographical location information. For example, the suggestion unit can refer to the user's past travel history and provide optimal improvement suggestions. In this way, the suggestion unit can provide the best suggestions for the user by providing optimal improvement suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0067] The suggestion unit can analyze the user's social media activity and adjust the suggestion content when making improvement proposals. For example, the suggestion unit can obtain and analyze the user's social media activity data from a database. For example, the suggestion unit can provide relevant improvement suggestions based on the content of the user's social media posts. The suggestion unit can also refer to the user's social media activity history and prioritize providing relevant improvement suggestions. For example, the suggestion unit can provide relevant improvement suggestions based on the user's areas of interest on social media. In this way, the suggestion unit can provide the most suitable improvement suggestions for the user by adjusting the suggestion content based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input social media activity data into a generating AI and have the generating AI perform the adjustment of the suggestion content.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The event planning support system can analyze a user's past event history and recommend the most suitable venues and suppliers. For example, it can recommend the best venues and suppliers for events held under similar conditions based on data from past successful events. It can also consider feedback from past events and make recommendations that reflect improvements. Furthermore, it can analyze the user's past preferences and prioritize recommending venues and suppliers that the user likes. This enables optimal recommendations that leverage the user's past experience.

[0070] The event planning support system can monitor the progress of an event based on real-time data and automatically adjust task priorities as needed. For example, if an unexpected problem occurs on the day of the event, the system can grasp the situation in real time and prioritize important tasks. It can also analyze participant behavior data in real time to enhance management of areas where congestion is expected. Furthermore, it can adjust the timing of reminder notifications according to the progress of the event, ensuring timely notifications. This is expected to contribute to the smooth running of the event.

[0071] The event planning support system can recommend the most suitable venues and suppliers by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize recommending the best venues and suppliers within that region. It can also analyze the user's travel history and recommend venues and suppliers they have visited in the past. Furthermore, it can recommend venues and suppliers with convenient transportation access based on the user's current location. This enables optimal recommendations tailored to the user's geographical conditions.

[0072] The event planning support system can analyze a user's social media activity and recommend the most suitable venues and suppliers. For example, it can prioritize recommending venues and suppliers that the user frequently mentions on social media. It can also analyze the user's areas of interest on social media and recommend relevant venues and suppliers. Furthermore, it can refer to the user's social media activity history and recommend venues and suppliers that have received positive feedback in the past. This enables optimal recommendations based on the user's social media activity.

[0073] The event planning support system can select the optimal reminder delivery method by considering the user's device information. For example, if the user is using a smartphone, it can send a reminder optimized for the screen size. If the user is using a tablet, it can send a reminder optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can send a concise and highly visible reminder. This provides the most optimal reminder delivery method based on the user's device information.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The reception desk receives event requirements from users. These requirements may include, for example, information about the products or services to be advertised at the event, the number of participants, the number of days the event will last, and the location where it will be held. The reception desk can receive requirements via online forms, telephone, or in person. For example, information entered into online forms can be stored in a database, telephone inquiries can be entered into the system by an operator, and in-person inquiries can be entered into the system by digitizing paper forms. Step 2: The recommendation department recommends event venues or suppliers based on the requirements received by the reception department. Recommendations are made based on evaluation criteria and algorithms. For example, it may recommend the most suitable venues or suppliers based on past event data and generate a list of venues and suppliers that meet the user's requirements. Step 3: The management department manages the event schedule and tasks. Management is based on detailed schedule and task management. For example, event dates are registered on a calendar, tasks are listed, and the progress of tasks is tracked. Step 4: The notification department sends reminders for tasks managed by the management department. Reminders are sent via methods such as email, SMS, and push notifications. For example, an email reminder is sent the day before the task deadline, an SMS reminder is sent on the deadline day, and a push notification is sent via a smartphone app.

[0076] (Example of form 2) An event planning support system according to an embodiment of the present invention is a system that receives event requirements from a user, recommends venues and suppliers based on those requirements, and manages the event schedule and tasks. The event planning support system receives event requirements from a user, recommends venues and suppliers based on those requirements, and manages the event schedule and tasks. The event planning support system also collects and analyzes participant feedback and behavioral data and makes suggestions for improving the next event. For example, the event planning support system receives event requirements from a user. For example, the user enters information such as information about the products or services to be advertised at the event, the number of participants, the number of days of the event, and the location where it will be held. This information is received by the reception department. Next, based on the requirements received by the reception department, the recommendation department recommends venues and suppliers. For example, based on information about other events held at the same time, it recommends venues so that the venues of other events are not too close together. Furthermore, the management department manages the event schedule and tasks and sends reminders. For example, it automatically schedules the event schedule and tasks and sends reminders from the notification department. Furthermore, the data collection department gathers feedback and behavioral data from event participants, and the analysis department analyzes this data. Based on the analysis results, the proposal department makes suggestions for improving the next event. For example, based on participant feedback, it makes suggestions to improve the content and operation of the next event. In this way, an AI assistant is provided to support the entire event planning process. This allows the event planning support system to recommend event venues and suppliers based on user requirements, manage event dates and tasks, and send reminders.

