system

Abstract

Provide a system. 【Solution means】 Means for generating optimal visit candidates based on the preference data of participants, Means for presenting the list of generated visit candidates and automatically making a reservation to the selected location, Means for sending event participation confirmation to members via a communication network, Means for selecting the date on which the most members can participate based on the response to the participation confirmation, Means for automatically calculating the expenditure amount for each member and notifying the member, Means for generating location candidates according to preferences using prompts, Means for making a settlement through the platform after selection, A system including the above.

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, including 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 conventional management process of events and gatherings, the organizer has to manually select facilities according to the preferences of participants, confirm attendance, adjust schedules, and allocate costs, which places an excessive burden on the organizer. In addition, the failure to select facilities based on the appropriate preferences of participants is a factor that reduces the satisfaction of participants. Furthermore, since it is difficult to fairly allocate costs, there is a possibility of generating a sense of unfairness among participants.

Means for Solving the Problems

[0005] This invention relates to an event management system that includes means for generating optimal venue candidates based on participant preference data and automatically making reservations at the selected venues. Furthermore, it includes means for sending attendance confirmations to participants via a communication network and means for selecting the date and time that is most available to the most participants based on those responses. In addition, by including means for automatically calculating and notifying participants of their expenses, the system reduces the burden on organizers and improves participant satisfaction.

[0006] "Participant preference data" refers to data that represents individual preferences and interests based on the past participation history and preference information of participants.

[0007] A "potential venue" refers to a list of suitable locations or shops for the event, suggested based on the participants' preferences and requests.

[0008] "Attendance confirmation" is a process of asking participants whether they will be able to attend an event and collecting their responses.

[0009] A "communication network" is a system built for sending and receiving digital data, and includes the internet and dedicated networks.

[0010] An "artificial intelligence model" is a computational model that learns patterns and knowledge from data to make predictions and judgments, and it is a technology that uses machine learning algorithms.

[0011] "Expense settlement" is the process of fairly dividing the total costs of an event among the participants and calculating the amount each participant should pay. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention specifically describes a management system for users to efficiently plan and manage events. In this system, the roles of servers, terminals, and users are important elements.

[0034] First, users access this system for the next event and enter basic event information and participant details. This information is sent to the server and processed by the system.

[0035] The server uses an artificial intelligence model to generate optimal facility suggestions based on participants' past participation history and preference data. These facility suggestions are customized according to the type of event and the participants' preferences. For example, if a participant has a history of liking Japanese food, Japanese restaurants will be prioritized in the list. The generated facility suggestions are sent to the user's terminal via the server.

[0036] The terminal presents the user with a list of available facilities, and the user selects their preferred facility from the list. Once the user selects a facility, the server automatically makes a reservation for that facility. The reservation status is notified to the user in real time.

[0037] Next, when scheduling the event, the user enters several possible dates into the system. The server sends an attendance confirmation message to all participants via the communication network. Participants reply to the server using their devices to indicate whether they can attend or not.

[0038] The server analyzes attendance responses from participants and selects the optimal date that allows the most participants to attend. This determined date is then notified to all participants, and the schedule is finalized.

[0039] After the event, users enter the actual expenses incurred into the system. The server automatically calculates the payment amount for each participant based on the entered expenses and notifies each participant. Each participant can check their payment amount and settle their account via their terminal.

[0040] This system significantly reduces the workload of event organizers and improves the satisfaction of all participants. Furthermore, it enables real-time information updates and efficient processing, ensuring smooth event management.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users access the system and enter basic event information and participant lists. This sends the necessary data to the server.

[0044] Step 2:

[0045] The server analyzes past participation history and preference data based on the information received from participants. Using an artificial intelligence model, it generates a list of candidate facilities that are most suitable for the participant.

[0046] Step 3:

[0047] The server sends a list of generated potential facilities to the terminal. The terminal presents this list to the user, who then selects the desired facility from the presented list.

[0048] Step 4:

[0049] When a user selects a facility, the server automatically makes a reservation for that facility. This reservation status is notified to the user in real time via their device.

[0050] Step 5:

[0051] The user enters suggested dates and times for the event into the system. Based on this, the server sends attendance confirmation messages to participants via the communication network.

[0052] Step 6:

[0053] The terminal sends attendance responses from participants to the server. The server analyzes these responses and selects the date that is available to the most participants.

[0054] Step 7:

[0055] The server notifies all participants of the confirmed optimal date and determines the event schedule. This information is provided to participants in real time via their devices.

[0056] Step 8:

[0057] After the event ends, users enter the total actual cost into the system. The server calculates the payment amount for each participant based on the entered costs and notifies the participants via their terminals.

[0058] Step 9:

[0059] Participants use their devices to check their payment amounts and settle their expenses appropriately. This entire process significantly streamlines the user's event management tasks.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Events with a large number of participants require significant time and effort to plan and manage, making it particularly difficult to select a venue that matches participants' preferences and to adjust the schedule to the optimal level. Furthermore, expense settlement presents challenges such as manual calculation errors and failure to notify participants.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for generating optimal options based on participant preference information, means for automatically making reservations for confirmed options from the generated options, and means for transmitting confirmation of participation in the event to members via information communication. This makes it possible to improve the efficiency of event planning and operation and increase the satisfaction of all participants.

[0065] "Participants" refers to individuals or groups who are planning to participate in an event or occurrence.

[0066] "Preference information" refers to data based on participants' preferences, interests, and past selection history, and is information that reflects individual preferences.

[0067] "Options" refers to selectable items such as venues and dates that participants can choose from at an event.

[0068] A "reservation" is a procedure that secures the use of designated facilities or services in advance, based on the options selected by the participant.

[0069] "Information and communication" refers to the technology or protocol used to send and receive data over a digital network.

[0070] A "member" refers to a person who belongs to a specific group or organization and participates in its activities.

[0071] "Expense calculation" is the process of determining the amount each participant should bear based on the expenses incurred.

[0072] This invention is a system for streamlining event planning and management, in which servers, terminals, and users cooperate with each other to realize its functions.

[0073] Users access the system from their devices and send basic event information and participant details to the server via prompt messages. By using specific prompts such as, "I want to plan our next company trip. Please suggest the best accommodation and meal plan considering the participants' preferences," the server utilizes participants' past event participation history and preference information, and uses a generative AI model to list the best options.

[0074] The server generates optimal choices based on participant data and then sends them to the user's device. This process utilizes machine learning algorithms, such as generative AI models, and the system reflects the participant's preferences, resulting in more personalized choices. For example, if data shows a past preference for Japanese food, Japanese restaurants will be listed as recommended options.

[0075] The user selects their desired option from a list presented via their device, and this selection is sent to the server, automatically processing the reservation. The reservation status is notified to the user in real time and can be checked on their device.

[0076] Furthermore, the server receives schedule input from users, sends participation confirmations to participants via the information and communication network, and determines the optimal schedule based on the responses. Automating this process reduces the workload for users and ensures optimal scheduling.

[0077] Furthermore, after the event, users enter their expenses, and the server automatically calculates the cost for each participant. This allows each participant to easily check and settle their expenses from their own device, streamlining the settlement process.

[0078] In this way, the entire process from event planning to execution and settlement is efficiently managed, resulting in smoother operation.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user accesses the system using a terminal and enters basic event information and participant details. The entered data is sent to the server via prompts. This input includes the event name, proposed dates, and participant list. Based on this information, the server prepares for the next steps.

