system

Abstract

The system according to this embodiment aims to efficiently carry out preparations for events and parties. [Solution] The system according to the embodiment comprises a reception unit, a plan creation unit, a choice presentation unit, a booking unit, and a guidance unit. The reception unit specifies the budget and purpose. The plan creation unit creates an optimal plan based on the budget and purpose specified by the reception unit. The choice presentation unit presents choices of gifts and prizes based on the plan created by the plan creation unit. The booking unit makes reservations and purchases based on the choices presented by the choice presentation unit. The guidance unit provides guidance to participants based on the information of reservations and purchases completed by the booking unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes a lot of labor and time to prepare for events or parties, and it is difficult to proceed efficiently.

[0005] The system according to the embodiment aims to efficiently proceed with the preparation for events or parties.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a plan creation unit, a choice presentation unit, a booking unit, and a guidance unit. The reception unit specifies the budget and objectives. The plan creation unit creates an optimal plan based on the budget and objectives specified by the reception unit. The choice presentation unit presents choices of gifts and prizes based on the plan created by the plan creation unit. The booking unit makes reservations and purchases based on the choices presented by the choice presentation unit. The guidance unit provides guidance to participants based on the information of reservations and purchases completed by the booking unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently carry out preparations for events and parties. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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) The event preparation system according to an embodiment of the present invention is a system that uses an AI agent to make reservations, purchases, and arrangements for events and parties such as birthday parties, year-end parties, after-parties, and dinners all at once. This event preparation system creates an optimal plan and arranges venues, restaurants, gifts, prizes, transportation, games, etc., simply by the user specifying their budget and purpose. For example, when preparing for a birthday party, the user inputs their budget and purpose, and the AI ​​agent suggests the most suitable venue and restaurant, and presents options for gifts and prizes. Based on the user's selection, the AI ​​agent makes reservations and purchases, and also completes the notification to participants and prospective participants all at once. Furthermore, even if restaurants only accept telephone reservations, telephone reservations can be made using AI-generated voice generation. This allows for arrangements to be made without compromising quality or being limited to online reservations. For example, the AI ​​agent can handle tedious preparations such as arranging a venue for a last-minute after-party, preparing gifts and prizes, and arranging games all at once. This mechanism significantly reduces the effort required for users to prepare for events and parties. For example, an AI agent can handle all the tedious preparations, such as arranging a venue for a last-minute after-party, preparing gifts and prizes, and arranging games. Furthermore, by leveraging specific economic spheres to complete the arrangements, preparations can proceed efficiently. As a result, event preparation systems can significantly reduce the effort users put into preparing for events and parties.

[0029] The event preparation system according to this embodiment comprises a reception unit, a plan creation unit, a choice presentation unit, a booking unit, and a guidance unit. The reception unit allows the user to specify the budget and purpose. For example, the user can input the type of event and the range of the budget. The reception unit identifies the purpose and budget of the event based on the information entered by the user. The plan creation unit creates an optimal plan based on the budget and purpose specified by the reception unit. For example, the plan creation unit selects the best venue and restaurant within the user's budget. The plan creation unit can automatically generate a plan that suits the user's wishes using AI, for example. The choice presentation unit presents options for gifts and prizes based on the plan created by the plan creation unit. For example, the choice presentation unit presents multiple gift and prize options according to the user's budget and purpose. The choice presentation unit can suggest gifts and prizes that suit the user's preferences using AI, for example. The booking unit makes reservations and purchases based on the options presented by the choice presentation unit. For example, the booking unit purchases the gifts and prizes selected by the user online. The arrangement unit can, for example, use AI to automatically make reservations and purchases based on user selections. The guidance unit provides information to participants based on the information completed by the arrangement unit for reservations and purchases. For example, the guidance unit sends information to participants via email or message. The guidance unit can, for example, use AI to automatically provide information to participants. As a result, the event preparation system according to this embodiment allows users to specify their budget and objectives, and the system will complete the creation of the optimal plan, the presentation of gift and prize options, reservations and purchases, and participant guidance all in one go.

[0030] The reception desk allows users to specify their budget and purpose. For example, users can input the type of event and the budget range. Based on the information entered by the user, the reception desk identifies the purpose and budget of the event. Specifically, the information entered by the user includes the type of event (e.g., wedding, birthday party, corporate meeting), the number of participants, the desired date and time, location, and special requests (e.g., a specific theme or decoration, specific food or drink provision). The reception desk collects this information and stores it in a database. Furthermore, the reception desk can use AI to analyze the information entered by the user and identify the purpose and budget of the event. For example, natural language processing technology can be used to analyze the text information entered by the user and automatically extract the purpose and budget of the event. This allows the reception desk to make it easy for users to enter information and to proceed with event preparation smoothly. The reception desk also plays a role in providing necessary data to the planning and option presentation departments based on the information entered by the user. This allows the entire system to work together efficiently. In addition, the reception desk can update the information entered by the user in real time and make corrections or additions as needed. For example, if a user changes the budget or adds participants, the reception department can immediately reflect these changes and notify other departments. This allows the reception department to respond flexibly to user needs and support optimal event preparation.

[0031] The planning department creates the optimal plan based on the budget and objectives specified by the reception department. For example, the planning department selects the best venue and restaurant within the user's budget. The planning department can also use AI to automatically generate a plan that meets the user's wishes. Specifically, the planning department searches a database for suitable venue and restaurant candidates based on the budget and objectives specified by the user and proposes the optimal plan. The AI ​​can utilize past data and statistical information to generate a plan that is closest to the user's wishes. For example, it can select the best venue and restaurant based on feedback and evaluations from users who have held similar events in the past. The AI ​​can also customize the plan, taking into account the user's preferences and special requests. For example, it can propose a plan that meets the user's wishes, such as decorations tailored to a specific theme or the provision of specific food and drinks. Furthermore, the planning department can create a detailed schedule and budget estimate based on the plan selected by the user. This allows the user to grasp all the information necessary for event preparation in one place and proceed with the event smoothly. The planning department also provides an interface that allows the user to review, modify, and add to the plan. This allows users to check the plan details in real time and make changes as needed. For example, it enables flexible responses to user requests, such as changing the venue or arranging additional services. As a result, the plan creation department can provide the plan that best suits the user's needs and support the success of the event.

[0032] The option presentation unit presents gift and prize options based on the plan created by the plan creation unit. For example, the option presentation unit presents multiple gift and prize options according to the user's budget and purpose. The option presentation unit can, for example, use AI to suggest gifts and prizes that match the user's preferences. Specifically, the option presentation unit searches the database for suitable gift and prize candidates based on the budget and purpose specified by the user and proposes the best option. The AI ​​can use past data and statistical information to suggest gifts and prizes that are closest to the user's preferences. For example, it can present the user with the best option based on gifts and prizes that were popular at similar events in the past. The AI ​​can also customize the options, taking into account the user's preferences and special requests. For example, it can suggest options that match the user's wishes, such as gifts that fit a specific theme or prizes from a specific brand. Furthermore, the option presentation unit can provide detailed information and images based on the gifts and prizes selected by the user. This allows the user to see the contents of the gifts and prizes at a glance and use it as a reference for selection. Furthermore, the selection display unit provides an interface for users to compare the gifts and prizes they have selected. This allows users to compare multiple options and select the most suitable gift or prize. For example, users can compare options based on points they value, such as price, quality, and design. In this way, the selection display unit can suggest the most suitable gift or prize to the user and support the success of the event.

[0033] The arrangement department makes reservations and purchases based on the options presented by the option presentation department. For example, the arrangement department purchases gifts or prizes selected by the user online. The arrangement department can use AI to automatically make reservations and purchases based on the user's selections. Specifically, the arrangement department checks the inventory status of gifts or prizes selected by the user and makes reservations or purchases at the optimal time. The AI ​​works in conjunction with the inventory management system to grasp inventory status in real time and secure the necessary gifts or prizes. The arrangement department also handles the delivery arrangements for gifts and prizes selected by the user. For example, it handles the procedures for delivering gifts or prizes to a specified location at a specified date and time. Through cooperation with delivery companies, the AI ​​calculates the optimal delivery route and schedule, enabling the gifts and prizes to be delivered quickly and reliably. Furthermore, the arrangement department also makes reservations for venues and restaurants selected by the user. For example, it checks the availability of venues and secures reservations for the date and time desired by the user. The AI ​​works in conjunction with the venue reservation system to grasp availability in real time and secure the most suitable venue. Similarly, the system also handles restaurant reservations and arrangements, ensuring that the user receives the desired menu and services. This allows the arrangement department to quickly and reliably make reservations and purchases based on the user's selection, enabling smooth event preparations. Furthermore, the arrangement department manages the reservation and purchase status in real time and reports the progress to the user. This allows the user to always be aware of the event preparation status and proceed with the event with peace of mind.

[0034] The information department provides guidance to participants based on information completed by the booking department for reservations and purchases. For example, the information department sends guidance to participants via email or message. The information department can also automate the process of providing guidance to participants using AI. Specifically, the information department creates detailed guidance for participants based on the reservation and purchase information provided by the booking department. The AI ​​manages participant lists and contact information and can send guidance at the optimal time. For example, it creates guidance that includes detailed information such as the date and time of the event, the location, the meeting place and time for participants, and notes on what to bring and dress code, and sends it to participants. The information department also provides an interface to handle inquiries from participants. For example, it can provide a chatbot that participants can use to ask questions or clarify any uncertainties regarding the guidance, and the AI ​​can automatically answer them. This allows participants to obtain information quickly and accurately, and to participate in the event with peace of mind. Furthermore, the information department also provides guidance and support on the day of the event. For example, it can station staff to handle registration and guidance when participants arrive at the venue, supporting the smooth running of the event. The AI ​​can monitor participant arrival status in real time and issue instructions to staff as needed. This allows the event planning department to provide participants with quick and accurate information, supporting the success of the event. Furthermore, the planning department also follows up after the event, for example, by sending thank-you messages to participants and collecting feedback. AI can analyze participant feedback and identify areas for improvement in future events. This allows the planning department to improve the overall quality of the event and support preparations for the next event.

[0035] The booking unit includes a voice generation unit that uses AI-generated voice to make reservations when only telephone reservations are accepted. For example, if a restaurant only accepts telephone reservations, the booking unit can use AI-generated voice to make the reservation. For example, the booking unit can use the AI-generated voice to call the restaurant and make the reservation. For example, the booking unit can use the AI-generated voice to convey the reservation details. This makes it possible to make reservations using AI-generated voice even when only telephone reservations are accepted. Voice generation is performed using, for example, text-to-speech technology. The AI ​​generates natural-sounding voice based on the reservation information entered by the user. The AI ​​can adjust the tone and speed of the voice to accurately convey the reservation details. This makes it possible for the booking unit to make reservations using AI-generated voice even when only telephone reservations are accepted.

[0036] The planning department handles last-minute venue arrangements for after-parties. For example, to quickly arrange a venue for a last-minute after-party, the planning department uses AI to select the optimal venue. For instance, if a user requests a last-minute after-party venue, the AI ​​can suggest the most suitable venue. The planning department, for example, uses AI to check venue availability and prices in real time and select the best venue. This allows for quick arrangements for last-minute after-parties. For last-minute after-party venue arrangements, for example, the AI ​​selects the best venue based on the user's preferences. The AI, for example, checks venue availability and prices in real time and suggests the best venue. The AI ​​can, for example, present multiple venue options according to the user's budget and purpose. This allows the planning department to quickly arrange venues for last-minute after-parties.

[0037] The selection display unit presents options for gifts and prizes. For example, the selection display unit presents multiple gift and prize options based on the user's budget and purpose. The selection display unit can, for example, use AI to suggest gifts and prizes that suit the user's preferences. For example, the selection display unit considers the type and price range of gifts and prizes to present the optimal option. This allows the user to select from a selection of gifts and prizes. The presentation of gift and prize options can, for example, involve AI suggesting the optimal option based on the user's preferences. For example, the AI ​​considers the type and price range of gifts and prizes to present the optimal option. For example, the AI ​​can analyze the user's past selection history to suggest gifts and prizes that suit the user's preferences. This allows the selection display unit to present gift and prize options for the user to select from.