[0077] The event planning support system according to this embodiment comprises a reception unit, a recommendation unit, a management unit, and a notification unit. The reception unit receives event requirements from users. User requirements include, but are not limited to, information about products or services to be advertised at the event, the number of participants, the number of days of the event, and the location where it will be held. The reception unit receives requirements, for example, through an online form. The reception unit can also receive requirements by telephone or in person. For example, the reception unit stores the information entered by the user in the online form in a database. In the case of receiving requirements by telephone, the reception unit has an operator listen to the user's requirements and enter them into the system. In the case of receiving requirements in person, the reception unit digitizes the paper form filled out by the user and enters it into the system. The recommendation unit recommends event venues or suppliers based on the requirements received by the reception unit. Recommendations are made, for example, based on evaluation criteria or algorithms, but are not limited to such examples. For example, the recommendation unit recommends the most suitable venue or supplier based on past event data. The recommendation unit can also generate a list of venues or suppliers based on user requirements. For example, the Recommendation Department searches a database for venues and suppliers that meet user requirements and generates a list. The Management Department manages event dates and tasks. Management is based on, but is not limited to, detailed scheduling and task management. For example, the Management Department registers event dates in a calendar and lists tasks. The Management Department can also track task progress and send reminders. For example, the Management Department sends reminders when task deadlines are approaching. The Notifications Department sends reminders for tasks managed by the Management Department. Reminders are sent by, but are not limited to, email, SMS, or push notifications. For example, the Notifications Department sends an email reminder the day before a task deadline. The Notifications Department can also send an SMS reminder on the day of the task deadline. Furthermore, the Notifications Department can also send reminders using push notifications. For example, the Notifications Department sends push notifications through a smartphone app.As a result, the event planning support system according to the embodiment can recommend event venues and suppliers based on user requirements, manage event schedules and tasks, and send reminders.

[0078] The reception department receives event requirements from users. These requirements may include, but are not limited to, information about products or services to be advertised at the event, the number of participants, the duration of the event, and the location. The reception department may accept requirements through online forms, for example. These online forms are designed for easy user input and include guidelines and checklists to ensure all necessary information is collected. The forms feature input fields such as text boxes, dropdown menus, and checkboxes, allowing for intuitive user interaction. The reception department can also accept requirements by phone or in person. For example, the reception department can store information entered by users into a database. The database is securely managed to safely store users' personal information and event details. For phone inquiries, operators listen to the user's requirements and enter them into the system. These operators are trained to accurately understand user requirements and provide prompt and courteous service. For in-person inquiries, the reception department digitizes the user's completed paper forms and enters them into the system. This digitized information is shared with other departments, enabling efficient event planning. This allows the reception department to flexibly accept diverse user requirements and improve the overall efficiency of the system.

[0079] The recommendation department recommends event venues or suppliers based on requirements received by the reception department. Recommendations are based on, but are not limited to, evaluation criteria or algorithms. For example, the recommendation department can recommend the most suitable venues or suppliers based on past event data. This data includes participant feedback, success stories, and failure stories. By analyzing this data, the recommendation department can identify the venues or suppliers best suited to the user's requirements. The recommendation department can also generate lists of venues and suppliers based on user requirements. For example, it can search its database for venues and suppliers that match the user's requirements and generate a list. The list includes detailed information, ratings, pricing, and available services for each venue or supplier, organized for easy comparison. Furthermore, the recommendation department can use AI to analyze user requirements and provide optimal recommendations. The AI ​​analyzes user requirements using natural language processing techniques and identifies the most suitable venues or suppliers based on past data and evaluation criteria. This allows the recommendation department to quickly and accurately recommend the venues or suppliers best suited to the user's requirements.

[0080] The management team manages event dates and tasks. This management is based on, but not limited to, detailed scheduling and task management. For example, the management team registers event dates on a calendar and lists tasks. The calendar displays each event date and key milestones, allowing users to see the overall schedule at a glance. The management team can also track task progress and send reminders. For example, they send reminders when task deadlines are approaching. Reminders are sent via email, SMS, push notifications, etc., to help users remember important tasks. Furthermore, the management team can prioritize tasks and organize them according to importance and urgency. This allows users to efficiently manage tasks and smoothly prepare for events. The management team updates task progress in real time, ensuring users always stay up-to-date. The management team also provides shared calendars and task lists accessible to multiple users simultaneously, promoting collaboration across the team. This allows the management department to efficiently manage event schedules and tasks, and to support users in ensuring the smooth running of events.

[0081] The notification unit sends reminders for tasks managed by the management unit. Reminders are sent via methods such as email, SMS, and push notifications, but are not limited to these. For example, the notification unit might send an email reminder the day before a task's deadline. The email includes detailed task information, the deadline date and time, and relevant links, allowing the user to quickly access the necessary information. The notification unit can also send an SMS reminder on the day of the task's deadline. SMS messages are short and easy for the user to read immediately. Furthermore, the notification unit can send reminders using push notifications. For example, it can send push notifications through a smartphone app. Push notifications appear directly on the user's smartphone, reminding them not to forget important tasks. The notification unit can also allow users to customize the timing and frequency of reminder notifications. This allows users to set reminders according to their schedule and preferences, enabling efficient task management. Additionally, the notification unit saves a history of reminder transmissions, allowing users to review past notifications. This allows the notification unit to send reminders to the user at the appropriate time, supporting task management.

[0082] The data collection unit can collect feedback or behavioral data from event participants. For example, the data collection unit can collect feedback using questionnaires. For example, the data collection unit can send questionnaires to participants after the event and collect their responses. The data collection unit can also collect participant behavioral data during the event. For example, the data collection unit can collect participants' movement history and activity logs. Furthermore, the data collection unit can also collect participant comments. For example, the data collection unit can collect comments posted by participants during the event. This allows the data collection unit to use the collected feedback and behavioral data from event participants to improve future events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input questionnaire response data into a generating AI and have the generating AI perform feedback analysis.