[0082] Step 2:

[0083] The server analyzes the received event information and participant data, and uses a generative AI model to generate optimal facility and schedule options. This process utilizes past participation history and preference information to process the data and create a list of candidates tailored to the participant's preferences. This candidate list is then sent from the server to the user's terminal as available options.

[0084] Step 3:

[0085] The terminal presents the user with a list of available facilities and dates received from the server. The user selects their preferred option from the presented list. Based on this selection, the terminal sends the user's intended reservation details to the server. This then enables the next reservation process.

[0086] Step 4:

[0087] The server automatically makes reservations for facilities and dates based on the user's selections. Once the reservation process is complete, the results are notified to the user's device in real time. The server generates data for reservation confirmation and confirms the reservation in the relevant system.

[0088] Step 5:

[0089] The user enters potential event dates into the server, which then receives them. The server sends confirmation of participation to all participants via the information and communication network. This confirmation includes a message asking about their availability for multiple dates.

[0090] Step 6:

[0091] Participants open the participation confirmation message they receive on their device, select whether they can participate or not, and reply to the server. This response data is compiled by the server and used to automatically select the most suitable date for participation.

[0092] Step 7:

[0093] The server selects the most suitable date based on the collected participant responses and notifies all participants of the selection result. This notification includes the confirmed date, and participants can add the event to their own schedules.

[0094] Step 8:

[0095] After the event ends, users enter the actual costs incurred into the system via their terminals, and the server receives and analyzes this data. The server calculates each participant's cost and sends a notification to each participant. This calculation uses data such as the total cost and the number of participants.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In planning and managing events, there are challenges in efficiently coordinating with participants' diverse preferences and schedules, as well as settling expenses. Furthermore, selecting destinations that satisfy all participants, and ensuring a smooth booking and payment process are essential.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for generating optimal visit candidates based on participant preference data, means for generating location candidates tailored to preferences using prompts, and means for processing payments through the platform after selection. This enables efficient event management that improves participant satisfaction.

[0101] "Participant preference data" refers to information collected based on each member's past activity history and preferences, and serves as the basis for suggesting the most suitable destinations.

[0102] "Possible Visits" is a list of locations and places that participants may visit during the event, generated based on their preference data.

[0103] "Generative intelligence technology" is an artificial intelligence technology used to analyze large amounts of data to understand participants' preferences and patterns, and then make suggestions.

[0104] A "reservation" is the process of securing a seat, time, and other details in advance for a chosen place to visit.

[0105] "Participation confirmation" is a communication to confirm whether a member is able to attend an event on a specific date.

[0106] "Expenses" refers to the calculated cost that each member will be responsible for in connection with the event.

[0107] A "platform" is a digital environment where users can access information and manage event schedules and payment procedures.

[0108] The system of this invention has a configuration for streamlining event planning and management. The server manages participant preference data and uses generative intelligence technology to analyze the data based on past activity history. This makes it possible to generate visitor suggestions tailored to individual preferences.

[0109] In this system, users input event information using a terminal, present specific requests through prompts, and receive results based on those requests. The terminal is developed using React Native and is cross-platform compatible. It can be operated from smartphones and smart glasses.

[0110] The server manages data using AWS® Lambda and DynamoDB, and performs real-time information processing. Generative intelligence technology utilizes Google® Cloud AI to analyze user preferences through prompts and provide results. Facility reservations and payment processing are handled through a dedicated platform. This enables smooth and efficient reservations and payments after selection.

[0111] For example, if a user enters a prompt such as, "I'd like to have a lunch gathering at an organic restaurant this weekend," the server generates a list of suitable restaurants based on the user's past visit history and preferences, and notifies the user's device in real time. The user can then immediately complete a reservation through the platform.

[0112] Furthermore, to coordinate everyone's schedules, the server has a system that checks attendance via the network and selects the date that is most likely to attract participants. After the event, individual expenses are automatically calculated and notified to each participant. This reduces the burden on the organizer (the user) and improves participant satisfaction.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] Users use their devices to enter basic event information and participant details. The entered information is sent to the server. The server receives the information, verifies its integrity, and stores it in its database.

[0116] Step 2:

[0117] The server collects participants' past activity history and preference data, and uses a generative AI model to generate prompt-based visit suggestions. The server invokes the AI ​​model to identify and list potential locations that match the participants' preferences from that data. Data filtering and ranking are performed during the process of generating this list.

[0118] Step 3:

[0119] The terminal receives a list of potential destinations sent from the server and presents it to the user. Once the user makes a selection, the terminal sends that selection information to the server. The server automatically starts the reservation process for the selected destinations and completes the actual reservation by calling an external reservation system.

[0120] Step 4:

[0121] The server sends real-time confirmation messages to all participants based on the candidate dates set by the user. Participants select whether they can attend or not on their respective devices and reply to the server with that information. Based on these responses, the server selects the optimal date and notifies all participants.

[0122] Step 5:

[0123] After the event ends, users enter the actual expenses incurred into the server via their devices. The server uses this expense information to automatically calculate each participant's expenditure and sends a notification to each of them. Participants receive the notification via their devices and complete the payment through the designated platform.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention describes a specific embodiment of an event management system that combines an emotion engine for recognizing user emotions and reflecting them in the plan. In this system, server, terminal, and user interaction play important roles.

[0126] Specifically, when a user plans an event, they input basic information and a list of participants into the system via a terminal. At this stage, the terminal's emotion engine measures the user's emotional state through user input behavior, voice, and facial expression analysis. For example, speech recognition technology is used to analyze the user's tone of voice and speaking style to estimate their emotional state.

[0127] The server combines emotional data obtained from the emotion engine with preference data based on the participant's past participation history. Based on this data, an artificial intelligence model is prepared to generate optimal candidate facilities for visits. For example, if a user is feeling stressed, the system will prioritize suggesting restaurants and facilities with a relaxing atmosphere.

[0128] Next, the server sends a generated list of potential visiting facilities to the terminal and presents it to the user. The user can then select their desired facility from the presented list. Based on this selection, the server automatically makes a reservation for the facility and notifies the user of the result in real time.

[0129] During event scheduling, the server continues to send attendance confirmation messages to participants via the communication network. Participant responses arrive at the server via their devices. Here again, the server analyzes the responses, and the emotion engine is incorporated into the process of selecting the optimal date based on the user's emotional state.

[0130] After the event ends, users enter the incurred expenses into the system, and the server calculates and notifies each participant of their individual expenses. An emotion engine may also be used to consider how to distribute expenses in a way that maximizes participant satisfaction.

[0131] This system allows users not only to improve operational efficiency but also to plan and manage more satisfying events that take emotional aspects into consideration. Real-time planning tailored to the individual emotions of each user is achieved.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user logs into the system and enters information about a new event. During this process, the terminal is equipped with a function to analyze the user's voice and facial expressions, and the emotion engine detects the user's emotional state.

[0135] Step 2:

[0136] The device sends detected emotion data to the server. The server combines the emotion data with the participant's preference data and uses an artificial intelligence model to generate optimal list of places to visit. For example, if the user is seeking relaxation, the server will create a list that includes quiet cafes and relaxation facilities.

[0137] Step 3:

[0138] The server sends the generated list of potential facilities to the terminal, which then presents this list to the user. The user then selects the desired facilities from the presented list.

[0139] Step 4:

[0140] When a user selects a facility to visit, the server automatically makes a reservation for that facility. This reservation information is then notified to the user via their device.

[0141] Step 5:

[0142] The user enters the dates of potential events into the server. The server then sends attendance confirmation messages to all participants via the communication network.