[0038] The information department provides information to participants and prospective participants. For example, the information department sends information to participants via email or message. The information department can also use AI to automate the process of providing information to participants. For example, the information department can select the most appropriate method of notification based on participants' contact information. This allows for the information department to send information to participants and prospective participants in bulk. For example, the AI ​​selects the most appropriate method of notification based on participants' contact information. The AI ​​sends information via email or message. The AI ​​can also send information at the optimal time, taking into account participants' schedules. This allows the information department to send information to participants and prospective participants in bulk.

[0039] The arrangement department completes arrangements by leveraging a specific economic zone. For example, the arrangement department efficiently carries out arrangements by leveraging a specific economic zone. For example, the arrangement department can use AI to select the optimal arrangement method within a specific economic zone. For example, the arrangement department completes arrangements by utilizing businesses and services within a specific economic zone. This allows for efficient arrangement completion by leveraging a specific economic zone. Arrangements utilizing a specific economic zone involve, for example, AI selecting the optimal arrangement method within that economic zone. For example, AI can complete arrangements by utilizing businesses and services within a specific economic zone. For example, AI can propose the optimal arrangement method to maximize the efficiency of arrangements within a specific economic zone. This allows the arrangement department to efficiently complete arrangements by leveraging a specific economic zone.

[0040] The reception desk analyzes the user's past event history and proposes the optimal budget and objectives. For example, the reception desk makes optimal suggestions based on the budget and objectives of events the user has held in the past. For example, the reception desk extracts specific patterns from the user's past event history and proposes budgets and objectives. For example, the reception desk analyzes the user's past event history and proposes trends in budgets and objectives. This allows the reception desk to propose the optimal budget and objectives based on the user's past event history. Analysis of past event history can be done by, for example, AI analyzing the user's past event data and making optimal suggestions. For example, the AI ​​considers the type of past event, the number of participants, and the evaluation to propose the optimal budget and objectives. For example, the AI ​​can analyze and propose trends in budgets and objectives based on the user's past event history. This allows the reception desk to propose the optimal budget and objectives based on the user's past event history.

[0041] The reception desk customizes input fields based on the user's current situation and areas of interest during registration. For example, when the user enters their current situation, the reception desk automatically displays relevant input fields based on their areas of interest. For example, the reception desk customizes input fields based on the user's current situation and efficiently collects necessary information. For example, the reception desk customizes input fields based on the user's areas of interest and makes optimal suggestions. This allows the reception desk to customize input fields according to the user's current situation and areas of interest. Customizing input fields involves, for example, AI analyzing the user's current situation and areas of interest and suggesting optimal input fields. For example, the AI ​​customizes input fields based on the user's behavioral history and interest data. For example, the AI ​​automatically displays input fields based on the user's current situation and areas of interest and efficiently collects information. This allows the reception desk to customize input fields based on the user's current situation and areas of interest.

[0042] The reception desk, upon receiving a registration, considers the user's geographical location to provide highly relevant suggestions. For example, the reception desk may suggest nearby venues or restaurants based on the user's current location. For example, the reception desk may suggest the most suitable mode of transportation based on the user's geographical location. For example, the reception desk may suggest relevant gifts or prizes based on the user's geographical location. This allows the reception desk to provide highly relevant suggestions based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location to provide optimal suggestions. The AI ​​may use, for example, GPS data or location services to identify the user's current location. The AI ​​can, for example, suggest highly relevant venues, restaurants, gifts, or prizes based on the user's geographical location. This allows the reception desk to provide highly relevant suggestions based on the user's geographical location.

[0043] The reception desk analyzes the user's social media activity at the time of registration and makes relevant suggestions. For example, the reception desk analyzes the user's social media activity and suggests events or parties of interest. For example, the reception desk suggests relevant gifts or prizes based on the user's social media activity. For example, the reception desk analyzes the user's social media activity and suggests the most suitable mode of transportation. This allows the reception desk to make relevant suggestions based on the user's social media activity. Social media activity analysis can be performed by AI, for example, by analyzing the user's posts, follower count, and engagement rate to make optimal suggestions. For example, the AI ​​can suggest relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can suggest the most suitable mode of transportation based on the user's social media activity. This allows the reception desk to make relevant suggestions based on the user's social media activity.

[0044] The planning department adjusts the level of detail in the plan based on the importance of the event. For example, for important events, the planning department provides a detailed plan that covers all arrangements. For general events, the planning department provides a basic plan that only handles the necessary arrangements. For simple events, the planning department provides a simple plan that handles the minimum arrangements. This allows the level of detail in the plan to be adjusted according to the importance of the event. The adjustment of plan detail can be done by, for example, AI evaluating the importance of the event and proposing the optimal plan. The AI ​​adjusts the level of detail in the plan by considering factors such as the number of participants, the scale of the event, and its impact. For example, for important events, the AI ​​provides a detailed plan that covers all arrangements. This allows the planning department to adjust the level of detail in the plan according to the importance of the event.

[0045] The planning department applies different planning algorithms depending on the event category when creating a plan. For example, for a birthday party, the planning department provides a plan that includes special gifts and prizes. For example, for a year-end party, the planning department provides a plan that includes arrangements for restaurants and games. For example, for a second party, the planning department provides a plan that includes last-minute venue and transportation arrangements. This allows the planning department to apply different planning algorithms depending on the event category. The application of the planning algorithm involves, for example, an AI analyzing the event category and proposing the optimal plan. The AI ​​applies different planning algorithms depending on, for example, the type and purpose of the event. For example, for a birthday party, the AI ​​provides a plan that includes special gifts and prizes. This allows the planning department to apply different planning algorithms depending on the event category.

[0046] The planning department prioritizes plans based on the timing of the events when creating them. For example, for upcoming events, the planning department prioritizes creating plans and making arrangements quickly. For example, for events far in the future, the planning department creates plans using standard procedures. For example, for events related to a specific season, the planning department provides plans tailored to that season. This allows the planning department to prioritize plans according to the timing of the events. The planning department can also prioritize plans by having an AI analyze the timing of the events and propose the optimal plan. The AI ​​considers factors such as the season, specific dates, and the event schedule to determine the priority of plans. For example, for upcoming events, the AI ​​prioritizes creating plans and making arrangements quickly. This allows the planning department to prioritize plans according to the timing of the events.

[0047] The planning department adjusts the order of plans based on the relevance of the events when creating them. For example, for important events, the planning department creates the plan first and prioritizes arrangements. For general events, the planning department creates the plan in the usual order. For simple events, the planning department creates the plan later. This allows the order of plans to be adjusted according to the relevance of the events. The adjustment of the order of plans can be done, for example, by having AI analyze the relevance of the events and propose the optimal plan. The AI ​​adjusts the order of plans by considering, for example, matching themes, commonalities among participants, and relevance to past events. For example, for important events, the AI ​​creates the plan first and prioritizes arrangements. This allows the planning department to adjust the order of plans according to the relevance of the events.

[0048] The option presentation unit adjusts the level of detail of the options based on the importance of the items when presenting options. For example, for important items, the option presentation unit provides detailed information and allows customization of the options. For example, for common items, the option presentation unit provides basic information and simplifies the options. For example, for simple items, the option presentation unit provides minimal information and presents options quickly. This allows the level of detail of the options to be adjusted according to the importance of the items. The adjustment of the level of detail of the options can be done, for example, by AI evaluating the importance of items and suggesting the best option. The AI ​​adjusts the level of detail of the options by considering, for example, price, quality, and popularity. For example, for important items, the AI ​​provides detailed information and allows customization of the options. This allows the option presentation unit to adjust the level of detail of the options according to the importance of the items.

[0049] The option presentation unit applies different option presentation algorithms depending on the item category when presenting options. For example, in the case of a gift, the option presentation unit provides special options and suggests customizable options. For example, in the case of a prize, the option presentation unit provides detailed information and makes the options customizable. For example, in the case of a game, the option presentation unit provides basic information and simplifies the options. This allows different option presentation algorithms to be applied depending on the item category. The application of the option presentation algorithm involves, for example, an AI analyzing the item category and suggesting the optimal option. The AI ​​applies different option presentation algorithms depending on the category, such as food, general merchandise, or electronic devices. For example, in the case of a gift, the AI ​​provides special options and suggests customizable options. This allows the option presentation unit to apply different option presentation algorithms depending on the item category.

[0050] The option presentation unit determines the priority of options based on the item's availability when presenting options. For example, for items that are coming soon, the option presentation unit prioritizes presenting options and arranging them quickly. For items that are coming far in the future, the option presentation unit presents options using the normal procedure. For items that are related to a specific season, the option presentation unit provides options appropriate for that season. This allows the option presentation unit to determine the priority of options according to the item's availability. Determining the priority of options can be done, for example, by an AI analyzing the item's availability and suggesting the optimal option. The AI ​​determines the priority of options by considering, for example, seasonal products, limited-edition products, and availability schedules. For example, for items that are coming soon, the AI ​​prioritizes presenting options and arranging them quickly. This allows the option presentation unit to determine the priority of options according to the item's availability.

[0051] The option presentation unit adjusts the order of options based on the relevance of the items when presenting them. For example, if an item is important, the option presentation unit presents the option first and prioritizes its arrangement. For example, if an item is common, the option presentation unit presents the options in the normal order. For example, if an item is simple, the option presentation unit presents the option later. This allows the order of options to be adjusted according to the relevance of the items. The adjustment of the order of options can be done, for example, by an AI analyzing the relevance of the items and suggesting the optimal option. The AI ​​adjusts the order of options by considering, for example, theme matching, user interests, and past purchase history. For example, if an item is important, the AI ​​presents the option first and prioritizes its arrangement. This allows the option presentation unit to adjust the order of options according to the relevance of the items.

[0052] The booking department analyzes the user's past booking history to select the optimal booking method. For example, the booking department proposes the optimal booking method based on the booking methods the user has used in the past. For example, the booking department extracts specific patterns from the user's past booking history and proposes the optimal booking method. For example, the booking department analyzes the user's past booking history and proposes booking trends. This allows the booking department to select the optimal booking method based on the user's past booking history. For example, the analysis of past booking history can be done by AI analyzing the user's past booking data and proposing the optimal booking method. For example, the AI ​​selects the optimal booking method considering factors such as booking type, success rate, and user evaluation. For example, the AI ​​can analyze and propose booking trends based on the user's past booking history. This allows the booking department to select the optimal booking method based on the user's past booking history.

[0053] The booking unit customizes the booking method based on the user's current situation when booking. For example, when the user inputs their current situation, the booking unit automatically displays the optimal booking method. For example, the booking unit customizes the booking method based on the user's current situation and efficiently collects necessary information. For example, the booking unit customizes the booking method based on the user's current situation and makes optimal suggestions. This allows the booking method to be customized according to the user's current situation. Customization of the booking method involves, for example, AI analyzing the user's current situation and suggesting the optimal booking method. The AI ​​customizes the booking method considering, for example, the user's location information, current activity status, and urgency. For example, the AI ​​automatically displays the booking method based on the user's current situation and efficiently collects information. This allows the booking unit to customize the booking method based on the user's current situation.

[0054] The booking department selects the optimal booking method when making arrangements, taking into account the user's geographical location. For example, the booking department may arrange nearby venues or restaurants based on the user's current location. For example, the booking department may arrange the most suitable mode of transportation based on the user's geographical location. For example, the booking department may arrange relevant gifts or prizes based on the user's geographical location. This allows the booking department to select the optimal booking method based on the user's geographical location. Consideration of geographical location involves, for example, an AI analyzing the user's current location and proposing the optimal booking method. The AI ​​may use, for example, GPS data or location services to identify the user's current location. The AI ​​may, for example, arrange highly relevant venues, restaurants, gifts, or prizes based on the user's geographical location. This allows the booking department to select the optimal booking method based on the user's geographical location.