[0083] The analysis unit can analyze the data collected by the collection unit. The analysis unit analyzes the data based on data analysis methods and algorithms used, for example. For example, the analysis unit can analyze collected feedback data to find areas for improvement in the event. The analysis unit can also analyze behavioral data to identify participant behavior patterns. For example, the analysis unit can analyze participants' movement history to identify popular areas. Furthermore, the analysis unit can combine and analyze feedback data and behavioral data. For example, the analysis unit can integrate feedback data and behavioral data to evaluate participant satisfaction. This allows the analysis unit to find areas for improvement in the event by analyzing the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the data analysis.

[0084] The proposal department can make suggestions for improving the next event based on the analysis results obtained by the analysis department. For example, the proposal department can make improvement suggestions based on the evaluation criteria and details of the suggestions. For example, the proposal department can make suggestions to improve the content and operation of the next event based on participant feedback. The proposal department can also make suggestions to improve the layout and arrangement of the event based on behavioral data. For example, the proposal department can make suggestions to increase the number of popular areas based on participants' movement history. Furthermore, the proposal department can make improvement suggestions by combining feedback data and behavioral data. For example, the proposal department can integrate feedback data and behavioral data to make suggestions to improve participant satisfaction. In this way, the proposal department can improve the quality of the event by making improvement suggestions for the next event based on the analysis results. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate improvement suggestions.

[0085] The management department can automatically schedule event dates and tasks. For example, the management department schedules dates and tasks based on scheduling algorithms and priority settings. For instance, the management department automatically registers event dates in a calendar and lists tasks. The management department can also track task progress and send reminders. For example, it sends reminders when task deadlines are approaching. This allows the management department to reduce the user's burden by automatically scheduling event dates and tasks. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input event date and task data into a generating AI and have the generating AI generate the schedule.

[0086] The reception desk can receive, as event requirements, at least one of the following: information about the products or services to be advertised at the event, the number of participants allowed at the event, the number of days the event will be held, and information about the region where the event will be held. The reception desk can receive these requirements, for example, through an online form. For example, the reception desk can store the information entered by the user in a database. The reception desk can also receive requirements by telephone or in person. For example, the reception desk can have an operator listen to the user's requirements and enter them into the system. This allows the reception desk to receive detailed event requirements and recommend more suitable venues and suppliers. Some or all of the above processing in the reception desk may be performed, for example, using AI, or not using AI. For example, the reception desk can input the user's entered requirement data into a generating AI and have the generating AI perform the analysis of the requirements.

[0087] The recommendation system can recommend event venues based on information about other events scheduled to be held around the same time, ensuring that the venues are not located more than a predetermined distance apart from other events. For example, the recommendation system can retrieve information about other events from a database and recommend venues that are located more than a predetermined distance apart. For example, the recommendation system can calculate the distance to the venues of other events and list venues that are located more than a predetermined distance apart. The recommendation system can also narrow down venue candidates based on user requirements. For example, the recommendation system can recommend venues located more than a predetermined distance apart within a region specified by the user. This allows the recommendation system to avoid event conflicts by ensuring that venues are not located too close together. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system can input information about other events into a generating AI and have the generating AI perform venue recommendations.

[0088] The reception desk can estimate the user's emotions and adjust the way requests are processed based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on changes in facial expressions. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on fluctuations in heart rate. This allows the reception desk to reduce user stress by adjusting the way requests are processed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0089] The reception desk can analyze the user's past event requirements and select the optimal reception method. For example, the reception desk can retrieve and analyze past event requirement data from a database. For example, the reception desk can automatically display requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest requirements to be used during a specific time period based on the user's past event requirements. In this way, the reception desk can provide the optimal reception method by analyzing the user's past event requirements. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past event requirement data into a generating AI and have the generating AI select the optimal reception method.

[0090] The reception unit can filter requirements based on the user's current projects and areas of interest when receiving them. For example, the reception unit can retrieve the user's current project information and areas of interest data from a database and perform filtering. For example, the reception unit can prioritize displaying requirements related to the user's current ongoing projects. The reception unit can also filter and display relevant requirements based on the user's areas of interest. For example, the reception unit can refer to the user's past project history and suggest relevant requirements. This allows the reception unit to prioritize receiving highly relevant requirements by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input current project information and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0091] The reception desk can estimate the user's emotions and determine the priority of the requests to be accepted based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on changes in facial expressions. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on fluctuations in heart rate. This allows the reception desk to prioritize important requests by determining the priority of the requests to be accepted according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0092] The reception desk can prioritize receiving highly relevant requirements by considering the user's geographical location when receiving requirements. For example, the reception desk can retrieve the user's geographical location from a database and display highly relevant requirements preferentially. For example, the reception desk can prioritize receiving requirements for venues or suppliers close to the user's current location. The reception desk can also filter and display relevant requirements based on the user's geographical location. For example, the reception desk can refer to the user's past travel history and suggest relevant requirements. This enables efficient requirement reception by prioritizing highly relevant requirements based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input geographical location information into a generating AI and have the generating AI perform filtering of highly relevant requirements.

[0093] The reception unit can analyze the user's social media activity and accept relevant requirements when receiving a request. For example, the reception unit can retrieve and analyze the user's social media activity data from a database. For example, the reception unit can suggest relevant requirements based on the content of the user's social media posts. The reception unit can also refer to the user's social media activity history and prioritize accepting relevant requirements. For example, the reception unit can filter and display relevant requirements based on the user's areas of interest on social media. This allows the reception unit to accept requirements tailored to the user's interests by accepting relevant requirements based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input social media activity data into a generating AI and have the generating AI perform the analysis of relevant requirements.