[0143] Step 6:

[0144] Participants respond to attendance requests using their devices, and this information is sent to the server. Based on the returned information, the server uses an emotion engine to select the optimal date. Scheduling is adjusted to reduce stress for users and participants.

[0145] Step 7:

[0146] After the event ends, users enter their actual expenses into the system. Based on this data, the server automatically calculates each participant's payment amount, considers appropriate expense distribution based on the emotion engine, and notifies the user's terminal. For example, it may be possible to provide additional consideration as a token of appreciation.

[0147] Step 8:

[0148] Participants review the notified fees and settle the payment. The server reports the final settlement status to the user via their terminal. This completes the entire process of the emotionally sensitive event.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, event planning requires considering a great deal of information and factors, and making choices that reflect participants' emotions and preferences is particularly difficult. Furthermore, efficiently managing participant attendance and calculating cost sharing is crucial, but existing systems have placed a heavy burden on users. This invention aims to resolve these problems and enable more user-friendly and emotionally resonant event management.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for generating optimal recommendation candidates based on participant preference information and user emotional information, means for measuring the emotional state using user input behavior, voice, and facial expression analysis, and means for transmitting attendance confirmations for the plan to relevant parties via an information network. This enables the integration of more data, the proposal of plans that match the user's needs and emotions, and efficient and highly satisfying event management.

[0154] "Preference information" refers to information that indicates a participant's past activity history and personal preferences.

[0155] "Emotional information" refers to information that indicates the user's emotional state, obtained from user input behavior, voice, and facial expression analysis.

[0156] A "recommended list" is a list of suggested facilities and services that meet specific criteria, generated based on participants' preference information and users' emotional information.

[0157] "Attendance confirmation" is the procedure for confirming participants' intention to participate in planned activities or events.

[0158] An "information network" is a network system that connects computers and other devices for the purpose of transmitting data and information.

[0159] "Cost sharing" refers to calculating and distributing the costs associated with a specific activity or event to each participant.

[0160] The system of this invention is an advanced event management technology realized through collaboration between users, terminals, and servers via a computer network. In particular, it specializes in a function that integrates emotional data and preference data to propose the optimal event plan to the user.

[0161] The terminal provides an input interface for users to plan events. Users can enter basic event information and participant lists via the terminal. For example, tablets and smartphones are used, and the entered data is transmitted to the server in real time.

[0162] In acquiring emotional information, the device utilizes facial recognition and voice analysis technologies. Specifically, it uses "voice recognition technology" and "facial analysis software" to analyze the user's voice tone and facial expressions and estimate their emotional state. For example, if the user feels like they want to relax, that information is sent to the server as emotional data.

[0163] The server integrates emotional information received from the terminal and preference data obtained from the user's past participation history. The server implements a "machine learning model" and generates recommended facilities suitable for the user based on this data. A machine learning platform (e.g., "any AI platform") can be used in this process. The generated facility candidates are presented to the user via the terminal, and the user selects their preference from among them.

[0164] For example, if you input a prompt such as "Please suggest a suitable facility for when I want to relax" into a generative AI model, the AI ​​can generate candidates such as spas and parks and present them to the user via the server.

[0165] In this way, users can plan events that match their own emotions and preferences via their devices. This system allows users to hold more satisfying events.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users use a terminal to input basic event information and participant lists. Specifically, users enter information such as the event name, date and time, location, and participant email addresses into the terminal. This input data is sent to the server in real time. The entered data is stored in a database as basic information for event planning.

[0169] Step 2:

[0170] The device analyzes the user's facial expressions and voice to acquire emotional information. The device is equipped with a camera and microphone, and the resulting video and audio data is processed. Specifically, voice analysis software is used to detect the tone of voice, and facial recognition technology is used to analyze the user's facial expressions. As a result, the user's emotional state is quantified and sent to the server.

[0171] Step 3:

[0172] The server aggregates emotional information received from terminals and preference information obtained from participants' past participation history. The server receives this data as input and queries the database to extract participants' preferences. The obtained preference information is integrated with the user's emotional information and used as necessary information for event planning.

[0173] Step 4:

[0174] The server uses a machine learning model to generate optimal recommendation candidates. In this process, the server inputs integrated preference and sentiment information as prompts to the AI ​​model. For example, it might give the model the command "Recommend relaxing facilities," and the AI ​​generates a list of recommended facilities as output.

[0175] Step 5:

[0176] The server sends a list of recommended facilities back to the terminal and presents it to the user. The user selects a desired facility from this list via the terminal's interface. The selected facility information is sent to the server and recorded.

[0177] Step 6:

[0178] The server automatically makes facility reservations based on the user's selection. The server calls the API of an external reservation system, using the user's name and desired date and time as input, and confirms the reservation. After completion, reservation confirmation information is generated as output and notified to the user.

[0179] Step 7:

[0180] The server sends attendance confirmation messages to participants via the information network. These messages include event details and a link to ask about attendance, to which participants respond. The response data is sent to the server, where attendance information from participants is collected.

[0181] Step 8:

[0182] The server analyzes the collected attendance information and selects the optimal date. The analysis calculates the date that allows the most participants to attend, based on the attendance response data. The calculated optimal date is then finalized as output and notified to users and participants.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In modern event management, the focus is not simply on scheduling participants' schedules, but also on providing a more satisfying service that takes into account their emotional state. Furthermore, there is a challenge to improve the quality of daily life through appropriate, real-time suggestions that consider emotions in both home and public settings.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for generating candidate optimal visiting locations based on participants' preference information, means for analyzing residents' emotional states in real time and generating appropriate suggestions based on that analysis, and means for suggesting appropriate activities or relaxation methods to residents based on the emotional analysis. This makes it possible to provide individually optimized services based on emotional states.

[0188] "Participant preference information" refers to data collected based on users' past behavior and preferences, and serves as the basis for presenting specific options and candidates.

[0189] The "optimal place to visit" is a candidate location that is predicted to provide the highest level of satisfaction for the user, taking into account the participant's preferences and emotional state.

[0190] "Automatic booking" refers to a process where, based on user selection, the system completes online bookings without requiring manual intervention.

[0191] "Event attendance confirmation" refers to the act of contacting participants via email or notification to inquire about their availability to attend a scheduled event.

[0192] "Communication infrastructure" refers to the entire network infrastructure that enables the transmission and reception of data, and includes the internet and local networks.

[0193] "Real-time analysis" refers to the ability to perform analysis and processing immediately at the moment data is generated.

[0194] "Appropriate activities or relaxation methods" refer to actions or methods recommended to promote the user's mental and physical health at that particular time, based on their emotional state.

[0195] "Automatically calculating expense settlements" refers to the process where the system automatically calculates and organizes the financial burdens that users are responsible for, and then notifies the participants of the results.

[0196] The system for realizing this invention consists of a server, an emotion recognition engine, and a terminal. The server processes participant preference information and emotion data acquired in real time, and suggests the optimal place to visit according to the participant's emotional state. The terminal has the function of receiving input from the user and displaying output from the server. The user can input necessary information via the terminal and receive suggestions from the terminal, thereby adjusting events based on emotions.

[0197] The specific technologies used in this system include emotion recognition libraries, natural language processing libraries, and machine learning frameworks. For example, the Microsoft® Azure® Emotion API is used for emotion recognition, acquiring emotion data from the user's facial expressions and voice. The Google Cloud Natural Language API is also used to analyze the user's text input and generate appropriate suggestions. This allows for the presentation of appropriate activities and relaxation methods based on the user's emotions.