[0055] The arrangement department analyzes the user's social media activity when making arrangements and proposes arrangement methods. For example, the arrangement department analyzes the user's social media activity and arranges events and parties of interest. For example, the arrangement department arranges relevant gifts and prizes based on the user's social media activity. For example, the arrangement department analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the arrangement department to propose arrangement methods based on the user's social media activity. Social media activity analysis can be performed by AI, for example, analyzing the user's posts, follower count, and engagement rate to propose the most suitable arrangement method. For example, the AI ​​can arrange relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can suggest the most suitable mode of transportation based on the user's social media activity. This allows the arrangement department to propose arrangement methods based on the user's social media activity.

[0056] The guidance system selects the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance system proposes the optimal guidance method based on guidance methods the user has used in the past. For example, the guidance system extracts specific patterns from the user's past guidance history and proposes the optimal guidance method. For example, the guidance system analyzes the user's past guidance history and proposes guidance trends. This allows the guidance system to select the optimal guidance method based on the user's past guidance history. For example, the analysis of past guidance history can be done by AI analyzing the user's past guidance data and proposing the optimal guidance method. For example, the AI ​​selects the optimal guidance method by considering the type of guidance, success rate, and user evaluation. For example, the AI ​​can analyze and propose guidance trends based on the user's past guidance history. This allows the guidance system to select the optimal guidance method based on the user's past guidance history.

[0057] The guidance unit customizes the guidance methods based on the user's current situation during guidance. For example, when the user inputs their current situation, the guidance unit automatically displays the most suitable guidance method. For example, the guidance unit customizes the guidance methods based on the user's current situation and efficiently collects necessary information. For example, the guidance unit customizes the guidance methods based on the user's current situation and makes optimal suggestions. This allows the guidance methods to be customized according to the user's current situation. Customization of guidance methods can be done, for example, by having AI analyze the user's current situation and suggest the most suitable guidance method. For example, the AI ​​customizes the guidance methods considering the user's location information, current activity status, and urgency. For example, the AI ​​can automatically display guidance methods based on the user's current situation and efficiently collect information. This allows the guidance unit to customize the guidance methods based on the user's current situation.

[0058] The guidance system selects the optimal guidance method when providing directions, taking into account the user's geographical location. For example, the guidance system can guide users to nearby venues or restaurants based on their current location. For example, the guidance system can guide users to the most suitable mode of transportation based on their geographical location. For example, the guidance system can guide users to relevant souvenirs or prizes based on their geographical location. This allows the guidance system to select the optimal guidance method based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location and proposing the optimal guidance method. The AI ​​can identify the user's current location using, for example, GPS data or location services. The AI ​​can guide users to highly relevant venues, restaurants, souvenirs, or prizes based on the user's geographical location. This allows the guidance system to select the optimal guidance method based on the user's geographical location.

[0059] The guidance department analyzes the user's social media activity when providing guidance and proposes guidance methods. For example, the guidance department analyzes the user's social media activity and provides guidance on events and parties of interest. For example, the guidance department provides guidance on relevant gifts and prizes based on the user's social media activity. For example, the guidance department analyzes the user's social media activity and provides guidance on the most suitable mode of transportation. This allows the guidance department to propose guidance methods based on the user's social media activity. Social media activity analysis can be performed by, for example, AI analyzing the user's posts, follower count, and engagement rate to propose the most suitable guidance method. For example, AI can provide guidance on relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, AI can propose the most suitable mode of transportation based on the user's social media activity. This allows the guidance department to propose guidance methods based on the user's social media activity.

[0060] The voice generation unit adjusts the level of detail in the voice output according to the reservation content. For example, for important reservations, the voice generation unit generates voice output containing detailed information. For example, for general reservations, the voice generation unit generates voice output containing basic information. For example, for simple reservations, the voice generation unit generates voice output containing minimal information. This allows the voice generation unit to adjust the level of detail in the voice output according to the reservation content. The adjustment of voice detail is done, for example, by an AI analyzing the reservation content and generating the optimal voice output. The AI ​​adjusts the content of the voice output, for example, by considering the importance and detail level of the reservation. For example, for important reservations, the AI ​​generates voice output containing detailed information. This allows the voice generation unit to adjust the level of detail in the voice output according to the reservation content.

[0061] The voice generation unit applies different voice generation algorithms depending on the reservation category when generating voices. For example, the voice generation unit applies a special voice generation algorithm for restaurant reservations. For example, the voice generation unit applies a basic voice generation algorithm for transportation reservations. For example, the voice generation unit applies a visually appealing voice generation algorithm for game reservations. This allows the voice generation unit to apply different voice generation algorithms depending on the reservation category. The application of voice generation algorithms involves, for example, the AI ​​analyzing the reservation category and generating the optimal voice. The AI ​​applies different voice generation algorithms depending on the category, such as restaurants, transportation, or games. For example, the AI ​​applies a special voice generation algorithm for restaurant reservations. This allows the voice generation unit to apply different voice generation algorithms depending on the reservation category.

[0062] The voice generation unit determines the priority of voices based on the timing of the reservation when generating voices. For example, for upcoming reservations, the voice generation unit prioritizes voice generation and ensures quick arrangements. For example, for reservations far in the future, the voice generation unit generates voices using the normal procedure. For example, for reservations related to a specific season, the voice generation unit generates voices appropriate for that season. This allows the voice generation unit to determine the priority of voices according to the timing of the reservation. Determining the priority of voices involves, for example, an AI analyzing the timing of the reservation and generating the optimal voice. The AI ​​determines the priority of voices by considering, for example, the season, a specific date, and the reservation schedule. For example, for upcoming reservations, the AI ​​prioritizes voice generation and ensures quick arrangements. This allows the voice generation unit to determine the priority of voices according to the timing of the reservation.

[0063] The voice generation unit adjusts the order of voices based on the relevance of the reservations during voice generation. For example, for important reservations, the voice generation unit generates the voice first and prioritizes their arrangement. For example, for general reservations, the voice generation unit generates the voices in the normal order. For example, for simple reservations, the voice generation unit generates the voice later. This allows the voice generation unit to adjust the order of voices according to the relevance of the reservations. The adjustment of the order of voices is done, for example, by the AI ​​analyzing the relevance of the reservations and generating the optimal voice. The AI ​​adjusts the order of voices by considering, for example, theme matching, user interests, and relevance to past reservations. For example, for important reservations, the AI ​​generates the voice first and prioritizes their arrangement. This allows the voice generation unit to adjust the order of voices according to the relevance of the reservations.

[0064] The last-minute after-party venue booking department analyzes the user's past venue booking history to select the optimal booking method. For example, the last-minute after-party venue booking department proposes the optimal booking method based on the venue booking methods the user has used in the past. For example, the last-minute after-party venue booking department extracts specific patterns from the user's past venue booking history and proposes the optimal booking method. For example, the last-minute after-party venue booking department analyzes the user's past venue booking history and proposes booking trends. This allows the department to select the optimal booking method based on the user's past venue booking history. For analysis of past venue booking history, for example, AI analyzes the user's past venue booking data and proposes the optimal booking method. For example, the AI ​​selects the optimal booking method considering factors such as booking type, success rate, and user evaluation. For example, the AI ​​can analyze and propose booking trends based on the user's past venue booking history. This allows the last-minute after-party venue booking department to select the optimal booking method based on the user's past venue booking history.

[0065] The last-minute after-party venue booking department customizes the booking method based on the user's current situation when booking a venue. For example, when the user enters their current situation, the last-minute after-party venue booking department automatically displays the optimal booking method. For example, the last-minute after-party venue booking department customizes the booking method based on the user's current situation and efficiently collects necessary information. For example, the last-minute after-party venue booking department customizes the booking method based on the user's current situation and makes the best suggestion. This allows the booking method to be customized according to the user's current situation. Customization of the booking method involves, for example, AI analyzing the user's current situation and suggesting the optimal booking method. The AI ​​customizes the booking method by considering, for example, the user's location information, current activity status, and urgency. For example, the AI ​​automatically displays the booking method based on the user's current situation and efficiently collects information. This allows the last-minute after-party venue booking department to customize the booking method based on the user's current situation.

[0066] The last-minute after-party venue arrangement department selects the optimal arrangement method by considering the user's geographical location when arranging a venue. For example, the last-minute after-party venue arrangement department will arrange a nearby venue based on the user's current location. For example, the last-minute after-party venue arrangement department will arrange the optimal means of transportation based on the user's geographical location. For example, the last-minute after-party venue arrangement department will arrange relevant gifts and prizes based on the user's geographical location. This allows the department to select the optimal arrangement method based on the user's geographical location. Consideration of geographical location information can be achieved, for example, by having AI analyze the user's current location and propose the optimal arrangement method. The AI ​​can identify the user's current location using, for example, GPS data or location services. The AI ​​can arrange highly relevant venues, means of transportation, gifts, and prizes based on the user's geographical location. This allows the last-minute after-party venue arrangement department to select the optimal arrangement method based on the user's geographical location.

[0067] The last-minute after-party venue arrangement department analyzes the user's social media activity when arranging a venue and proposes arrangement methods. For example, the last-minute after-party venue arrangement department analyzes the user's social media activity and arranges venues of interest. For example, the last-minute after-party venue arrangement department arranges relevant gifts and prizes based on the user's social media activity. For example, the last-minute after-party venue arrangement department analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the department to propose arrangement methods based on the user's social media activity. Social media activity analysis can be done by, for example, using AI to analyze the user's posts, follower count, and engagement rate and propose the most suitable arrangement method. For example, the AI ​​can arrange relevant venues, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can propose the most suitable mode of transportation based on the user's social media activity. This allows the last-minute after-party venue arrangement department to propose arrangement methods based on the user's social media activity.

[0068] A booking department that leverages a specific economic zone selects the optimal booking method by referring to past economic zone data during the booking process. For example, a booking department that leverages a specific economic zone might propose the optimal booking method based on previously used economic zone data. Another example is extracting specific patterns from past economic zone data and proposing the optimal booking method. A booking department that leverages a specific economic zone might analyze past economic zone data and propose booking trends. This allows for the selection of the optimal booking method based on past economic zone data. Referencing past economic zone data could involve, for example, AI analyzing past economic zone data and proposing the optimal booking method. The AI ​​might select the optimal booking method considering factors such as the type of economic zone, success rate, and user evaluation. The AI ​​could also analyze and propose booking trends based on past economic zone data. This allows a booking department that leverages a specific economic zone to select the optimal booking method based on past economic zone data.

[0069] A booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation during the booking process. For example, when the user inputs their current situation, the booking system automatically displays the most suitable means of travel within that economic zone. For example, the booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation and efficiently collects necessary information. For example, the booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation and makes optimal suggestions. This allows the means of travel within that economic zone to be customized according to the user's current situation. Customization of the means of travel within that economic zone can be done, for example, by having AI analyze the user's current situation and suggest the most suitable means of travel within that economic zone. The AI ​​customizes the means of travel within that economic zone, for example, by considering the user's location, current activity status, and urgency. The AI ​​can automatically display the means of travel within that economic zone based on the user's current situation and efficiently collect information. This allows the booking system that leverages a specific economic zone to customize the means of travel within that economic zone based on the user's current situation.