[0094] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, it can calculate an emotion score based on changes in facial expressions. The recommendation system can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the recommendation system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows the recommendation system to provide the user with optimal recommendation information by adjusting the way recommendations are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation department may be performed using AI, for example, or without AI. For example, the recommendation department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The recommendation system can adjust the level of detail in recommendations based on the importance of venues and suppliers. For example, the recommendation system can evaluate the importance of venues and suppliers and adjust the level of detail. For example, the recommendation system can provide detailed recommendation information for important venues and suppliers. It can also provide concise recommendation information for less important venues and suppliers. For example, the recommendation system can adjust the display order of recommendation information according to the importance of venues and suppliers. In this way, the recommendation system can provide the user with the most suitable recommendation information by adjusting the level of detail in recommendations according to the importance of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input venue and supplier importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in recommendations.

[0096] The recommendation system can apply different recommendation algorithms depending on the event category during the recommendation process. For example, the recommendation system can retrieve the event category from a database and select the appropriate recommendation algorithm. For instance, for business events, the recommendation system might apply a business-oriented recommendation algorithm. Similarly, for entertainment events, it could apply an entertainment-oriented recommendation algorithm. For educational events, it might apply an educational-oriented recommendation algorithm. By applying different recommendation algorithms depending on the event category, the recommendation system can provide users with the most suitable recommendation information. Some or all of the above-described processes in the recommendation system may be performed using AI, or not. For example, the recommendation system could input event category data into a generating AI and have the generating AI select the recommendation algorithm.

[0097] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, the recommendation system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For instance, it can calculate an emotion score based on changes in facial expressions. The recommendation system can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the recommendation system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate. This allows the recommendation system to provide optimal recommendation information to the user by adjusting the length of recommendations according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation department may be performed using AI, for example, or without AI. For example, the recommendation department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The recommendation system can determine the priority of recommendations based on the availability of venues and suppliers. For example, the recommendation system can retrieve the availability of venues and suppliers from a database and determine the priority. For example, the recommendation system will prioritize recommending venues and suppliers with upcoming availability dates. It can also postpone recommending venues and suppliers with later availability dates. For example, the recommendation system can adjust the display order of recommendation information according to the availability dates. In this way, the recommendation system can provide the user with the most suitable recommendation information by determining the priority of recommendations based on the availability of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input venue and supplier availability data into a generating AI and have the generating AI perform the determination of recommendation priorities.

[0099] The recommendation system can adjust the order of recommendations based on the relevance of venues and suppliers. For example, the recommendation system can evaluate the relevance of venues and suppliers and adjust their order. For example, the recommendation system can prioritize recommending venues and suppliers with high relevance. It can also postpone recommending venues and suppliers with low relevance. For example, the recommendation system can adjust the display order of recommendation information according to relevance. In this way, the recommendation system can provide users with the most suitable recommendation information by adjusting the order of recommendations based on the relevance of venues and suppliers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not using AI. For example, the recommendation system can input venue and supplier relevance data into a generating AI and have the generating AI perform the adjustment of the recommendation order.

[0100] The management unit can estimate the user's emotions and adjust the task management method based on the estimated emotions. For example, the management unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the management unit can calculate an emotion score based on changes in facial expressions. The management unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the management unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the management unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the management unit can calculate an emotion score based on fluctuations in heart rate. In this way, the management unit can provide the user with the optimal task management method by adjusting the task management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0101] The management department can analyze a user's past task history to select the optimal task management method during task management. For example, the management department can retrieve and analyze past task history data from a database. For example, the management department can prioritize suggesting task management methods that the user has frequently used in the past. The management department can also predict and suggest the optimal task management method based on the user's past task history. For example, the management department can analyze the user's past task history and suggest an efficient task management method. In this way, the management department can provide the optimal task management method by analyzing the user's past task history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past task history data into a generating AI and have the generating AI select the optimal task management method.

[0102] The management department can customize task priorities based on the user's current life circumstances when managing tasks. For example, the management department can retrieve user life circumstances data from a database and customize priorities. For instance, if the user is busy, the management department will prioritize important tasks. Conversely, if the user is relaxed, the management department can provide detailed task management methods. For example, the management department adjusts task priorities based on the user's current life circumstances. This enables efficient task management by allowing the management department to customize task priorities based on the user's current life circumstances. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input life circumstances data into a generating AI and have the generating AI perform the task priority customization.

[0103] The management department can estimate the user's emotions and determine task priorities based on those estimated emotions. For example, the management department can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the management department can calculate an emotion score based on changes in facial expressions. The management department can also record the user's voice and estimate their emotions using voice analysis technology. For example, the management department can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the management department can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the management department can calculate an emotion score based on fluctuations in heart rate. This allows the management department to prioritize important tasks by determining task priorities according to the user's emotions. Emotion estimation is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0104] The management department can select the optimal task management method when managing tasks, taking into account the user's geographical location information. For example, the management department can obtain the user's geographical location information from a database and select the optimal task management method. For example, the management department can propose the optimal task management method based on the user's current location. The management department can also prioritize the management of relevant tasks based on the user's geographical location information. For example, the management department can refer to the user's past travel history and propose the optimal task management method. This enables efficient task management by providing the optimal task management method based on the user's geographical location information. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location information into a generating AI and have the generating AI select the optimal task management method.