[0198] As a concrete example, let's consider a scenario where a user who is tired after work uses this system. The server would use an emotion recognition engine to understand the user's level of fatigue and make a suggestion such as, "You must be tired. Shall we play a recommended music list to help you relax?"

[0199] Examples of prompts to input into a generative AI model include the following:

[0200] "Analyze the user's emotional state and generate suggestions for stress relief."

[0201] "Design an algorithm that suggests music and activities that help users relax."

[0202] In this way, it becomes possible to provide specific services that are tailored to the user's emotional state.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server receives basic participant information, a participant list, and initial emotional state data from the user's terminal. This input data includes audio files and facial expression images. The server analyzes this data using an emotion recognition library (e.g., Microsoft Azure Emotion API) and outputs the user's emotional state as numerical data.

[0206] Step 2:

[0207] The server integrates the analyzed sentiment data with past participant preference information and uses a generative AI model to generate candidate optimal visiting locations. This model receives two inputs: sentiment data and preference information, and its output is a list of candidate locations.

[0208] Step 3:

[0209] The user receives a list of suggested locations to visit from the server on their device. The user then uses an intuitive interface to select a location from the list and sends that selection information to the server.

[0210] Step 4:

[0211] The server receives the user's location selection and automatically completes the reservation via the internet. The location and date / time to be reserved are determined based on the selection information and confirmed through the online reservation system.

[0212] Step 5:

[0213] The server continuously monitors the user's emotional state and suggests relaxation methods in real time. It continuously analyzes emotional data using an emotion recognition engine and, if necessary, sends automatically generated suggestions to the user's device using the Google Cloud Natural Language API.

[0214] Step 6:

[0215] Users review suggested activities and relaxation methods on their devices and select actions based on them. This information is also sent back to the server and stored as feedback. This data is used to improve the accuracy of future suggestions.

[0216] 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.

[0217] Data generation model 58 is a type 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] 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.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] 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.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention specifically describes a management system for users to efficiently plan and manage events. In this system, the roles of servers, terminals, and users are important elements.

[0233] First, users access this system for the next event and enter basic event information and participant details. This information is sent to the server and processed by the system.

[0234] The server uses an artificial intelligence model to generate optimal facility suggestions based on participants' past participation history and preference data. These facility suggestions are customized according to the type of event and the participants' preferences. For example, if a participant has a history of liking Japanese food, Japanese restaurants will be prioritized in the list. The generated facility suggestions are sent to the user's terminal via the server.

[0235] The terminal presents the user with a list of available facilities, and the user selects their preferred facility from the list. Once the user selects a facility, the server automatically makes a reservation for that facility. The reservation status is notified to the user in real time.

[0236] Next, when scheduling the event, the user enters several possible dates into the system. The server sends an attendance confirmation message to all participants via the communication network. Participants reply to the server using their devices to indicate whether they can attend or not.

[0237] The server analyzes attendance responses from participants and selects the optimal date that allows the most participants to attend. This determined date is then notified to all participants, and the schedule is finalized.

[0238] After the event, users enter the actual expenses incurred into the system. The server automatically calculates the payment amount for each participant based on the entered expenses and notifies each participant. Each participant can check their payment amount and settle their account via their terminal.

[0239] This system significantly reduces the workload of event organizers and improves the satisfaction of all participants. Furthermore, it enables real-time information updates and efficient processing, ensuring smooth event management.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Users access the system and enter basic event information and participant lists. This sends the necessary data to the server.

[0243] Step 2:

[0244] The server analyzes past participation history and preference data based on the information received from participants. Using an artificial intelligence model, it generates a list of candidate facilities that are most suitable for the participant.

[0245] Step 3:

[0246] The server sends a list of generated potential facilities to the terminal. The terminal presents this list to the user, who then selects the desired facility from the presented list.

[0247] Step 4:

[0248] When a user selects a facility, the server automatically makes a reservation for that facility. This reservation status is notified to the user in real time via their device.

[0249] Step 5:

[0250] The user enters suggested dates and times for the event into the system. Based on this, the server sends attendance confirmation messages to participants via the communication network.

[0251] Step 6:

[0252] The terminal sends attendance responses from participants to the server. The server analyzes these responses and selects the date that is available to the most participants.

[0253] Step 7:

[0254] The server notifies all participants of the confirmed optimal date and determines the event schedule. This information is provided to participants in real time via their devices.

[0255] Step 8:

[0256] After the event ends, users enter the total actual cost into the system. The server calculates the payment amount for each participant based on the entered costs and notifies the participants via their terminals.

[0257] Step 9:

[0258] Participants use their devices to check their payment amounts and settle their expenses appropriately. This entire process significantly streamlines the user's event management tasks.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Events with a large number of participants require significant time and effort to plan and manage, making it particularly difficult to select a venue that matches participants' preferences and to adjust the schedule to the optimal level. Furthermore, expense settlement presents challenges such as manual calculation errors and failure to notify participants.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes means for generating optimal options based on participant preference information, means for automatically making reservations for confirmed options from the generated options, and means for transmitting confirmation of participation in the event to members via information communication. This makes it possible to improve the efficiency of event planning and operation and increase the satisfaction of all participants.

[0264] "Participants" refers to individuals or groups who are planning to participate in an event or occurrence.

[0265] "Preference information" refers to data based on participants' preferences, interests, and past selection history, and is information that reflects individual preferences.

[0266] "Options" refers to selectable items such as venues and dates that participants can choose from at an event.

[0267] A "reservation" is a procedure that secures the use of designated facilities or services in advance, based on the options selected by the participant.

[0268] "Information and communication" refers to the technology or protocol used to send and receive data over a digital network.

[0269] A "member" refers to a person who belongs to a specific group or organization and participates in its activities.

[0270] "Expense calculation" is the process of determining the amount each participant should bear based on the expenses incurred.

[0271] This invention is a system for streamlining event planning and management, in which servers, terminals, and users cooperate with each other to realize its functions.

[0272] Users access the system from their devices and send basic event information and participant details to the server via prompt messages. By using specific prompts such as, "I want to plan our next company trip. Please suggest the best accommodation and meal plan considering the participants' preferences," the server utilizes participants' past event participation history and preference information, and uses a generative AI model to list the best options.

[0273] The server generates optimal choices based on participant data and then sends them to the user's device. This process utilizes machine learning algorithms, such as generative AI models, and the system reflects the participant's preferences, resulting in more personalized choices. For example, if data shows a past preference for Japanese food, Japanese restaurants will be listed as recommended options.

[0274] The user selects their desired option from a list presented via their device, and this selection is sent to the server, automatically processing the reservation. The reservation status is notified to the user in real time and can be checked on their device.

[0275] Furthermore, the server receives schedule input from users, sends participation confirmations to participants via the information and communication network, and determines the optimal schedule based on the responses. Automating this process reduces the workload for users and ensures optimal scheduling.

[0276] Furthermore, after the event, users enter their expenses, and the server automatically calculates the cost for each participant. This allows each participant to easily check and settle their expenses from their own device, streamlining the settlement process.

[0277] In this way, the entire process from event planning to execution and settlement is efficiently managed, resulting in smoother operation.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user accesses the system using a terminal and enters basic event information and participant details. The entered data is sent to the server via prompts. This input includes the event name, proposed dates, and participant list. Based on this information, the server prepares for the next steps.

[0281] Step 2:

[0282] The server analyzes the received event information and participant data, and uses the generated AI model to generate optimal facility and schedule options. In this process, past participation history and preference information are utilized to process the data and create a candidate list according to the preferences of the participants. This candidate list is sent from the server to the terminal as options available to the user.