[0070] A booking system that leverages specific economic zones selects the optimal economic zone when booking, taking into account the user's geographical location. For example, it selects a nearby economic zone based on the user's current location. For example, it arranges the optimal mode of transportation based on the user's geographical location. For example, it arranges relevant souvenirs or prizes based on the user's geographical location. This allows the system to select the optimal economic zone based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location and suggesting the optimal economic zone. The AI ​​identifies the user's current location using, for example, GPS data or location services. The AI ​​can arrange highly relevant economic zones, modes of transportation, souvenirs, or prizes based on the user's geographical location. This allows the booking system that leverages specific economic zones to select the optimal economic zone based on the user's geographical location.

[0071] A booking department that leverages specific economic zones analyzes the user's social media activity during the booking process and proposes means within that economic zone. For example, a booking department that leverages specific economic zones analyzes the user's social media activity and arranges within the economic zone of interest. For example, a booking department that leverages specific economic zones arranges relevant gifts or prizes based on the user's social media activity. For example, a booking department that leverages specific economic zones analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the booking department to propose means within an economic zone based on the user's social media activity. Social media activity analysis can be performed by, for example, AI analyzing the user's posts, follower count, and engagement rate to propose the most suitable booking method. For example, AI can arrange relevant economic zones, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, AI can propose the most suitable mode of transportation based on the user's social media activity. This allows the booking department that leverages specific economic zones to propose means within an economic zone based on the user's social media activity.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The booking department can also analyze a user's past booking history and select the optimal booking method. For example, it can suggest the optimal booking method based on the booking methods the user has used in the past. It can also extract specific patterns from a user's past booking history and suggest the optimal booking method. It can also analyze a user's past booking history and suggest booking trends. This allows the system to select the optimal booking method based on the user's past booking history.

[0074] The option presentation unit can also analyze the user's past selection history when presenting gift or prize options and suggest the most suitable choice. For example, it can suggest gifts or prizes that match the user's preferences based on their past selection history. It can also extract specific patterns from the user's past selection history and suggest the most suitable choice. It can also analyze the user's past selection history and suggest selection trends. This allows for the suggestion of the most suitable choice based on the user's past selection history.

[0075] The booking system can also select the most suitable booking method by considering the user's geographical location. For example, it can book nearby venues or restaurants based on the user's current location. It can also book the most suitable mode of transportation based on the user's geographical location. It can also book related gifts or prizes based on the user's geographical location. This allows the system to select the most suitable booking method based on the user's geographical location.

[0076] The reception desk can also analyze users' social media activity and make relevant suggestions. For example, it can analyze users' social media activity and suggest events or parties that might interest them. It can also suggest relevant gifts or prizes based on users' social media activity. It can analyze users' social media activity and suggest the most suitable mode of transportation. This allows for relevant suggestions based on users' social media activity.

[0077] The planning department can also adjust the level of detail in the plan based on the importance of the event. For example, for important events, a detailed plan can be provided, covering all arrangements. For general events, a basic plan can be provided, covering only the necessary arrangements. For simple events, a simple plan can be provided, covering minimal arrangements. This allows for adjusting the level of detail in the plan according to the importance of the event.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The reception desk allows the user to specify the budget and purpose. For example, the user can enter the type of event and the budget range, and the reception desk uses this information to determine the purpose and budget of the event. Step 2: The planning department creates the optimal plan based on the budget and objectives specified by the reception department. For example, the planning department can select the best venues and restaurants within the user's budget and automatically generate a plan that meets the user's wishes using AI. Step 3: The option presentation unit presents gift and prize options based on the plan created by the plan creation unit. For example, the option presentation unit can present multiple gift and prize options according to the user's budget and purpose, and can use AI to suggest gifts and prizes that suit the user's preferences. Step 4: The arrangement unit makes reservations and purchases based on the options presented by the option presentation unit. For example, the arrangement unit can purchase gifts or prizes selected by the user online, and can use AI to automatically make reservations and purchases based on the user's selections. Step 5: The information department provides guidance to participants based on the information confirmed by the booking department regarding reservations and purchases. For example, the information department can send guidance to participants via email or message, or it can use AI to automatically provide guidance to participants.

[0080] (Example of form 2) The event preparation system according to an embodiment of the present invention is a system that uses an AI agent to make reservations, purchases, and arrangements for events and parties such as birthday parties, year-end parties, after-parties, and dinners all at once. This event preparation system creates an optimal plan and arranges venues, restaurants, gifts, prizes, transportation, games, etc., simply by the user specifying their budget and purpose. For example, when preparing for a birthday party, the user inputs their budget and purpose, and the AI ​​agent suggests the most suitable venue and restaurant, and presents options for gifts and prizes. Based on the user's selection, the AI ​​agent makes reservations and purchases, and also completes the notification to participants and prospective participants all at once. Furthermore, even if restaurants only accept telephone reservations, telephone reservations can be made using AI-generated voice generation. This allows for arrangements to be made without compromising quality or being limited to online reservations. For example, the AI ​​agent can handle tedious preparations such as arranging a venue for a last-minute after-party, preparing gifts and prizes, and arranging games all at once. This mechanism significantly reduces the effort required for users to prepare for events and parties. For example, an AI agent can handle all the tedious preparations, such as arranging a venue for a last-minute after-party, preparing gifts and prizes, and arranging games. Furthermore, by leveraging specific economic spheres to complete the arrangements, preparations can proceed efficiently. As a result, event preparation systems can significantly reduce the effort users put into preparing for events and parties.

[0081] The event preparation system according to this embodiment comprises a reception unit, a plan creation unit, a choice presentation unit, a booking unit, and a guidance unit. The reception unit allows the user to specify the budget and purpose. For example, the user can input the type of event and the range of the budget. The reception unit identifies the purpose and budget of the event based on the information entered by the user. The plan creation unit creates an optimal plan based on the budget and purpose specified by the reception unit. For example, the plan creation unit selects the best venue and restaurant within the user's budget. The plan creation unit can automatically generate a plan that suits the user's wishes using AI, for example. The choice presentation unit presents options for gifts and prizes based on the plan created by the plan creation unit. For example, the choice presentation unit presents multiple gift and prize options according to the user's budget and purpose. The choice presentation unit can suggest gifts and prizes that suit the user's preferences using AI, for example. The booking unit makes reservations and purchases based on the options presented by the choice presentation unit. For example, the booking unit purchases the gifts and prizes selected by the user online. The arrangement unit can, for example, use AI to automatically make reservations and purchases based on user selections. The guidance unit provides information to participants based on the information completed by the arrangement unit for reservations and purchases. For example, the guidance unit sends information to participants via email or message. The guidance unit can, for example, use AI to automatically provide information to participants. As a result, the event preparation system according to this embodiment allows users to specify their budget and objectives, and the system will complete the creation of the optimal plan, the presentation of gift and prize options, reservations and purchases, and participant guidance all in one go.

[0082] The reception desk allows users to specify their budget and purpose. For example, users can input the type of event and the budget range. Based on the information entered by the user, the reception desk identifies the purpose and budget of the event. Specifically, the information entered by the user includes the type of event (e.g., wedding, birthday party, corporate meeting), the number of participants, the desired date and time, location, and special requests (e.g., a specific theme or decoration, specific food or drink provision). The reception desk collects this information and stores it in a database. Furthermore, the reception desk can use AI to analyze the information entered by the user and identify the purpose and budget of the event. For example, natural language processing technology can be used to analyze the text information entered by the user and automatically extract the purpose and budget of the event. This allows the reception desk to make it easy for users to enter information and to proceed with event preparation smoothly. The reception desk also plays a role in providing necessary data to the planning and option presentation departments based on the information entered by the user. This allows the entire system to work together efficiently. In addition, the reception desk can update the information entered by the user in real time and make corrections or additions as needed. For example, if a user changes the budget or adds participants, the reception department can immediately reflect these changes and notify other departments. This allows the reception department to respond flexibly to user needs and support optimal event preparation.

[0083] The planning department creates the optimal plan based on the budget and objectives specified by the reception department. For example, the planning department selects the best venue and restaurant within the user's budget. The planning department can also use AI to automatically generate a plan that meets the user's wishes. Specifically, the planning department searches a database for suitable venue and restaurant candidates based on the budget and objectives specified by the user and proposes the optimal plan. The AI ​​can utilize past data and statistical information to generate a plan that is closest to the user's wishes. For example, it can select the best venue and restaurant based on feedback and evaluations from users who have held similar events in the past. The AI ​​can also customize the plan, taking into account the user's preferences and special requests. For example, it can propose a plan that meets the user's wishes, such as decorations tailored to a specific theme or the provision of specific food and drinks. Furthermore, the planning department can create a detailed schedule and budget estimate based on the plan selected by the user. This allows the user to grasp all the information necessary for event preparation in one place and proceed with the event smoothly. The planning department also provides an interface that allows the user to review, modify, and add to the plan. This allows users to check the plan details in real time and make changes as needed. For example, it enables flexible responses to user requests, such as changing the venue or arranging additional services. As a result, the plan creation department can provide the plan that best suits the user's needs and support the success of the event.

[0084] The option presentation unit presents gift and prize options based on the plan created by the plan creation unit. For example, the option presentation unit presents multiple gift and prize options according to the user's budget and purpose. The option presentation unit can, for example, use AI to suggest gifts and prizes that match the user's preferences. Specifically, the option presentation unit searches the database for suitable gift and prize candidates based on the budget and purpose specified by the user and proposes the best option. The AI ​​can use past data and statistical information to suggest gifts and prizes that are closest to the user's preferences. For example, it can present the user with the best option based on gifts and prizes that were popular at similar events in the past. The AI ​​can also customize the options, taking into account the user's preferences and special requests. For example, it can suggest options that match the user's wishes, such as gifts that fit a specific theme or prizes from a specific brand. Furthermore, the option presentation unit can provide detailed information and images based on the gifts and prizes selected by the user. This allows the user to see the contents of the gifts and prizes at a glance and use it as a reference for selection. Furthermore, the selection display unit provides an interface for users to compare the gifts and prizes they have selected. This allows users to compare multiple options and select the most suitable gift or prize. For example, users can compare options based on points they value, such as price, quality, and design. In this way, the selection display unit can suggest the most suitable gift or prize to the user and support the success of the event.

[0085] The arrangement department makes reservations and purchases based on the options presented by the option presentation department. For example, the arrangement department purchases gifts or prizes selected by the user online. The arrangement department can use AI to automatically make reservations and purchases based on the user's selections. Specifically, the arrangement department checks the inventory status of gifts or prizes selected by the user and makes reservations or purchases at the optimal time. The AI ​​works in conjunction with the inventory management system to grasp inventory status in real time and secure the necessary gifts or prizes. The arrangement department also handles the delivery arrangements for gifts and prizes selected by the user. For example, it handles the procedures for delivering gifts or prizes to a specified location at a specified date and time. Through cooperation with delivery companies, the AI ​​calculates the optimal delivery route and schedule, enabling the gifts and prizes to be delivered quickly and reliably. Furthermore, the arrangement department also makes reservations for venues and restaurants selected by the user. For example, it checks the availability of venues and secures reservations for the date and time desired by the user. The AI ​​works in conjunction with the venue reservation system to grasp availability in real time and secure the most suitable venue. Similarly, the system also handles restaurant reservations and arrangements, ensuring that the user receives the desired menu and services. This allows the arrangement department to quickly and reliably make reservations and purchases based on the user's selection, enabling smooth event preparations. Furthermore, the arrangement department manages the reservation and purchase status in real time and reports the progress to the user. This allows the user to always be aware of the event preparation status and proceed with the event with peace of mind.