[0105] The management department can analyze users' social media activity and adjust task priorities during task management. For example, the management department can retrieve and analyze users' social media activity data from a database. For example, the management department can prioritize tasks based on the content of users' social media posts. The management department can also refer to users' social media activity history and prioritize tasks based on that. For example, the management department can prioritize tasks based on users' social media interests. This allows the management department to manage tasks according to users' interests by adjusting task priorities based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input social media activity data into a generating AI and have the generating AI adjust task priorities.

[0106] The notification unit can estimate the user's emotions and adjust the reminder delivery method based on the estimated emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on fluctuations in heart rate. As a result, the notification unit can provide the user with the most optimal reminder by adjusting the reminder delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0107] The notification unit can select the optimal reminder sending method by referring to the user's past notification history when sending a reminder. For example, the notification unit can retrieve and refer to past notification history data from a database. For example, the notification unit can prioritize suggesting reminder sending methods that the user has frequently used in the past. The notification unit can also predict and suggest the optimal reminder sending method based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and suggest an efficient reminder sending method. In this way, the notification unit can provide the optimal reminder sending method by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal reminder sending method.

[0108] The notification unit can adjust the timing of reminder transmission based on the user's current situation. For example, the notification unit can retrieve the user's current situation data from a database and adjust the transmission timing. For example, if the user is busy, the notification unit will prioritize sending important reminders. Conversely, if the user is relaxed, the notification unit can also send detailed reminders. For example, the notification unit adjusts the timing of reminder transmission based on the user's current situation. This allows the notification unit to provide reminders at the optimal time for the user by adjusting the timing of reminder transmission based on the user's current situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input current situation data into a generating AI and have the generating AI perform the adjustment of transmission timing.

[0109] The notification unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on changes in facial expressions. The notification unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the notification unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the notification unit can calculate an emotion score based on fluctuations in heart rate. As a result, the notification unit can prioritize important reminders by determining the priority of reminders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user image data captured by the camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0110] The notification unit can select the optimal sending method when sending a reminder, taking into account the user's device information. For example, the notification unit can retrieve the user's device information from a database and select the optimal sending method. For example, if the user is using a smartphone, the notification unit can send a reminder that is sized to fit the screen. It can also send a reminder optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the notification unit can send a concise and highly visible reminder. In this way, the notification unit can provide the user with the best possible reminder by offering the optimal reminder sending method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input device information into a generating AI and have the generating AI select the optimal sending method.

[0111] The notification unit can provide multilingual reminders according to the user's language settings when sending reminders. For example, the notification unit can retrieve the user's device language settings from a database and automatically set the language of the reminder. For example, the notification unit can provide a language switching function if the user uses multiple languages. The notification unit can also provide reminders in a specific language if the user selects that language. For example, the notification unit can automatically set the language of the reminder based on the user's device language settings. In this way, the notification unit can provide the user with the most suitable reminder by providing multilingual reminders according to the user's language settings. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input language setting data into a generating AI and have the generating AI perform the task of providing multilingual reminders.

[0112] The data collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. In this way, the data collection unit can provide the optimal feedback collection method for the user by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0113] The data collection unit can select the optimal data collection method by referring to the user's past feedback history when collecting feedback. For example, the data collection unit can retrieve and refer to past feedback history data from a database. For example, the data collection unit can prioritize suggesting feedback collection methods that the user has frequently used in the past. The data collection unit can also predict and suggest the optimal data collection method based on the user's past feedback history. For example, the data collection unit can analyze the user's past feedback history and suggest an efficient data collection method. In this way, the data collection unit can provide the optimal data collection method by referring to the user's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past feedback history data into a generating AI and have the generating AI select the optimal data collection method.

[0114] The data collection unit can adjust the timing of feedback collection based on the user's current situation. For example, the data collection unit can retrieve the user's current situation data from a database and adjust the collection timing. For example, if the user is busy, the data collection unit will prioritize collecting important feedback. Conversely, if the user is relaxed, the data collection unit can also collect detailed feedback. For example, the data collection unit adjusts the timing of feedback collection based on the user's current situation. This allows the data collection unit to collect feedback at the optimal time for the user by adjusting the timing of feedback collection based on the user's current situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input current situation data into a generating AI and have the generating AI perform the adjustment of the collection timing.

[0115] The data collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection unit to prioritize important feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0116] The data collection unit can select the optimal data collection method when collecting feedback, taking into account the user's device information. For example, the data collection unit can obtain the user's device information from a database and select the optimal data collection method. For example, if the user is using a smartphone, the data collection unit can provide a feedback collection method that is adapted to the screen size. The data collection unit can also provide a feedback collection method optimized for a larger screen if the user is using a tablet. For example, if the user is using a smartwatch, the data collection unit can provide a concise and highly visible feedback collection method. In this way, the data collection unit can provide the optimal data collection method based on the user's device information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input device information into a generating AI and have the generating AI select the optimal data collection method.

[0117] The data collection unit can provide multilingual feedback collection according to the user's language settings when collecting feedback. For example, the data collection unit can obtain the user's device language settings from a database and automatically set the language for feedback collection. For example, the data collection unit can provide a language switching function if the user uses multiple languages. The data collection unit can also provide feedback collection in a specific language if the user selects a particular language. For example, the data collection unit can automatically set the language for feedback collection based on the user's device language settings. In this way, the data collection unit can provide the user with the optimal feedback collection method by providing multilingual feedback collection according to the user's language settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input language setting data into a generating AI and have the generating AI perform the provision of multilingual feedback collection.