[0283] Step 3:

[0284] The terminal presents the facility and schedule candidate list received from the server to the user. The user selects the desired options from the presented list. Based on this selection, the terminal sends the reservation details intended by the user to the server. This enables the next reservation process.

[0285] Step 4:

[0286] The server automatically executes the reservation of the facility and schedule based on the selection information from the user. When the reservation process is completed, the result is notified to the user's terminal in real time. The server generates data for reservation confirmation and confirms the reservation in the relevant system.

[0287] Step 5:

[0288] The user inputs the event schedule candidates to the server, and the server receives them. The server sends participation confirmation to all participants through the information communication network. This transmission includes messages asking about the availability of multiple schedules.

[0289] Step 6:

[0290] The participants open the participation confirmation message received on their terminals, select whether to participate, and reply to the server. This response data is aggregated by the server and used as material for automatically selecting the most available schedule.

[0291] Step 7:

[0292] The server selects the most suitable date based on the collected participant responses and notifies all participants of the selection result. This notification includes the confirmed date, and participants can add the event to their own schedules.

[0293] Step 8:

[0294] After the event ends, users enter the actual costs incurred into the system via their terminals, and the server receives and analyzes this data. The server calculates each participant's cost and sends a notification to each participant. This calculation uses data such as the total cost and the number of participants.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] In planning and managing events, there are challenges in efficiently coordinating with participants' diverse preferences and schedules, as well as settling expenses. Furthermore, selecting destinations that satisfy all participants, and ensuring a smooth booking and payment process are essential.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes means for generating optimal visit candidates based on participant preference data, means for generating location candidates tailored to preferences using prompts, and means for processing payments through the platform after selection. This enables efficient event management that improves participant satisfaction.

[0300] "Participant preference data" refers to information collected based on the past activity history and preferences of each member, and is the data that serves as the basis for proposing optimal visit locations.

[0301] "Visit candidates" refer to a list of locations or places that may be visited at an event, generated based on the participant preference data.

[0302] "Generative intelligence technology" refers to artificial intelligence technology that analyzes large amounts of data to understand participant preferences and patterns and is used to make proposals.

[0303] "Reservation" refers to the procedure of securing seats, times, etc. in advance for the selected visit location.

[0304] "Participation confirmation" refers to the communication of intention confirmation conducted to confirm whether members can participate in the event on a specific date.

[0305] "Expenditure amount" refers to the calculation result of the expenses that each member will bear in relation to the event.

[0306] "Platform" refers to a digital environment through which users access information and conduct event scheduling and settlement procedures.

[0307] The system of this invention has a configuration for streamlining event planning and operation. The server manages participant preference data and uses generative intelligence technology to analyze data based on past activity history. As a result, it is possible to generate visit candidates tailored to individual preferences.

[0308] In this system, the user inputs event information using a terminal, presents specific requests through prompts, and receives results based on them. The terminal is developed using React Native and is cross-platform compatible. Operations from smartphones and smart glasses are possible.

[0309] The server manages data using AWS Lambda and DynamoDB, and performs real-time information processing. Generative intelligence technology leverages Google Cloud AI to analyze user preferences through prompts and provide results. Facility reservations and payment processing are handled through a dedicated platform. This enables smooth and efficient reservations and payments after selection.

[0310] For example, if a user enters a prompt such as, "I'd like to have a lunch gathering at an organic restaurant this weekend," the server generates a list of suitable restaurants based on the user's past visit history and preferences, and notifies the user's device in real time. The user can then immediately complete a reservation through the platform.

[0311] Furthermore, to coordinate everyone's schedules, the server has a system that checks attendance via the network and selects the date that is most likely to attract participants. After the event, individual expenses are automatically calculated and notified to each participant. This reduces the burden on the organizer (the user) and improves participant satisfaction.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] Users use their devices to enter basic event information and participant details. The entered information is sent to the server. The server receives the information, verifies its integrity, and stores it in its database.

[0315] Step 2:

[0316] The server collects participants' past activity history and preference data, and uses a generative AI model to generate prompt-based visit suggestions. The server invokes the AI ​​model to identify and list potential locations that match the participants' preferences from that data. Data filtering and ranking are performed during the process of generating this list.

[0317] Step 3:

[0318] The terminal receives a list of potential destinations sent from the server and presents it to the user. Once the user makes a selection, the terminal sends that selection information to the server. The server automatically starts the reservation process for the selected destinations and completes the actual reservation by calling an external reservation system.

[0319] Step 4:

[0320] The server sends real-time confirmation messages to all participants based on the candidate dates set by the user. Participants select whether they can attend or not on their respective devices and reply to the server with that information. Based on these responses, the server selects the optimal date and notifies all participants.

[0321] Step 5:

[0322] After the event ends, users enter the actual expenses incurred into the server via their devices. The server uses this expense information to automatically calculate each participant's expenditure and sends a notification to each of them. Participants receive the notification via their devices and complete the payment through the designated platform.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] This invention describes a specific embodiment of an event management system that combines an emotion engine for recognizing user emotions and reflecting them in the plan. In this system, server, terminal, and user interaction play important roles.

[0325] Specifically, when a user plans an event, they input basic information and a list of participants into the system via a terminal. At this stage, the terminal's emotion engine measures the user's emotional state through user input behavior, voice, and facial expression analysis. For example, speech recognition technology is used to analyze the user's tone of voice and speaking style to estimate their emotional state.

[0326] The server combines emotional data obtained from the emotion engine with preference data based on the participant's past participation history. Based on this data, an artificial intelligence model is prepared to generate optimal candidate facilities for visits. For example, if a user is feeling stressed, the system will prioritize suggesting restaurants and facilities with a relaxing atmosphere.

[0327] Next, the server sends a generated list of potential visiting facilities to the terminal and presents it to the user. The user can then select their desired facility from the presented list. Based on this selection, the server automatically makes a reservation for the facility and notifies the user of the result in real time.

[0328] During event scheduling, the server continues to send attendance confirmation messages to participants via the communication network. Participant responses arrive at the server via their devices. Here again, the server analyzes the responses, and the emotion engine is incorporated into the process of selecting the optimal date based on the user's emotional state.

[0329] After the event ends, users enter the incurred expenses into the system, and the server calculates and notifies each participant of their individual expenses. An emotion engine may also be used to consider how to distribute expenses in a way that maximizes participant satisfaction.

[0330] This system allows users not only to improve operational efficiency but also to plan and manage more satisfying events that take emotional aspects into consideration. Real-time planning tailored to the individual emotions of each user is achieved.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user logs into the system and enters information about a new event. During this process, the terminal is equipped with a function to analyze the user's voice and facial expressions, and the emotion engine detects the user's emotional state.

[0334] Step 2:

[0335] The device sends detected emotion data to the server. The server combines the emotion data with the participant's preference data and uses an artificial intelligence model to generate optimal list of places to visit. For example, if the user is seeking relaxation, the server will create a list that includes quiet cafes and relaxation facilities.

[0336] Step 3:

[0337] The server sends the generated list of potential facilities to the terminal, which then presents this list to the user. The user then selects the desired facilities from the presented list.

[0338] Step 4:

[0339] When a user selects a facility to visit, the server automatically makes a reservation for that facility. This reservation information is then notified to the user via their device.

[0340] Step 5:

[0341] The user enters the dates of potential events into the server. The server then sends attendance confirmation messages to all participants via the communication network.

[0342] Step 6:

[0343] Participants respond to attendance requests using their devices, and this information is sent to the server. Based on the returned information, the server uses an emotion engine to select the optimal date. Scheduling is adjusted to reduce stress for users and participants.