[0086] The information department provides guidance to participants based on information completed by the booking department for reservations and purchases. For example, the information department sends guidance to participants via email or message. The information department can also automate the process of providing guidance to participants using AI. Specifically, the information department creates detailed guidance for participants based on the reservation and purchase information provided by the booking department. The AI ​​manages participant lists and contact information and can send guidance at the optimal time. For example, it creates guidance that includes detailed information such as the date and time of the event, the location, the meeting place and time for participants, and notes on what to bring and dress code, and sends it to participants. The information department also provides an interface to handle inquiries from participants. For example, it can provide a chatbot that participants can use to ask questions or clarify any uncertainties regarding the guidance, and the AI ​​can automatically answer them. This allows participants to obtain information quickly and accurately, and to participate in the event with peace of mind. Furthermore, the information department also provides guidance and support on the day of the event. For example, it can station staff to handle registration and guidance when participants arrive at the venue, supporting the smooth running of the event. The AI ​​can monitor participant arrival status in real time and issue instructions to staff as needed. This allows the event planning department to provide participants with quick and accurate information, supporting the success of the event. Furthermore, the planning department also follows up after the event, for example, by sending thank-you messages to participants and collecting feedback. AI can analyze participant feedback and identify areas for improvement in future events. This allows the planning department to improve the overall quality of the event and support preparations for the next event.

[0087] The booking unit includes a voice generation unit that uses AI-generated voice to make reservations when only telephone reservations are accepted. For example, if a restaurant only accepts telephone reservations, the booking unit can use AI-generated voice to make the reservation. For example, the booking unit can use the AI-generated voice to call the restaurant and make the reservation. For example, the booking unit can use the AI-generated voice to convey the reservation details. This makes it possible to make reservations using AI-generated voice even when only telephone reservations are accepted. Voice generation is performed using, for example, text-to-speech technology. The AI ​​generates natural-sounding voice based on the reservation information entered by the user. The AI ​​can adjust the tone and speed of the voice to accurately convey the reservation details. This makes it possible for the booking unit to make reservations using AI-generated voice even when only telephone reservations are accepted.

[0088] The planning department handles last-minute venue arrangements for after-parties. For example, to quickly arrange a venue for a last-minute after-party, the planning department uses AI to select the optimal venue. For instance, if a user requests a last-minute after-party venue, the AI ​​can suggest the most suitable venue. The planning department, for example, uses AI to check venue availability and prices in real time and select the best venue. This allows for quick arrangements for last-minute after-parties. For last-minute after-party venue arrangements, for example, the AI ​​selects the best venue based on the user's preferences. The AI, for example, checks venue availability and prices in real time and suggests the best venue. The AI ​​can, for example, present multiple venue options according to the user's budget and purpose. This allows the planning department to quickly arrange venues for last-minute after-parties.

[0089] The selection display unit presents options for gifts and prizes. For example, the selection display unit presents multiple gift and prize options based on the user's budget and purpose. The selection display unit can, for example, use AI to suggest gifts and prizes that suit the user's preferences. For example, the selection display unit considers the type and price range of gifts and prizes to present the optimal option. This allows the user to select from a selection of gifts and prizes. The presentation of gift and prize options can, for example, involve AI suggesting the optimal option based on the user's preferences. For example, the AI ​​considers the type and price range of gifts and prizes to present the optimal option. For example, the AI ​​can analyze the user's past selection history to suggest gifts and prizes that suit the user's preferences. This allows the selection display unit to present gift and prize options for the user to select from.

[0090] The information department provides information to participants and prospective participants. For example, the information department sends information to participants via email or message. The information department can also use AI to automate the process of providing information to participants. For example, the information department can select the most appropriate method of notification based on participants' contact information. This allows for the information department to send information to participants and prospective participants in bulk. For example, the AI ​​selects the most appropriate method of notification based on participants' contact information. The AI ​​sends information via email or message. The AI ​​can also send information at the optimal time, taking into account participants' schedules. This allows the information department to send information to participants and prospective participants in bulk.

[0091] The arrangement department completes arrangements by leveraging a specific economic zone. For example, the arrangement department efficiently carries out arrangements by leveraging a specific economic zone. For example, the arrangement department can use AI to select the optimal arrangement method within a specific economic zone. For example, the arrangement department completes arrangements by utilizing businesses and services within a specific economic zone. This allows for efficient arrangement completion by leveraging a specific economic zone. Arrangements utilizing a specific economic zone involve, for example, AI selecting the optimal arrangement method within that economic zone. For example, AI can complete arrangements by utilizing businesses and services within a specific economic zone. For example, AI can propose the optimal arrangement method to maximize the efficiency of arrangements within a specific economic zone. This allows the arrangement department to efficiently complete arrangements by leveraging a specific economic zone.

[0092] The reception desk estimates the user's emotions and adjusts the input method for budget and objectives based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of budget and objectives. This allows the input method for budget and objectives to be adjusted 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) or multimodal generation AI. The reception desk estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the reception desk to adjust the input method for budget and objectives based on the user's emotions.

[0093] The reception desk analyzes the user's past event history and proposes the optimal budget and objectives. For example, the reception desk makes optimal suggestions based on the budget and objectives of events the user has held in the past. For example, the reception desk extracts specific patterns from the user's past event history and proposes budgets and objectives. For example, the reception desk analyzes the user's past event history and proposes trends in budgets and objectives. This allows the reception desk to propose the optimal budget and objectives based on the user's past event history. Analysis of past event history can be done by, for example, AI analyzing the user's past event data and making optimal suggestions. For example, the AI ​​considers the type of past event, the number of participants, and the evaluation to propose the optimal budget and objectives. For example, the AI ​​can analyze and propose trends in budgets and objectives based on the user's past event history. This allows the reception desk to propose the optimal budget and objectives based on the user's past event history.

[0094] The reception desk customizes input fields based on the user's current situation and areas of interest during registration. For example, when the user enters their current situation, the reception desk automatically displays relevant input fields based on their areas of interest. For example, the reception desk customizes input fields based on the user's current situation and efficiently collects necessary information. For example, the reception desk customizes input fields based on the user's areas of interest and makes optimal suggestions. This allows the reception desk to customize input fields according to the user's current situation and areas of interest. Customizing input fields involves, for example, AI analyzing the user's current situation and areas of interest and suggesting optimal input fields. For example, the AI ​​customizes input fields based on the user's behavioral history and interest data. For example, the AI ​​automatically displays input fields based on the user's current situation and areas of interest and efficiently collects information. This allows the reception desk to customize input fields based on the user's current situation and areas of interest.

[0095] The reception desk estimates the user's emotions and determines the priority of reception based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize their reception and provide prompt service. For example, if the user is relaxed, the reception desk will apply the normal reception procedure. For example, if the user is in a hurry, the reception desk will prioritize their reception and provide prompt service. This allows the reception desk to determine the priority of reception 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. The reception desk estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the reception desk to determine the priority of reception based on the user's emotions.

[0096] The reception desk, upon receiving a registration, considers the user's geographical location to provide highly relevant suggestions. For example, the reception desk may suggest nearby venues or restaurants based on the user's current location. For example, the reception desk may suggest the most suitable mode of transportation based on the user's geographical location. For example, the reception desk may suggest relevant gifts or prizes based on the user's geographical location. This allows the reception desk to provide highly relevant suggestions based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location to provide optimal suggestions. The AI ​​may use, for example, GPS data or location services to identify the user's current location. The AI ​​can, for example, suggest highly relevant venues, restaurants, gifts, or prizes based on the user's geographical location. This allows the reception desk to provide highly relevant suggestions based on the user's geographical location.

[0097] The reception desk analyzes the user's social media activity at the time of registration and makes relevant suggestions. For example, the reception desk analyzes the user's social media activity and suggests events or parties of interest. For example, the reception desk suggests relevant gifts or prizes based on the user's social media activity. For example, the reception desk analyzes the user's social media activity and suggests the most suitable mode of transportation. This allows the reception desk to make relevant suggestions based on the user's social media activity. Social media activity analysis can be performed by AI, for example, by analyzing the user's posts, follower count, and engagement rate to make optimal suggestions. For example, the AI ​​can suggest relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can suggest the most suitable mode of transportation based on the user's social media activity. This allows the reception desk to make relevant suggestions based on the user's social media activity.

[0098] The planning unit estimates the user's emotions and adjusts the way the plan is presented based on those emotions. For example, if the user is relaxed, the planning unit provides a detailed plan and suggests customizable options. If the user is in a hurry, the planning unit provides a simple and quickly understandable plan. If the user is excited, the planning unit provides a visually appealing plan. This allows the plan's presentation to be adjusted according to the user's 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. The planning unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the planning unit to adjust the plan's presentation based on the user's emotions.

[0099] The planning department adjusts the level of detail in the plan based on the importance of the event. For example, for important events, the planning department provides a detailed plan that covers all arrangements. For general events, the planning department provides a basic plan that only handles the necessary arrangements. For simple events, the planning department provides a simple plan that handles the minimum arrangements. This allows the level of detail in the plan to be adjusted according to the importance of the event. The adjustment of plan detail can be done by, for example, AI evaluating the importance of the event and proposing the optimal plan. The AI ​​adjusts the level of detail in the plan by considering factors such as the number of participants, the scale of the event, and its impact. For example, for important events, the AI ​​provides a detailed plan that covers all arrangements. This allows the planning department to adjust the level of detail in the plan according to the importance of the event.

[0100] The planning department applies different planning algorithms depending on the event category when creating a plan. For example, for a birthday party, the planning department provides a plan that includes special gifts and prizes. For example, for a year-end party, the planning department provides a plan that includes arrangements for restaurants and games. For example, for a second party, the planning department provides a plan that includes last-minute venue and transportation arrangements. This allows the planning department to apply different planning algorithms depending on the event category. The application of the planning algorithm involves, for example, an AI analyzing the event category and proposing the optimal plan. The AI ​​applies different planning algorithms depending on, for example, the type and purpose of the event. For example, for a birthday party, the AI ​​provides a plan that includes special gifts and prizes. This allows the planning department to apply different planning algorithms depending on the event category.

[0101] The planning unit estimates the user's emotions and adjusts the length of the plan based on the estimated emotions. For example, if the user is relaxed, the planning unit provides a detailed plan with longer explanations. If the user is in a hurry, the planning unit provides a simple and short plan. If the user is excited, the planning unit provides a visually appealing plan. This allows the length of the plan to be adjusted 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. The planning unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the planning unit to adjust the length of the plan based on the user's emotions.

[0102] The planning department prioritizes plans based on the timing of the events when creating them. For example, for upcoming events, the planning department prioritizes creating plans and making arrangements quickly. For example, for events far in the future, the planning department creates plans using standard procedures. For example, for events related to a specific season, the planning department provides plans tailored to that season. This allows the planning department to prioritize plans according to the timing of the events. The planning department can also prioritize plans by having an AI analyze the timing of the events and propose the optimal plan. The AI ​​considers factors such as the season, specific dates, and the event schedule to determine the priority of plans. For example, for upcoming events, the AI ​​prioritizes creating plans and making arrangements quickly. This allows the planning department to prioritize plans according to the timing of the events.

[0103] The planning department adjusts the order of plans based on the relevance of the events when creating them. For example, for important events, the planning department creates the plan first and prioritizes arrangements. For general events, the planning department creates the plan in the usual order. For simple events, the planning department creates the plan later. This allows the order of plans to be adjusted according to the relevance of the events. The adjustment of the order of plans can be done, for example, by having AI analyze the relevance of the events and propose the optimal plan. The AI ​​adjusts the order of plans by considering, for example, matching themes, commonalities among participants, and relevance to past events. For example, for important events, the AI ​​creates the plan first and prioritizes arrangements. This allows the planning department to adjust the order of plans according to the relevance of the events.

[0104] The option presentation unit estimates the user's emotions and adjusts how the options are displayed based on the estimated emotions. For example, if the user is relaxed, the option presentation unit provides detailed options and suggests customizable options. For example, if the user is in a hurry, the option presentation unit provides simple and quickly understandable options. For example, if the user is excited, the option presentation unit provides visually appealing options. This allows the display of options to be adjusted 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. The option presentation unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the option presentation unit to adjust how the options are displayed based on the user's emotions.