[0118] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. In this way, the analysis unit can provide the optimal analysis method for the user by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0119] The analysis unit can select the optimal analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can retrieve and refer to past analysis data from a database. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also predict and propose an efficient analysis algorithm from past analysis data. For example, the analysis unit can analyze past analysis data and select the most effective analysis algorithm. In this way, the analysis unit can provide the optimal analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the selection of the optimal analysis algorithm.

[0120] The analysis unit can adjust its analysis algorithms while considering real-time data during analysis. For example, the analysis unit can acquire real-time data from a database and adjust the analysis algorithm. For example, the analysis unit can select the optimal analysis algorithm based on real-time data. The analysis unit can also predict and propose efficient analysis algorithms from real-time data. For example, the analysis unit can analyze real-time data and select the most effective analysis algorithm. In this way, the analysis unit can provide the optimal analysis algorithm by considering real-time data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0121] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to prioritize important analyses by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0122] The analysis unit can weight the analysis data based on the timing of feedback submissions during the analysis. For example, the analysis unit can retrieve the timing of feedback submissions from a database and weight the analysis data. For example, the analysis unit can prioritize analyzing data with recent feedback submission dates. Alternatively, the analysis unit can postpone analyzing data with later feedback submission dates. For example, the analysis unit can adjust the weighting of the analysis data according to the timing of feedback submissions. This allows the analysis unit to prioritize the analysis of the most recent data by weighting the analysis data based on the timing of feedback submissions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the feedback submission date data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0123] The analysis unit can adjust the analysis algorithm while considering the user's health condition during analysis. For example, the analysis unit can obtain user health condition data from a database and adjust the analysis algorithm. For example, if the user is tired, the analysis unit can provide a simple analysis method. Alternatively, if the user is healthy, the analysis unit can provide a detailed analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's health condition. In this way, the analysis unit can provide the optimal analysis method for the user by adjusting the analysis algorithm based on the user's health condition. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health condition data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0124] The suggestion unit can estimate the user's emotions and adjust the method of improvement suggestions based on the estimated user emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. As a result, the suggestion unit can provide the optimal improvement suggestions for the user by adjusting the method of improvement suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0125] The suggestion unit can provide optimal suggestions by referring to the user's past event history when proposing improvements. For example, the suggestion unit can retrieve and refer to past event history data from a database. For example, the suggestion unit can provide optimal improvement suggestions based on the history of events the user has held in the past. The suggestion unit can also predict and propose efficient improvement suggestions based on the user's past event history. For example, the suggestion unit can analyze the user's past event history and provide the most effective improvement suggestions. In this way, the suggestion unit can provide optimal improvement suggestions by referring to the user's past event history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past event history data into a generating AI and have the generating AI perform the task of providing optimal improvement suggestions.

[0126] The proposal department can adjust its proposals by considering real-time data when making improvement suggestions. For example, the proposal department can obtain real-time data from a database and adjust the proposal content. For example, the proposal department can provide the optimal improvement suggestion based on real-time data. The proposal department can also predict and propose efficient improvement suggestions from real-time data. For example, the proposal department can analyze real-time data and provide the most effective improvement suggestion. In this way, the proposal department can provide the optimal improvement suggestion by considering real-time data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input real-time data into a generating AI and have the generating AI perform the adjustment of the proposal content.

[0127] The suggestion unit can estimate the user's emotions and prioritize improvement suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. This allows the suggestion unit to prioritize improvement suggestions according to the user's emotions, thereby providing important improvement suggestions preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0128] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making improvement suggestions. For example, the suggestion unit can retrieve the user's geographical location information from a database and provide optimal suggestions. For example, the suggestion unit can provide optimal improvement suggestions based on the user's current location. The suggestion unit can also prioritize providing relevant improvement suggestions based on the user's geographical location information. For example, the suggestion unit can refer to the user's past travel history and provide optimal improvement suggestions. In this way, the suggestion unit can provide the best suggestions for the user by providing optimal improvement suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical location information into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0129] The suggestion unit can analyze the user's social media activity and adjust the suggestion content when making improvement proposals. For example, the suggestion unit can obtain and analyze the user's social media activity data from a database. For example, the suggestion unit can provide relevant improvement suggestions based on the content of the user's social media posts. The suggestion unit can also refer to the user's social media activity history and prioritize providing relevant improvement suggestions. For example, the suggestion unit can provide relevant improvement suggestions based on the user's areas of interest on social media. In this way, the suggestion unit can provide the most suitable improvement suggestions for the user by adjusting the suggestion content based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input social media activity data into a generating AI and have the generating AI perform the adjustment of the suggestion content.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The event planning support system can estimate the user's emotions and automatically adjust event requirements based on those emotions. For example, if the user is stressed, the system simplifies the requirement input and requests only the minimum necessary information. Conversely, if the user is relaxed, it can prompt them to enter detailed requirements. Furthermore, depending on the user's emotions, the system can display appropriate support messages to reduce user stress. This enables flexible responses tailored to the user's emotions, leading to an improved user experience.

[0132] The event planning support system can analyze a user's past event history and recommend the most suitable venues and suppliers. For example, it can recommend the best venues and suppliers for events held under similar conditions based on data from past successful events. It can also consider feedback from past events and make recommendations that reflect improvements. Furthermore, it can analyze the user's past preferences and prioritize recommending venues and suppliers that the user likes. This enables optimal recommendations that leverage the user's past experience.