[0344] Step 7:

[0345] After the event ends, users enter their actual expenses into the system. Based on this data, the server automatically calculates each participant's payment amount, considers appropriate expense distribution based on the emotion engine, and notifies the user's terminal. For example, it may be possible to provide additional consideration as a token of appreciation.

[0346] Step 8:

[0347] Participants review the notified fees and settle the payment. The server reports the final settlement status to the user via their terminal. This completes the entire process of the emotionally sensitive event.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] In modern society, event planning requires considering a great deal of information and factors, and making choices that reflect participants' emotions and preferences is particularly difficult. Furthermore, efficiently managing participant attendance and calculating cost sharing is crucial, but existing systems have placed a heavy burden on users. This invention aims to resolve these problems and enable more user-friendly and emotionally resonant event management.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for generating optimal recommendation candidates based on participant preference information and user emotional information, means for measuring the emotional state using user input behavior, voice, and facial expression analysis, and means for transmitting attendance confirmations for the plan to relevant parties via an information network. This enables the integration of more data, the proposal of plans that match the user's needs and emotions, and efficient and highly satisfying event management.

[0353] "Preference information" refers to information that indicates a participant's past activity history and personal preferences.

[0354] "Emotional information" refers to information that indicates the user's emotional state, obtained from user input behavior, voice, and facial expression analysis.

[0355] A "recommended list" is a list of suggested facilities and services that meet specific criteria, generated based on participants' preference information and users' emotional information.

[0356] "Attendance confirmation" is the procedure for confirming participants' intention to participate in planned activities or events.

[0357] An "information network" is a network system that connects computers and other devices for the purpose of transmitting data and information.

[0358] "Cost sharing" refers to calculating and distributing the costs associated with a specific activity or event to each participant.

[0359] The system of this invention is an advanced event management technology realized through collaboration between users, terminals, and servers via a computer network. In particular, it specializes in a function that integrates emotional data and preference data to propose the optimal event plan to the user.

[0360] The terminal provides an input interface for users to plan events. Users can enter basic event information and participant lists via the terminal. For example, tablets and smartphones are used, and the entered data is transmitted to the server in real time.

[0361] In acquiring emotional information, the device utilizes facial recognition and voice analysis technologies. Specifically, it uses "voice recognition technology" and "facial analysis software" to analyze the user's voice tone and facial expressions and estimate their emotional state. For example, if the user feels like they want to relax, that information is sent to the server as emotional data.

[0362] The server integrates emotional information received from the terminal and preference data obtained from the user's past participation history. The server implements a "machine learning model" and generates recommended facilities suitable for the user based on this data. A machine learning platform (e.g., "any AI platform") can be used in this process. The generated facility candidates are presented to the user via the terminal, and the user selects their preference from among them.

[0363] For example, if you input a prompt such as "Please suggest a suitable facility for when I want to relax" into a generative AI model, the AI ​​can generate candidates such as spas and parks and present them to the user via the server.

[0364] In this way, users can plan events that match their own emotions and preferences via their devices. This system allows users to hold more satisfying events.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] Users use a terminal to input basic event information and participant lists. Specifically, users enter information such as the event name, date and time, location, and participant email addresses into the terminal. This input data is sent to the server in real time. The entered data is stored in a database as basic information for event planning.

[0368] Step 2:

[0369] The device analyzes the user's facial expressions and voice to acquire emotional information. The device is equipped with a camera and microphone, and the resulting video and audio data is processed. Specifically, voice analysis software is used to detect the tone of voice, and facial recognition technology is used to analyze the user's facial expressions. As a result, the user's emotional state is quantified and sent to the server.

[0370] Step 3:

[0371] The server aggregates emotional information received from terminals and preference information obtained from participants' past participation history. The server receives this data as input and queries the database to extract participants' preferences. The obtained preference information is integrated with the user's emotional information and used as necessary information for event planning.

[0372] Step 4:

[0373] The server uses a machine learning model to generate optimal recommendation candidates. In this process, the server inputs integrated preference and sentiment information as prompts to the AI ​​model. For example, it might give the model the command "Recommend relaxing facilities," and the AI ​​generates a list of recommended facilities as output.

[0374] Step 5:

[0375] The server sends a list of recommended facilities back to the terminal and presents it to the user. The user selects a desired facility from this list via the terminal's interface. The selected facility information is sent to the server and recorded.

[0376] Step 6:

[0377] The server automatically makes facility reservations based on the user's selection. The server calls the API of an external reservation system, using the user's name and desired date and time as input, and confirms the reservation. After completion, reservation confirmation information is generated as output and notified to the user.

[0378] Step 7:

[0379] The server sends attendance confirmation messages to participants via the information network. These messages include event details and a link to ask about attendance, to which participants respond. The response data is sent to the server, where attendance information from participants is collected.

[0380] Step 8:

[0381] The server analyzes the collected attendance information and selects the optimal date. The analysis calculates the date that allows the most participants to attend, based on the attendance response data. The calculated optimal date is then finalized as output and notified to users and participants.

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] In modern event management, the focus is not simply on scheduling participants' schedules, but also on providing a more satisfying service that takes into account their emotional state. Furthermore, there is a challenge to improve the quality of daily life through appropriate, real-time suggestions that consider emotions in both home and public settings.

[0385] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0386] In this invention, the server includes means for generating candidate optimal visiting locations based on participants' preference information, means for analyzing residents' emotional states in real time and generating appropriate suggestions based on that analysis, and means for suggesting appropriate activities or relaxation methods to residents based on the emotional analysis. This makes it possible to provide individually optimized services based on emotional states.

[0387] "Participant preference information" refers to data collected based on users' past behavior and preferences, and serves as the basis for presenting specific options and candidates.

[0388] The "optimal place to visit" is a candidate location that is predicted to provide the highest level of satisfaction for the user, taking into account the participant's preferences and emotional state.

[0389] "Automatic booking" refers to a process where, based on user selection, the system completes online bookings without requiring manual intervention.

[0390] "Event attendance confirmation" refers to the act of contacting participants via email or notification to inquire about their availability to attend a scheduled event.

[0391] "Communication infrastructure" refers to the entire network infrastructure that enables the transmission and reception of data, and includes the internet and local networks.

[0392] "Real-time analysis" refers to the ability to perform analysis and processing immediately at the moment data is generated.

[0393] "Appropriate activities or relaxation methods" refer to actions or methods recommended to promote the user's mental and physical health at that particular time, based on their emotional state.

[0394] "Automatically calculating expense settlements" refers to the process where the system automatically calculates and organizes the financial burdens that users are responsible for, and then notifies the participants of the results.

[0395] The system for realizing this invention consists of a server, an emotion recognition engine, and a terminal. The server processes participant preference information and emotion data acquired in real time, and suggests the optimal place to visit according to the participant's emotional state. The terminal has the function of receiving input from the user and displaying output from the server. The user can input necessary information via the terminal and receive suggestions from the terminal, thereby adjusting events based on emotions.

[0396] The specific technologies used in this system include emotion recognition libraries, natural language processing libraries, and machine learning frameworks. For example, the Microsoft Azure Emotion API is used for emotion recognition, acquiring emotional data from the user's facial expressions and voice. The Google Cloud Natural Language API is also used to analyze the user's text input and generate appropriate suggestions. This allows for the presentation of appropriate activities and relaxation methods based on the user's emotions.