[0105] The option presentation unit adjusts the level of detail of the options based on the importance of the items when presenting options. For example, for important items, the option presentation unit provides detailed information and allows customization of the options. For example, for common items, the option presentation unit provides basic information and simplifies the options. For example, for simple items, the option presentation unit provides minimal information and presents options quickly. This allows the level of detail of the options to be adjusted according to the importance of the items. The adjustment of the level of detail of the options can be done, for example, by AI evaluating the importance of items and suggesting the best option. The AI ​​adjusts the level of detail of the options by considering, for example, price, quality, and popularity. For example, for important items, the AI ​​provides detailed information and allows customization of the options. This allows the option presentation unit to adjust the level of detail of the options according to the importance of the items.

[0106] The option presentation unit applies different option presentation algorithms depending on the item category when presenting options. For example, in the case of a gift, the option presentation unit provides special options and suggests customizable options. For example, in the case of a prize, the option presentation unit provides detailed information and makes the options customizable. For example, in the case of a game, the option presentation unit provides basic information and simplifies the options. This allows different option presentation algorithms to be applied depending on the item category. The application of the option presentation algorithm involves, for example, an AI analyzing the item category and suggesting the optimal option. The AI ​​applies different option presentation algorithms depending on the category, such as food, general merchandise, or electronic devices. For example, in the case of a gift, the AI ​​provides special options and suggests customizable options. This allows the option presentation unit to apply different option presentation algorithms depending on the item category.

[0107] The option presentation unit estimates the user's emotions and adjusts the length of the options based on the estimated emotions. For example, if the user is relaxed, the option presentation unit provides detailed options with longer explanations. For example, if the user is in a hurry, the option presentation unit provides simple and short options. For example, if the user is excited, the option presentation unit provides visually appealing options. This allows the length of the options to be adjusted 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. The option presentation unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the option presentation unit to adjust the length of the options based on the user's emotions.

[0108] The option presentation unit determines the priority of options based on the item's availability when presenting options. For example, for items that are coming soon, the option presentation unit prioritizes presenting options and arranging them quickly. For items that are coming far in the future, the option presentation unit presents options using the normal procedure. For items that are related to a specific season, the option presentation unit provides options appropriate for that season. This allows the option presentation unit to determine the priority of options according to the item's availability. Determining the priority of options can be done, for example, by an AI analyzing the item's availability and suggesting the optimal option. The AI ​​determines the priority of options by considering, for example, seasonal products, limited-edition products, and availability schedules. For example, for items that are coming soon, the AI ​​prioritizes presenting options and arranging them quickly. This allows the option presentation unit to determine the priority of options according to the item's availability.

[0109] The option presentation unit adjusts the order of options based on the relevance of the items when presenting them. For example, if an item is important, the option presentation unit presents the option first and prioritizes its arrangement. For example, if an item is common, the option presentation unit presents the options in the normal order. For example, if an item is simple, the option presentation unit presents the option later. This allows the order of options to be adjusted according to the relevance of the items. The adjustment of the order of options can be done, for example, by an AI analyzing the relevance of the items and suggesting the optimal option. The AI ​​adjusts the order of options by considering, for example, theme matching, user interests, and past purchase history. For example, if an item is important, the AI ​​presents the option first and prioritizes its arrangement. This allows the option presentation unit to adjust the order of options according to the relevance of the items.

[0110] The ordering unit estimates the user's emotions and adjusts the ordering method based on the estimated emotions. For example, if the user is relaxed, the ordering unit provides a detailed ordering method and suggests customizable options. For example, if the user is in a hurry, the ordering unit provides a simple and quick ordering method. For example, if the user is excited, the ordering unit provides a visually appealing ordering method. This allows the ordering method to be adjusted 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. The ordering unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the ordering unit to adjust the ordering method based on the user's emotions.

[0111] The booking department analyzes the user's past booking history to select the optimal booking method. For example, the booking department proposes the optimal booking method based on the booking methods the user has used in the past. For example, the booking department extracts specific patterns from the user's past booking history and proposes the optimal booking method. For example, the booking department analyzes the user's past booking history and proposes booking trends. This allows the booking department to select the optimal booking method based on the user's past booking history. For example, the analysis of past booking history can be done by AI analyzing the user's past booking data and proposing the optimal booking method. For example, the AI ​​selects the optimal booking method considering factors such as booking type, success rate, and user evaluation. For example, the AI ​​can analyze and propose booking trends based on the user's past booking history. This allows the booking department to select the optimal booking method based on the user's past booking history.

[0112] The booking unit customizes the booking method based on the user's current situation when booking. For example, when the user inputs their current situation, the booking unit automatically displays the optimal booking method. For example, the booking unit customizes the booking method based on the user's current situation and efficiently collects necessary information. For example, the booking unit customizes the booking method based on the user's current situation and makes optimal suggestions. This allows the booking method to be customized according to the user's current situation. Customization of the booking method involves, for example, AI analyzing the user's current situation and suggesting the optimal booking method. The AI ​​customizes the booking method considering, for example, the user's location information, current activity status, and urgency. For example, the AI ​​automatically displays the booking method based on the user's current situation and efficiently collects information. This allows the booking unit to customize the booking method based on the user's current situation.

[0113] The booking unit estimates the user's emotions and determines booking priorities based on the estimated emotions. For example, if the user is nervous, the booking unit will prioritize booking and respond quickly. For example, if the user is relaxed, the booking unit will apply the normal booking procedure. For example, if the user is in a hurry, the booking unit will prioritize booking and respond quickly. This allows the booking unit to determine booking priorities 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. The booking unit estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the booking unit to determine booking priorities based on the user's emotions.

[0114] The booking department selects the optimal booking method when making arrangements, taking into account the user's geographical location. For example, the booking department may arrange nearby venues or restaurants based on the user's current location. For example, the booking department may arrange the most suitable mode of transportation based on the user's geographical location. For example, the booking department may arrange relevant gifts or prizes based on the user's geographical location. This allows the booking department to select the optimal booking method based on the user's geographical location. Consideration of geographical location involves, for example, an AI analyzing the user's current location and proposing the optimal booking method. The AI ​​may use, for example, GPS data or location services to identify the user's current location. The AI ​​may, for example, arrange highly relevant venues, restaurants, gifts, or prizes based on the user's geographical location. This allows the booking department to select the optimal booking method based on the user's geographical location.

[0115] The arrangement department analyzes the user's social media activity when making arrangements and proposes arrangement methods. For example, the arrangement department analyzes the user's social media activity and arranges events and parties of interest. For example, the arrangement department arranges relevant gifts and prizes based on the user's social media activity. For example, the arrangement department analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the arrangement department to propose arrangement methods based on the user's social media activity. Social media activity analysis can be performed by AI, for example, analyzing the user's posts, follower count, and engagement rate to propose the most suitable arrangement method. For example, the AI ​​can arrange relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can suggest the most suitable mode of transportation based on the user's social media activity. This allows the arrangement department to propose arrangement methods based on the user's social media activity.

[0116] The guidance unit estimates the user's emotions and adjusts the display method of the guidance based on the estimated emotions. For example, if the user is nervous, the guidance unit provides a simple and highly visible display method. For example, if the user is relaxed, the guidance unit provides a display method that includes detailed information. For example, if the user is in a hurry, the guidance unit provides a display method that gets straight to the point. This allows the guidance display method to be adjusted 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. The guidance unit estimates emotions by, for example, analyzing the user's facial expressions and voice. This allows the guidance unit to adjust the display method of the guidance based on the user's emotions.

[0117] The guidance system selects the optimal guidance method by referring to the user's past guidance history when providing guidance. For example, the guidance system proposes the optimal guidance method based on guidance methods the user has used in the past. For example, the guidance system extracts specific patterns from the user's past guidance history and proposes the optimal guidance method. For example, the guidance system analyzes the user's past guidance history and proposes guidance trends. This allows the guidance system to select the optimal guidance method based on the user's past guidance history. For example, the analysis of past guidance history can be done by AI analyzing the user's past guidance data and proposing the optimal guidance method. For example, the AI ​​selects the optimal guidance method by considering the type of guidance, success rate, and user evaluation. For example, the AI ​​can analyze and propose guidance trends based on the user's past guidance history. This allows the guidance system to select the optimal guidance method based on the user's past guidance history.

[0118] The guidance unit customizes the guidance methods based on the user's current situation during guidance. For example, when the user inputs their current situation, the guidance unit automatically displays the most suitable guidance method. For example, the guidance unit customizes the guidance methods based on the user's current situation and efficiently collects necessary information. For example, the guidance unit customizes the guidance methods based on the user's current situation and makes optimal suggestions. This allows the guidance methods to be customized according to the user's current situation. Customization of guidance methods can be done, for example, by having AI analyze the user's current situation and suggest the most suitable guidance method. For example, the AI ​​customizes the guidance methods considering the user's location information, current activity status, and urgency. For example, the AI ​​can automatically display guidance methods based on the user's current situation and efficiently collect information. This allows the guidance unit to customize the guidance methods based on the user's current situation.

[0119] The guidance system estimates the user's emotions and determines the priority of guidance based on the estimated emotions. For example, if the user is nervous, the guidance system will prioritize guidance and respond quickly. For example, if the user is relaxed, the guidance system will apply the normal guidance procedure. For example, if the user is in a hurry, the guidance system will prioritize guidance and respond quickly. This allows the guidance system to determine the priority of guidance 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples. The guidance system estimates emotions by, for example, analyzing the user's facial expressions and voice. This allows the guidance system to determine the priority of guidance based on the user's emotions.

[0120] The guidance system selects the optimal guidance method when providing directions, taking into account the user's geographical location. For example, the guidance system can guide users to nearby venues or restaurants based on their current location. For example, the guidance system can guide users to the most suitable mode of transportation based on their geographical location. For example, the guidance system can guide users to relevant souvenirs or prizes based on their geographical location. This allows the guidance system to select the optimal guidance method based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location and proposing the optimal guidance method. The AI ​​can identify the user's current location using, for example, GPS data or location services. The AI ​​can guide users to highly relevant venues, restaurants, souvenirs, or prizes based on the user's geographical location. This allows the guidance system to select the optimal guidance method based on the user's geographical location.

[0121] The guidance department analyzes the user's social media activity when providing guidance and proposes guidance methods. For example, the guidance department analyzes the user's social media activity and provides guidance on events and parties of interest. For example, the guidance department provides guidance on relevant gifts and prizes based on the user's social media activity. For example, the guidance department analyzes the user's social media activity and provides guidance on the most suitable mode of transportation. This allows the guidance department to propose guidance methods based on the user's social media activity. Social media activity analysis can be performed by, for example, AI analyzing the user's posts, follower count, and engagement rate to propose the most suitable guidance method. For example, AI can provide guidance on relevant events, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, AI can propose the most suitable mode of transportation based on the user's social media activity. This allows the guidance department to propose guidance methods based on the user's social media activity.

[0122] The voice generation unit estimates the user's emotions and adjusts the tone and speed of the voice based on the estimated emotions. For example, if the user is nervous, the voice generation unit will generate a calm tone and slow speed. For example, if the user is relaxed, the voice generation unit will generate a bright tone and normal speed. For example, if the user is in a hurry, the voice generation unit will generate a quick and concise voice. This allows the voice to adjust the tone and speed 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The voice generation unit estimates emotions by, for example, analyzing the user's facial expressions and voice. This allows the voice generation unit to adjust the tone and speed of the voice based on the user's emotions.

[0123] The voice generation unit adjusts the level of detail in the voice output according to the reservation content. For example, for important reservations, the voice generation unit generates voice output containing detailed information. For example, for general reservations, the voice generation unit generates voice output containing basic information. For example, for simple reservations, the voice generation unit generates voice output containing minimal information. This allows the voice generation unit to adjust the level of detail in the voice output according to the reservation content. The adjustment of voice detail is done, for example, by an AI analyzing the reservation content and generating the optimal voice output. The AI ​​adjusts the content of the voice output, for example, by considering the importance and detail level of the reservation. For example, for important reservations, the AI ​​generates voice output containing detailed information. This allows the voice generation unit to adjust the level of detail in the voice output according to the reservation content.