[0133] The event planning support system can monitor the progress of an event based on real-time data and automatically adjust task priorities as needed. For example, if an unexpected problem occurs on the day of the event, the system can grasp the situation in real time and prioritize important tasks. It can also analyze participant behavior data in real time to enhance management of areas where congestion is expected. Furthermore, it can adjust the timing of reminder notifications according to the progress of the event, ensuring timely notifications. This is expected to contribute to the smooth running of the event.

[0134] The event planning support system can estimate user emotions and adjust how event feedback is collected based on those estimates. For example, if users are satisfied, it can send a survey requesting detailed feedback. If users are dissatisfied, it can provide a concise feedback form to quickly collect their opinions. Furthermore, it can adjust the timing of feedback collection according to user emotions and send surveys at the appropriate time. This enables effective feedback collection that takes user emotions into consideration.

[0135] The event planning support system can recommend the most suitable venues and suppliers by taking into account the user's geographical location. For example, if the user is in a specific region, it will prioritize recommending the best venues and suppliers within that region. It can also analyze the user's travel history and recommend venues and suppliers they have visited in the past. Furthermore, it can recommend venues and suppliers with convenient transportation access based on the user's current location. This enables optimal recommendations tailored to the user's geographical conditions.

[0136] The event planning support system can estimate the user's emotions and adjust the event's progress in real time based on those estimates. For example, if the user is stressed, the system re-evaluates task priorities and prioritizes important tasks. Conversely, if the user is relaxed, it can provide support to ensure smooth task progress. Furthermore, depending on the user's emotions, the system can send appropriate reminders to reduce stress. This enables flexible event management that takes the user's emotions into consideration.

[0137] The event planning support system can analyze a user's social media activity and recommend the most suitable venues and suppliers. For example, it can prioritize recommending venues and suppliers that the user frequently mentions on social media. It can also analyze the user's areas of interest on social media and recommend relevant venues and suppliers. Furthermore, it can refer to the user's social media activity history and recommend venues and suppliers that have received positive feedback in the past. This enables optimal recommendations based on the user's social media activity.

[0138] The event planning support system can estimate the user's emotions and adjust the task management method for the event based on those emotions. For example, if the user is feeling stressed, the system can re-evaluate task priorities and prioritize important tasks. Conversely, if the user is relaxed, it can provide support to ensure smooth task progress. Furthermore, depending on the user's emotions, the system can send appropriate reminders to reduce user stress. This enables flexible task management that takes the user's emotions into consideration.

[0139] The event planning support system can select the optimal reminder delivery method by considering the user's device information. For example, if the user is using a smartphone, it can send a reminder optimized for the screen size. If the user is using a tablet, it can send a reminder optimized for the larger screen. Furthermore, if the user is using a smartwatch, it can send a concise and highly visible reminder. This provides the most optimal reminder delivery method based on the user's device information.

[0140] The event planning support system can estimate user emotions and adjust how event feedback is collected based on those estimates. For example, if users are satisfied, it can send a survey requesting detailed feedback. If users are dissatisfied, it can provide a concise feedback form to quickly collect their opinions. Furthermore, it can adjust the timing of feedback collection according to user emotions and send surveys at the appropriate time. This enables effective feedback collection that takes user emotions into consideration.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The reception desk receives event requirements from users. These requirements may include, for example, information about the products or services to be advertised at the event, the number of participants, the number of days the event will last, and the location where it will be held. The reception desk can receive requirements via online forms, telephone, or in person. For example, information entered into online forms can be stored in a database, telephone inquiries can be entered into the system by an operator, and in-person inquiries can be entered into the system by digitizing paper forms. Step 2: The recommendation department recommends event venues or suppliers based on the requirements received by the reception department. Recommendations are made based on evaluation criteria and algorithms. For example, it may recommend the most suitable venues or suppliers based on past event data and generate a list of venues and suppliers that meet the user's requirements. Step 3: The management department manages the event schedule and tasks. Management is based on detailed schedule and task management. For example, event dates are registered on a calendar, tasks are listed, and the progress of tasks is tracked. Step 4: The notification department sends reminders for tasks managed by the management department. Reminders are sent via methods such as email, SMS, and push notifications. For example, an email reminder is sent the day before the task deadline, an SMS reminder is sent on the deadline day, and a push notification is sent via a smartphone app.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0145] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the reception unit receives user requirements via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing unit 12. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and recommends the most suitable venue and supplier based on past event data. The management unit is implemented, for example, by the control unit 46A of the smart device 14, and manages the event schedule and tasks and sends reminders. The notification unit sends reminders, for example, via the output device 40 of the smart device 14 or the communication I / F 26 of the data processing unit 12. The collection unit collects participant feedback and behavioral data using, for example, the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit makes suggestions for improving the next event based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the reception unit receives user requirements via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12. The recommendation unit, implemented by, for example, the specific processing unit 290 of the data processing unit 12, recommends the most suitable venue and supplier based on past event data. The management unit, implemented by, for example, the control unit 46A of the smart glasses 214, manages the event schedule and tasks and sends reminders. The notification unit sends reminders via, for example, the speaker 240 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12. The collection unit collects participant feedback and behavioral data using, for example, the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit makes suggestions for improving the next event based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] For example, the reception unit receives user requests via the microphone 238 of the headset terminal 314 or the communication I / F 26 of the data processing unit 12. The recommendation unit, implemented by, for example, the specific processing unit 290 of the data processing unit 12, recommends the most suitable venue and supplier based on past event data. The management unit, implemented by, for example, the control unit 46A of the headset terminal 314, manages the event schedule and tasks and sends reminders. The notification unit sends reminders via, for example, the display 343 of the headset terminal 314 or the communication I / F 26 of the data processing unit 12. The collection unit collects participant feedback and behavioral data using, for example, the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit makes suggestions for improving the next event based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0183] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0185] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0186] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0187] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0188] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0189] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0190] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0192] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0194] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0195] For example, the reception unit receives user requirements via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing unit 12. The recommendation unit, implemented by, for example, the specific processing unit 290 of the data processing unit 12, recommends the most suitable venue and supplier based on past event data. The management unit, implemented by, for example, the control unit 46A of the robot 414, manages the event schedule and tasks and sends reminders. The notification unit sends reminders via, for example, the speaker 240 of the robot 414 or the communication I / F 26 of the data processing unit 12. The collection unit collects participant feedback and behavioral data using, for example, the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data by, for example, the specific processing unit 290 of the data processing unit 12. The proposal unit makes suggestions for improving the next event based on the analysis results by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0196] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0198] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0199] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0200] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0204] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0205] 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.