[0397] As a concrete example, let's consider a scenario where a user who is tired after work uses this system. The server would use an emotion recognition engine to understand the user's level of fatigue and make a suggestion such as, "You must be tired. Shall we play a recommended music list to help you relax?"

[0398] Examples of prompts to input into a generative AI model include the following:

[0399] "Analyze the user's emotional state and generate suggestions for stress relief."

[0400] "Design an algorithm that suggests music and activities that help users relax."

[0401] In this way, it becomes possible to provide specific services that are tailored to the user's emotional state.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server receives basic participant information, a participant list, and initial emotional state data from the user's terminal. This input data includes audio files and facial expression images. The server analyzes this data using an emotion recognition library (e.g., Microsoft Azure Emotion API) and outputs the user's emotional state as numerical data.

[0405] Step 2:

[0406] The server integrates the analyzed sentiment data with past participant preference information and uses a generative AI model to generate candidate optimal visiting locations. This model receives two inputs: sentiment data and preference information, and its output is a list of candidate locations.

[0407] Step 3:

[0408] The user receives a list of suggested locations to visit from the server on their device. The user then uses an intuitive interface to select a location from the list and sends that selection information to the server.

[0409] Step 4:

[0410] The server receives the user's location selection and automatically completes the reservation via the internet. The location and date / time to be reserved are determined based on the selection information and confirmed through the online reservation system.

[0411] Step 5:

[0412] The server continuously monitors the user's emotional state and suggests relaxation methods in real time. It continuously analyzes emotional data using an emotion recognition engine and, if necessary, sends automatically generated suggestions to the user's device using the Google Cloud Natural Language API.

[0413] Step 6:

[0414] Users review suggested activities and relaxation methods on their devices and select actions based on them. This information is also sent back to the server and stored as feedback. This data is used to improve the accuracy of future suggestions.

[0415] 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.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] 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.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] 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.

[0426] 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.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention specifically describes a management system for users to efficiently plan and manage events. In this system, the roles of servers, terminals, and users are important elements.

[0432] First, users access this system for the next event and enter basic event information and participant details. This information is sent to the server and processed by the system.

[0433] The server uses an artificial intelligence model to generate optimal facility suggestions based on participants' past participation history and preference data. These facility suggestions are customized according to the type of event and the participants' preferences. For example, if a participant has a history of liking Japanese food, Japanese restaurants will be prioritized in the list. The generated facility suggestions are sent to the user's terminal via the server.

[0434] The terminal presents the user with a list of available facilities, and the user selects their preferred facility from the list. Once the user selects a facility, the server automatically makes a reservation for that facility. The reservation status is notified to the user in real time.

[0435] Next, when scheduling the event, the user enters several possible dates into the system. The server sends an attendance confirmation message to all participants via the communication network. Participants reply to the server using their devices to indicate whether they can attend or not.

[0436] The server analyzes attendance responses from participants and selects the optimal date that allows the most participants to attend. This determined date is then notified to all participants, and the schedule is finalized.

[0437] After the event, users enter the actual expenses incurred into the system. The server automatically calculates the payment amount for each participant based on the entered expenses and notifies each participant. Each participant can check their payment amount and settle their account via their terminal.

[0438] This system significantly reduces the workload of event organizers and improves the satisfaction of all participants. Furthermore, it enables real-time information updates and efficient processing, ensuring smooth event management.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] Users access the system and enter basic event information and participant lists. This sends the necessary data to the server.

[0442] Step 2:

[0443] The server analyzes past participation history and preference data based on the information received from participants. Using an artificial intelligence model, it generates a list of candidate facilities that are most suitable for the participant.

[0444] Step 3:

[0445] The server sends a list of generated potential facilities to the terminal. The terminal presents this list to the user, who then selects the desired facility from the presented list.

[0446] Step 4:

[0447] When a user selects a facility, the server automatically makes a reservation for that facility. This reservation status is notified to the user in real time via their device.

[0448] Step 5:

[0449] The user enters suggested dates and times for the event into the system. Based on this, the server sends attendance confirmation messages to participants via the communication network.

[0450] Step 6:

[0451] The terminal sends attendance responses from participants to the server. The server analyzes these responses and selects the date that is available to the most participants.

[0452] Step 7:

[0453] The server notifies all participants of the confirmed optimal date and determines the event schedule. This information is provided to participants in real time via their devices.

[0454] Step 8:

[0455] After the event ends, users enter the total actual cost into the system. The server calculates the payment amount for each participant based on the entered costs and notifies the participants via their terminals.

[0456] Step 9:

[0457] Participants use their devices to check their payment amounts and settle their expenses appropriately. This entire process significantly streamlines the user's event management tasks.

[0458] (Example 1)

[0459] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0460] Events with a large number of participants require significant time and effort to plan and manage, making it particularly difficult to select a venue that matches participants' preferences and to adjust the schedule to the optimal level. Furthermore, expense settlement presents challenges such as manual calculation errors and failure to notify participants.

[0461] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0462] In this invention, the server includes means for generating optimal options based on participant preference information, means for automatically making reservations for confirmed options from the generated options, and means for transmitting confirmation of participation in the event to members via information communication. This makes it possible to improve the efficiency of event planning and operation and increase the satisfaction of all participants.

[0463] "Participants" refers to individuals or groups who are planning to participate in an event or occurrence.

[0464] "Preference information" refers to data based on participants' preferences, interests, and past selection history, and is information that reflects individual preferences.

[0465] "Options" refers to selectable items such as venues and dates that participants can choose from at an event.

[0466] A "reservation" is a procedure that secures the use of designated facilities or services in advance, based on the options selected by the participant.

[0467] "Information and communication" refers to the technology or protocol used to send and receive data over a digital network.

[0468] A "member" refers to a person who belongs to a specific group or organization and participates in its activities.

[0469] "Expense calculation" is the process of determining the amount each participant should bear based on the expenses incurred.

[0470] This invention is a system for streamlining event planning and management, in which servers, terminals, and users cooperate with each other to realize its functions.

[0471] Users access the system from their devices and send basic event information and participant details to the server via prompt messages. By using specific prompts such as, "I want to plan our next company trip. Please suggest the best accommodation and meal plan considering the participants' preferences," the server utilizes participants' past event participation history and preference information, and uses a generative AI model to list the best options.

[0472] The server generates optimal choices based on participant data and then sends them to the user's device. This process utilizes machine learning algorithms, such as generative AI models, and the system reflects the participant's preferences, resulting in more personalized choices. For example, if data shows a past preference for Japanese food, Japanese restaurants will be listed as recommended options.

[0473] The user selects their desired option from a list presented via their device, and this selection is sent to the server, automatically processing the reservation. The reservation status is notified to the user in real time and can be checked on their device.

[0474] Furthermore, the server receives schedule input from users, sends participation confirmations to participants via the information and communication network, and determines the optimal schedule based on the responses. Automating this process reduces the workload for users and ensures optimal scheduling.

[0475] Furthermore, after the event, users enter their expenses, and the server automatically calculates the cost for each participant. This allows each participant to easily check and settle their expenses from their own device, streamlining the settlement process.

[0476] In this way, the entire process from event planning to execution and settlement is efficiently managed, resulting in smoother operation.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user accesses the system using a terminal and enters basic event information and participant details. The entered data is sent to the server via prompts. This input includes the event name, proposed dates, and participant list. Based on this information, the server prepares for the next steps.

[0480] Step 2:

[0481] The server analyzes the received event information and participant data, and uses a generative AI model to generate optimal facility and schedule options. This process utilizes past participation history and preference information to process the data and create a list of candidates tailored to the participant's preferences. This candidate list is then sent from the server to the user's terminal as available options.