[0124] The voice generation unit applies different voice generation algorithms depending on the reservation category when generating voices. For example, the voice generation unit applies a special voice generation algorithm for restaurant reservations. For example, the voice generation unit applies a basic voice generation algorithm for transportation reservations. For example, the voice generation unit applies a visually appealing voice generation algorithm for game reservations. This allows the voice generation unit to apply different voice generation algorithms depending on the reservation category. The application of voice generation algorithms involves, for example, the AI ​​analyzing the reservation category and generating the optimal voice. The AI ​​applies different voice generation algorithms depending on the category, such as restaurants, transportation, or games. For example, the AI ​​applies a special voice generation algorithm for restaurant reservations. This allows the voice generation unit to apply different voice generation algorithms depending on the reservation category.

[0125] The voice generation unit estimates the user's emotions and adjusts the length of the audio based on the estimated emotions. For example, if the user is relaxed, the voice generation unit generates detailed audio, including longer explanations. For example, if the user is in a hurry, the voice generation unit generates simple and short audio. For example, if the user is excited, the voice generation unit generates visually appealing audio. This allows the length of the audio to be adjusted 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The voice generation unit estimates emotions by, for example, analyzing the user's facial expressions and voice. This allows the voice generation unit to adjust the length of the audio based on the user's emotions.

[0126] The voice generation unit determines the priority of voices based on the timing of the reservation when generating voices. For example, for upcoming reservations, the voice generation unit prioritizes voice generation and ensures quick arrangements. For example, for reservations far in the future, the voice generation unit generates voices using the normal procedure. For example, for reservations related to a specific season, the voice generation unit generates voices appropriate for that season. This allows the voice generation unit to determine the priority of voices according to the timing of the reservation. Determining the priority of voices involves, for example, an AI analyzing the timing of the reservation and generating the optimal voice. The AI ​​determines the priority of voices by considering, for example, the season, a specific date, and the reservation schedule. For example, for upcoming reservations, the AI ​​prioritizes voice generation and ensures quick arrangements. This allows the voice generation unit to determine the priority of voices according to the timing of the reservation.

[0127] The voice generation unit adjusts the order of voices based on the relevance of the reservations during voice generation. For example, for important reservations, the voice generation unit generates the voice first and prioritizes their arrangement. For example, for general reservations, the voice generation unit generates the voices in the normal order. For example, for simple reservations, the voice generation unit generates the voice later. This allows the voice generation unit to adjust the order of voices according to the relevance of the reservations. The adjustment of the order of voices is done, for example, by the AI ​​analyzing the relevance of the reservations and generating the optimal voice. The AI ​​adjusts the order of voices by considering, for example, theme matching, user interests, and relevance to past reservations. For example, for important reservations, the AI ​​generates the voice first and prioritizes their arrangement. This allows the voice generation unit to adjust the order of voices according to the relevance of the reservations.

[0128] The last-minute after-party venue arrangement department estimates the user's emotions and adjusts the venue arrangement method based on the estimated emotions. For example, if the user is relaxed, the last-minute after-party venue arrangement department provides a detailed venue arrangement method and suggests customizable options. For example, if the user is in a hurry, the last-minute after-party venue arrangement department provides a simple and quick arrangement method. For example, if the user is excited, the last-minute after-party venue arrangement department provides a visually appealing venue arrangement method. This allows the venue arrangement method to be adjusted 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. The last-minute after-party venue arrangement department estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the last-minute after-party venue arrangement department to adjust the venue arrangement method based on the user's emotions.

[0129] The last-minute after-party venue booking department analyzes the user's past venue booking history to select the optimal booking method. For example, the last-minute after-party venue booking department proposes the optimal booking method based on the venue booking methods the user has used in the past. For example, the last-minute after-party venue booking department extracts specific patterns from the user's past venue booking history and proposes the optimal booking method. For example, the last-minute after-party venue booking department analyzes the user's past venue booking history and proposes booking trends. This allows the department to select the optimal booking method based on the user's past venue booking history. For analysis of past venue booking history, for example, AI analyzes the user's past venue booking data and proposes the optimal booking method. For example, the AI ​​selects the optimal booking method considering factors such as booking type, success rate, and user evaluation. For example, the AI ​​can analyze and propose booking trends based on the user's past venue booking history. This allows the last-minute after-party venue booking department to select the optimal booking method based on the user's past venue booking history.

[0130] The last-minute after-party venue booking department customizes the booking method based on the user's current situation when booking a venue. For example, when the user enters their current situation, the last-minute after-party venue booking department automatically displays the optimal booking method. For example, the last-minute after-party venue booking department customizes the booking method based on the user's current situation and efficiently collects necessary information. For example, the last-minute after-party venue booking department customizes the booking method based on the user's current situation and makes the best suggestion. This allows the booking method to be customized according to the user's current situation. Customization of the booking method involves, for example, AI analyzing the user's current situation and suggesting the optimal booking method. The AI ​​customizes the booking method by considering, for example, the user's location information, current activity status, and urgency. For example, the AI ​​automatically displays the booking method based on the user's current situation and efficiently collects information. This allows the last-minute after-party venue booking department to customize the booking method based on the user's current situation.

[0131] The last-minute after-party venue booking department estimates the user's emotions and determines the priority of venue bookings based on the estimated emotions. For example, if the user is nervous, the last-minute after-party venue booking department will prioritize venue bookings and respond quickly. For example, if the user is relaxed, the last-minute after-party venue booking department will apply the normal booking procedure. For example, if the user is in a hurry, the last-minute after-party venue booking department will prioritize venue bookings and respond quickly. This allows the department to determine the priority of venue bookings 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, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. The last-minute after-party venue booking department estimates emotions by analyzing the user's facial expressions and voice, for example. This allows the department to determine the priority of venue bookings based on the user's emotions.

[0132] The last-minute after-party venue arrangement department selects the optimal arrangement method by considering the user's geographical location when arranging a venue. For example, the last-minute after-party venue arrangement department will arrange a nearby venue based on the user's current location. For example, the last-minute after-party venue arrangement department will arrange the optimal means of transportation based on the user's geographical location. For example, the last-minute after-party venue arrangement department will arrange relevant gifts and prizes based on the user's geographical location. This allows the department to select the optimal arrangement method based on the user's geographical location. Consideration of geographical location information can be achieved, for example, by having AI analyze the user's current location and propose the optimal arrangement method. The AI ​​can identify the user's current location using, for example, GPS data or location services. The AI ​​can arrange highly relevant venues, means of transportation, gifts, and prizes based on the user's geographical location. This allows the last-minute after-party venue arrangement department to select the optimal arrangement method based on the user's geographical location.

[0133] The last-minute after-party venue arrangement department analyzes the user's social media activity when arranging a venue and proposes arrangement methods. For example, the last-minute after-party venue arrangement department analyzes the user's social media activity and arranges venues of interest. For example, the last-minute after-party venue arrangement department arranges relevant gifts and prizes based on the user's social media activity. For example, the last-minute after-party venue arrangement department analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the department to propose arrangement methods based on the user's social media activity. Social media activity analysis can be done by, for example, using AI to analyze the user's posts, follower count, and engagement rate and propose the most suitable arrangement method. For example, the AI ​​can arrange relevant venues, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, the AI ​​can propose the most suitable mode of transportation based on the user's social media activity. This allows the last-minute after-party venue arrangement department to propose arrangement methods based on the user's social media activity.

[0134] The booking system, leveraging specific economic zones, estimates the user's emotions and selects an economic zone based on those emotions. For example, if the user is relaxed, it provides a detailed economic zone and offers customizable options. If the user is in a hurry, it provides a simple and fast booking zone. If the user is excited, it provides a visually appealing economic zone. This allows the system to select an economic zone according to the user's 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. The booking system, leveraging specific economic zones, can also estimate emotions by analyzing the user's facial expressions and voice. This allows the system to select an economic zone based on the user's emotions.

[0135] A booking department that leverages a specific economic zone selects the optimal booking method by referring to past economic zone data during the booking process. For example, a booking department that leverages a specific economic zone might propose the optimal booking method based on previously used economic zone data. Another example is extracting specific patterns from past economic zone data and proposing the optimal booking method. A booking department that leverages a specific economic zone might analyze past economic zone data and propose booking trends. This allows for the selection of the optimal booking method based on past economic zone data. Referencing past economic zone data could involve, for example, AI analyzing past economic zone data and proposing the optimal booking method. The AI ​​might select the optimal booking method considering factors such as the type of economic zone, success rate, and user evaluation. The AI ​​could also analyze and propose booking trends based on past economic zone data. This allows a booking department that leverages a specific economic zone to select the optimal booking method based on past economic zone data.

[0136] A booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation during the booking process. For example, when the user inputs their current situation, the booking system automatically displays the most suitable means of travel within that economic zone. For example, the booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation and efficiently collects necessary information. For example, the booking system that leverages a specific economic zone customizes the means of travel within that economic zone based on the user's current situation and makes optimal suggestions. This allows the means of travel within that economic zone to be customized according to the user's current situation. Customization of the means of travel within that economic zone can be done, for example, by having AI analyze the user's current situation and suggest the most suitable means of travel within that economic zone. The AI ​​customizes the means of travel within that economic zone, for example, by considering the user's location, current activity status, and urgency. The AI ​​can automatically display the means of travel within that economic zone based on the user's current situation and efficiently collect information. This allows the booking system that leverages a specific economic zone to customize the means of travel within that economic zone based on the user's current situation.

[0137] A service allocation system that leverages specific economic zones estimates the user's emotions and prioritizes economic zones based on those estimated emotions. For example, if the user is stressed, the system will prioritize selecting an economic zone and respond quickly. If the user is relaxed, the system will apply the normal allocation procedure. If the user is in a hurry, the system will prioritize selecting an economic zone and respond quickly. This allows the system to prioritize economic zones according to the user's 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. A service allocation system that leverages specific economic zones can also estimate emotions by analyzing the user's facial expressions and voice. This allows the system to prioritize economic zones based on the user's emotions.

[0138] A booking system that leverages specific economic zones selects the optimal economic zone when booking, taking into account the user's geographical location. For example, it selects a nearby economic zone based on the user's current location. For example, it arranges the optimal mode of transportation based on the user's geographical location. For example, it arranges relevant souvenirs or prizes based on the user's geographical location. This allows the system to select the optimal economic zone based on the user's geographical location. Considering geographical location involves, for example, an AI analyzing the user's current location and suggesting the optimal economic zone. The AI ​​identifies the user's current location using, for example, GPS data or location services. The AI ​​can arrange highly relevant economic zones, modes of transportation, souvenirs, or prizes based on the user's geographical location. This allows the booking system that leverages specific economic zones to select the optimal economic zone based on the user's geographical location.

[0139] A booking department that leverages specific economic zones analyzes the user's social media activity during the booking process and proposes means within that economic zone. For example, a booking department that leverages specific economic zones analyzes the user's social media activity and arranges within the economic zone of interest. For example, a booking department that leverages specific economic zones arranges relevant gifts or prizes based on the user's social media activity. For example, a booking department that leverages specific economic zones analyzes the user's social media activity and arranges the most suitable mode of transportation. This allows the booking department to propose means within an economic zone based on the user's social media activity. Social media activity analysis can be performed by, for example, AI analyzing the user's posts, follower count, and engagement rate to propose the most suitable booking method. For example, AI can arrange relevant economic zones, gifts, and prizes based on the user's areas of interest and behavioral patterns. For example, AI can propose the most suitable mode of transportation based on the user's social media activity. This allows the booking department that leverages specific economic zones to propose means within an economic zone based on the user's social media activity.