[0206] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0207] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0208] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0209] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0211] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0212] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0213] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0214] (Note 1) A reception desk that receives event requirements from users, Based on the requirements received by the reception department, a recommendation department recommends the venue for the event or the supplier for the event. A management department that manages the schedule of the aforementioned event or the aforementioned tasks, The system comprises a notification unit that sends reminders for tasks managed by the aforementioned management unit. A system characterized by the following features. (Note 2) The system includes a collection unit for collecting feedback or behavioral data from event participants. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The system includes a proposal unit that makes suggestions for improving the next event based on the analysis results obtained from the aforementioned analysis unit. The system described in Appendix 3, characterized by the features described herein. (Note 5) The aforementioned management department, Automatically schedule event dates and tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The event requirements include accepting information about the products or services to be advertised at the event, the number of participants, the number of days the event will last, and information about the region where the event will be held. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recommendation department, Based on information about other events scheduled to be held around the same time, the venue for the aforementioned event will be recommended to ensure that it is not located more than a certain distance away from other events. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how requirements are accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past event requirements and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving requirements, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requirements to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving requirements, the system prioritizes accepting highly relevant requirements by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving requirements, the system analyzes the user's social media activity and accepts relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the venue and suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recommendation department, When making recommendations, we prioritize them based on the availability of venues and suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, When making recommendations, we adjust the order of recommendations based on the relevance of the venue and supplier. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, It estimates the user's emotions and adjusts the task management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing tasks, the system analyzes the user's past task history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, When managing tasks, customize task priorities based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates the user's emotions and determines task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing tasks, the optimal task management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, When managing tasks, analyze users' social media activity to adjust task priorities. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts how reminders are sent based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a reminder, the system will refer to the user's past notification history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending reminders, the timing of sending will be adjusted based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending reminders, the system selects the optimal sending method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending reminders, provide multilingual reminders according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned collection unit is When collecting feedback, adjust the timing of collection based on the user's current situation. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned collection unit is When collecting feedback, the optimal collection method is selected considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned collection unit is When collecting feedback, provide multilingual feedback collection based on the user's language settings. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit is During analysis, the optimal analysis algorithm is selected by referring to past analysis data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned analysis unit is During analysis, the analysis algorithm is adjusted to take real-time data into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned analysis unit is During the analysis, the analysis data is weighted based on when feedback was submitted. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned analysis unit is During analysis, the analysis algorithm is adjusted to take the user's health status into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned proposal section is, It estimates the user's emotions and adjusts the method of suggesting improvements based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned proposal section is, When suggesting improvements, we refer to the user's past event history to provide the most suitable suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned proposal section is, When making improvement suggestions, adjust the content of the suggestions while taking real-time data into consideration. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned proposal section is, It estimates user emotions and prioritizes improvement suggestions based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned proposal section is, When providing improvement suggestions, we take the user's geographical location into consideration to offer the most suitable suggestions. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned proposal section is, When making improvement suggestions, we analyze users' social media activity and adjust the suggestions accordingly. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives event requirements from users, Based on the requirements received by the reception department, a recommendation department recommends the venue for the event or the supplier for the event. The management department manages the schedule or tasks for the aforementioned event, The system comprises a notification unit that sends reminders for tasks managed by the aforementioned management unit. A system characterized by the following features.

2. The system includes a collection unit for collecting feedback or behavioral data from participants of the aforementioned event. The system according to feature 1.

3. The system includes an analysis unit that analyzes the data collected by the aforementioned collection unit. The system according to feature 2.

4. The system includes a proposal unit that makes suggestions for improving the next event based on the analysis results obtained from the aforementioned analysis unit. The system according to claim 3.

5. The aforementioned management department, Automatically schedule the dates and tasks for the aforementioned events. The system according to feature 1.

6. The aforementioned reception unit is The requirements for the aforementioned event include accepting at least one of the following: information about the goods or services to be advertised at the event, the number of participants allowed to take part in the event, the number of days the event will be held, and information about the region where the event will be held. The system according to feature 1.

7. The aforementioned recommendation department, Based on information about other events scheduled to be held around the same time, the venue for the aforementioned event will be recommended to ensure that it is not located more than a certain distance away from other events. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how requirements are accepted based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past event requirements and select the optimal reception method. The system according to feature 1.

10. The aforementioned reception unit is When receiving requirements, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

Images