[0140] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0141] The reception desk can also estimate the user's emotions and adjust the way they input their budget and objectives based on that estimation. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be offered, and customizable input methods can be suggested. If the user is in a hurry, voice input can be prioritized, allowing them to quickly input their budget and objectives. This allows the system to adjust the way users input their budget and objectives according to their emotions.

[0142] The booking department can also analyze a user's past booking history and select the optimal booking method. For example, it can suggest the optimal booking method based on the booking methods the user has used in the past. It can also extract specific patterns from a user's past booking history and suggest the optimal booking method. It can also analyze a user's past booking history and suggest booking trends. This allows the system to select the optimal booking method based on the user's past booking history.

[0143] The planning department can also estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is relaxed, it can provide a detailed plan and suggest customizable options. If the user is in a hurry, it can provide a simple and easy-to-understand plan. If the user is excited, it can provide a visually appealing plan. This allows the plan's presentation to be adjusted according to the user's emotions.

[0144] The option presentation unit can also analyze the user's past selection history when presenting gift or prize options and suggest the most suitable choice. For example, it can suggest gifts or prizes that match the user's preferences based on their past selection history. It can also extract specific patterns from the user's past selection history and suggest the most suitable choice. It can also analyze the user's past selection history and suggest selection trends. This allows for the suggestion of the most suitable choice based on the user's past selection history.

[0145] The guidance system can also estimate the user's emotions and adjust the way guidance is displayed based on those emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. This allows the guidance to be adjusted according to the user's emotions.

[0146] The booking system can also select the most suitable booking method by considering the user's geographical location. For example, it can book nearby venues or restaurants based on the user's current location. It can also book the most suitable mode of transportation based on the user's geographical location. It can also book related gifts or prizes based on the user's geographical location. This allows the system to select the most suitable booking method based on the user's geographical location.

[0147] The reception desk can also analyze users' social media activity and make relevant suggestions. For example, it can analyze users' social media activity and suggest events or parties that might interest them. It can also suggest relevant gifts or prizes based on users' social media activity. It can analyze users' social media activity and suggest the most suitable mode of transportation. This allows for relevant suggestions based on users' social media activity.

[0148] The planning department can also adjust the level of detail in the plan based on the importance of the event. For example, for important events, a detailed plan can be provided, covering all arrangements. For general events, a basic plan can be provided, covering only the necessary arrangements. For simple events, a simple plan can be provided, covering minimal arrangements. This allows for adjusting the level of detail in the plan according to the importance of the event.

[0149] The option presentation section can also estimate the user's emotions and adjust how the options are displayed based on those emotions. For example, if the user is relaxed, it can provide detailed options and suggest customizable options. If the user is in a hurry, it can provide simple and quickly understandable options. If the user is excited, it can provide visually appealing options. This allows the display of options to be adjusted according to the user's emotions.

[0150] The booking system can also estimate the user's emotions and adjust the booking method based on those emotions. For example, if the user is relaxed, it can provide a detailed booking method and suggest customizable options. If the user is in a hurry, it can provide a simple and quick booking method. If the user is excited, it can provide a visually appealing booking method. This allows the booking method to be adjusted according to the user's emotions.

[0151] The following briefly describes the processing flow for example form 2.

[0152] Step 1: The reception desk allows the user to specify the budget and purpose. For example, the user can enter the type of event and the budget range, and the reception desk uses this information to determine the purpose and budget of the event. Step 2: The planning department creates the optimal plan based on the budget and objectives specified by the reception department. For example, the planning department can select the best venues and restaurants within the user's budget and automatically generate a plan that meets the user's wishes using AI. Step 3: The option presentation unit presents gift and prize options based on the plan created by the plan creation unit. For example, the option presentation unit can present multiple gift and prize options according to the user's budget and purpose, and can use AI to suggest gifts and prizes that suit the user's preferences. Step 4: The arrangement unit makes reservations and purchases based on the options presented by the option presentation unit. For example, the arrangement unit can purchase gifts or prizes selected by the user online, and can use AI to automatically make reservations and purchases based on the user's selections. Step 5: The information department provides guidance to participants based on the information confirmed by the booking department regarding reservations and purchases. For example, the information department can send guidance to participants via email or message, or it can use AI to automatically provide guidance to participants.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, plan creation unit, option presentation unit, arrangement unit, and guidance unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user specifies their budget and objectives. The plan creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an optimal plan is created based on the specified budget and objectives. The option presentation unit is implemented by, for example, the control unit 46A of the smart device 14, where options for gifts and prizes are presented. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where reservations and purchases are made based on the options. The guidance unit is implemented by, for example, the control unit 46A of the smart device 14, where information is provided to participants. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, plan creation unit, option presentation unit, arrangement unit, and guidance unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user specifies their budget and objectives. The plan creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an optimal plan is created based on the specified budget and objectives. The option presentation unit is implemented by, for example, the control unit 46A of the smart glasses 214, where options for gifts and prizes are presented. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where reservations and purchases are made based on the options. The guidance unit is implemented by, for example, the control unit 46A of the smart glasses 214, where guidance is provided to participants. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the reception unit, plan creation unit, option presentation unit, arrangement unit, and guidance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user specifies their budget and purpose. The plan creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an optimal plan is created based on the specified budget and purpose. The option presentation unit is implemented by, for example, the control unit 46A of the headset terminal 314, where options for gifts and prizes are presented. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where reservations and purchases are made based on the options. The guidance unit is implemented by, for example, the control unit 46A of the headset terminal 314, where information is provided to participants. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0189] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Each of the multiple elements described above, including the reception unit, plan creation unit, option presentation unit, arrangement unit, and guidance unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user specifies the budget and purpose. The plan creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where an optimal plan is created based on the specified budget and purpose. The option presentation unit is implemented by, for example, the control unit 46A of the robot 414, where options for gifts and prizes are presented. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where reservations and purchases are made based on the options. The guidance unit is implemented by, for example, the control unit 46A of the robot 414, where guidance is provided to participants. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] (Note 1) The reception desk where you specify the budget and purpose, A planning department that creates the optimal plan based on the budget and objectives specified by the aforementioned reception department, A selection presentation unit presents options for gifts and prizes based on the plan created by the aforementioned plan creation unit, A reservation and purchase arrangement unit that makes reservations and purchases based on the options presented by the aforementioned option presentation unit, The system includes a guidance unit that provides information to participants based on the reservation and purchase information completed by the aforementioned arrangement unit. A system characterized by the following features. (Note 2) The aforementioned ordering unit, It is equipped with a voice generation unit that uses AI to generate voice messages for reservations when telephone reservations are the only option. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned plan creation unit, We need to arrange a venue for the after-party on short notice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned option presentation unit, Offer options for gifts or prizes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is Provide information to participants and prospective participants. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned ordering unit, Complete the arrangements by leveraging a specific economic zone. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how budgets and objectives are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past event history to propose the optimal budget and objectives. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is At registration, input fields are customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, we will make highly relevant suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During registration, we analyze the user's social media activity and provide relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan creation unit, It estimates the user's emotions and adjusts how the plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan creation unit, When creating a plan, adjust the level of detail based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned plan creation unit, When creating a plan, different plan creation algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned plan creation unit, It estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned plan creation unit, When creating a plan, prioritize the plan based on the timing of the events. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned plan creation unit, When creating a plan, adjust the order of events based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned option presentation unit, It estimates the user's emotions and adjusts how options are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned option presentation unit, When presenting options, adjust the level of detail of the options based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned option presentation unit, When presenting options, different option presentation algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned option presentation unit, It estimates the user's emotions and adjusts the length of the options based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned option presentation unit, When presenting options, prioritize them based on when the items will be available. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned option presentation unit, When presenting options, adjust the order of the options based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ordering unit, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ordering unit, When making arrangements, the system analyzes the user's past arrangement history to select the most suitable arrangement method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ordering unit, When making a reservation, customize the reservation method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ordering unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ordering unit, When making arrangements, the optimal arrangement method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned ordering unit, When making arrangements, we analyze the user's social media activity and suggest arrangement methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned guide section is The system estimates the user's emotions and adjusts how the guidance is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned guide section is When providing guidance, the system selects the most suitable guidance method by referring to the user's past guidance history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned guide section is When providing guidance, customize the guidance method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned guide section is When providing directions, the system selects the most suitable guidance method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned guide section is When providing guidance, we analyze the user's social media activity and suggest guidance methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned speech generation unit, It estimates the user's emotions and adjusts the tone and speed of the voice based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned speech generation unit, When generating audio, adjust the level of detail in the audio according to the reservation details. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned speech generation unit, When generating speech, different speech generation algorithms are applied depending on the reservation category. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned speech generation unit, It estimates the user's emotions and adjusts the length of the audio based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned speech generation unit, When generating speech, the priority of speech is determined based on the reservation date. The system according to appendix 2, characterized in that (Appendix 42) The voice generation unit adjusts the order of voices based on the relevance of the reservation when generating voices The system according to appendix 2, characterized in that (Appendix 43) The venue arrangement unit for the urgent second meeting estimates the user's emotion and adjusts the venue arrangement method based on the estimated user's emotion The system according to appendix 3, characterized in that (Appendix 44) The venue arrangement unit for the urgent second meeting analyzes the user's past venue arrangement history to select an optimal arrangement method when arranging the venue The system according to appendix 3, characterized in that (Appendix 45) The venue arrangement unit for the urgent second meeting customizes the arrangement means based on the user's current situation when arranging the venue The system according to appendix 3, characterized in that (Appendix 46) The venue arrangement unit for the urgent second meeting estimates the user's emotion and determines the priority of the venue arrangement based on the estimated user's emotion The system according to appendix 3, characterized in that <​​​​​​​​​​​​​​​​​​​​It estimates user emotions and selects economic zones based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 50) The arrangement department, which utilizes the aforementioned specific economic zone, When making arrangements, the optimal arrangement method is selected by referring to past economic zone data. The system described in Appendix 6, characterized by the features described herein. (Note 51) The arrangement department, which utilizes the aforementioned specific economic zone, During the booking process, the economic sphere's means are customized based on the user's current situation. The system described in Appendix 6, characterized by the features described herein. (Note 52) The arrangement department, which utilizes the aforementioned specific economic zone, It estimates user sentiment and determines economic priorities based on the estimated user sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 53) The arrangement department, which utilizes the aforementioned specific economic zone, When making arrangements, the optimal economic zone is selected considering the user's geographical location. The system described in Appendix 6, characterized by the features described herein. (Note 54) The arrangement department, which utilizes the aforementioned specific economic zone, During the setup process, we analyze the user's social media activity and propose economic strategies. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0225] 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. The reception desk where you specify the budget and purpose, A planning department that creates the optimal plan based on the budget and objectives specified by the aforementioned reception department, A selection presentation unit presents options for gifts and prizes based on the plan created by the aforementioned plan creation unit, A reservation and purchase arrangement unit that makes reservations and purchases based on the options presented by the aforementioned option presentation unit, The system includes a guidance unit that provides information to participants based on the reservation and purchase information completed by the aforementioned arrangement unit. A system characterized by the following features.

2. The aforementioned procurement unit, In cases where telephone reservations are the only option, the system includes a voice generation unit that uses AI to generate the reservation voice. The system according to feature 1.

3. The aforementioned plan creation unit, We need to arrange a venue for the after-party on short notice. The system according to feature 1.

4. The aforementioned option presentation unit, Offer options for gifts or prizes. The system according to feature 1.

5. The aforementioned guide section is Provide information to participants and prospective participants. The system according to feature 1.

6. The aforementioned procurement unit, Complete the arrangements by leveraging a specific economic zone. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how budgets and objectives are entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past event history to propose the optimal budget and objectives. The system according to feature 1.

9. The aforementioned reception unit is At registration, input fields are customized based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the reception process based on those estimated emotions. The system according to feature 1.

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