[0482] Step 3:

[0483] The terminal presents the user with a list of available facilities and dates received from the server. The user selects their preferred option from the presented list. Based on this selection, the terminal sends the user's intended reservation details to the server. This then enables the next reservation process.

[0484] Step 4:

[0485] The server automatically makes reservations for facilities and dates based on the user's selections. Once the reservation process is complete, the results are notified to the user's device in real time. The server generates data for reservation confirmation and confirms the reservation in the relevant system.

[0486] Step 5:

[0487] The user enters potential event dates into the server, which then receives them. The server sends confirmation of participation to all participants via the information and communication network. This confirmation includes a message asking about their availability for multiple dates.

[0488] Step 6:

[0489] Participants open the participation confirmation message they receive on their device, select whether they can participate or not, and reply to the server. This response data is compiled by the server and used to automatically select the most suitable date for participation.

[0490] Step 7:

[0491] The server selects the most suitable date based on the collected participant responses and notifies all participants of the selection result. This notification includes the confirmed date, and participants can add the event to their own schedules.

[0492] Step 8:

[0493] After the event ends, users enter the actual costs incurred into the system via their terminals, and the server receives and analyzes this data. The server calculates each participant's cost and sends a notification to each participant. This calculation uses data such as the total cost and the number of participants.

[0494] (Application Example 1)

[0495] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0496] In planning and managing events, there are challenges in efficiently coordinating with participants' diverse preferences and schedules, as well as settling expenses. Furthermore, selecting destinations that satisfy all participants, and ensuring a smooth booking and payment process are essential.

[0497] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0498] In this invention, the server includes means for generating optimal visit candidates based on participant preference data, means for generating location candidates tailored to preferences using prompts, and means for processing payments through the platform after selection. This enables efficient event management that improves participant satisfaction.

[0499] "Participant preference data" refers to information collected based on each member's past activity history and preferences, and serves as the basis for suggesting the most suitable destinations.

[0500] "Possible Visits" is a list of locations and places that participants may visit during the event, generated based on their preference data.

[0501] "Generative intelligence technology" is an artificial intelligence technology used to analyze large amounts of data to understand participants' preferences and patterns, and then make suggestions.

[0502] A "reservation" is the process of securing a seat, time, and other details in advance for a chosen place to visit.

[0503] "Participation confirmation" is a communication to confirm whether a member is able to attend an event on a specific date.

[0504] "Expenses" refers to the calculated cost that each member will be responsible for in connection with the event.

[0505] A "platform" is a digital environment where users can access information and manage event schedules and payment procedures.

[0506] The system of this invention has a configuration for streamlining event planning and management. The server manages participant preference data and uses generative intelligence technology to analyze the data based on past activity history. This makes it possible to generate visitor suggestions tailored to individual preferences.

[0507] In this system, users input event information using a terminal, present specific requests through prompts, and receive results based on those requests. The terminal is developed using React Native and is cross-platform compatible. It can be operated from smartphones and smart glasses.

[0508] The server manages data using AWS Lambda and DynamoDB, and performs real-time information processing. Generative intelligence technology leverages Google Cloud AI to analyze user preferences through prompts and provide results. Facility reservations and payment processing are handled through a dedicated platform. This enables smooth and efficient reservations and payments after selection.

[0509] For example, if a user enters a prompt such as, "I'd like to have a lunch gathering at an organic restaurant this weekend," the server generates a list of suitable restaurants based on the user's past visit history and preferences, and notifies the user's device in real time. The user can then immediately complete a reservation through the platform.

[0510] Furthermore, to coordinate everyone's schedules, the server has a system that checks attendance via the network and selects the date that is most likely to attract participants. After the event, individual expenses are automatically calculated and notified to each participant. This reduces the burden on the organizer (the user) and improves participant satisfaction.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] Users use their devices to enter basic event information and participant details. The entered information is sent to the server. The server receives the information, verifies its integrity, and stores it in its database.

[0514] Step 2:

[0515] The server collects participants' past activity history and preference data, and uses a generative AI model to generate prompt-based visit suggestions. The server invokes the AI ​​model to identify and list potential locations that match the participants' preferences from that data. Data filtering and ranking are performed during the process of generating this list.

[0516] Step 3:

[0517] The terminal receives a list of potential destinations sent from the server and presents it to the user. Once the user makes a selection, the terminal sends that selection information to the server. The server automatically starts the reservation process for the selected destinations and completes the actual reservation by calling an external reservation system.

[0518] Step 4:

[0519] The server sends real-time confirmation messages to all participants based on the candidate dates set by the user. Participants select whether they can attend or not on their respective devices and reply to the server with that information. Based on these responses, the server selects the optimal date and notifies all participants.

[0520] Step 5:

[0521] After the event ends, users enter the actual expenses incurred into the server via their devices. The server uses this expense information to automatically calculate each participant's expenditure and sends a notification to each of them. Participants receive the notification via their devices and complete the payment through the designated platform.

[0522] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0523] This invention describes a specific embodiment of an event management system that combines an emotion engine for recognizing user emotions and reflecting them in the plan. In this system, server, terminal, and user interaction play important roles.

[0524] Specifically, when a user plans an event, they input basic information and a list of participants into the system via a terminal. At this stage, the terminal's emotion engine measures the user's emotional state through user input behavior, voice, and facial expression analysis. For example, speech recognition technology is used to analyze the user's tone of voice and speaking style to estimate their emotional state.

[0525] The server combines emotional data obtained from the emotion engine with preference data based on the participant's past participation history. Based on this data, an artificial intelligence model is prepared to generate optimal candidate facilities for visits. For example, if a user is feeling stressed, the system will prioritize suggesting restaurants and facilities with a relaxing atmosphere.

[0526] Next, the server sends a generated list of potential visiting facilities to the terminal and presents it to the user. The user can then select their desired facility from the presented list. Based on this selection, the server automatically makes a reservation for the facility and notifies the user of the result in real time.

[0527] During event scheduling, the server continues to send attendance confirmation messages to participants via the communication network. Participant responses arrive at the server via their devices. Here again, the server analyzes the responses, and the emotion engine is incorporated into the process of selecting the optimal date based on the user's emotional state.

[0528] After the event ends, users enter the incurred expenses into the system, and the server calculates and notifies each participant of their individual expenses. An emotion engine may also be used to consider how to distribute expenses in a way that maximizes participant satisfaction.

[0529] This system allows users not only to improve operational efficiency but also to plan and manage more satisfying events that take emotional aspects into consideration. Real-time planning tailored to the individual emotions of each user is achieved.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The user logs into the system and enters information about a new event. During this process, the terminal is equipped with a function to analyze the user's voice and facial expressions, and the emotion engine detects the user's emotional state.

[0533] Step 2:

[0534] The device sends detected emotion data to the server. The server combines the emotion data with the participant's preference data and uses an artificial intelligence model to generate optimal list of places to visit. For example, if the user is seeking relaxation, the server will create a list that includes quiet cafes and relaxation facilities.

[0535] Step 3:

[0536] The server sends the generated list of potential facilities to the terminal, which then presents this list to the user. The user then selects the desired facilities from the presented list.

[0537] Step 4:

[0538] When a user selects a facility to visit, the server automatically makes a reservation for that facility. This reservation information is then notified to the user via their device.

[0539] Step 5:

[0540] The user enters the dates of potential events into the server. The server then sends attendance confirmation messages to all participants via the communication network.

[0541] Step 6:

[0542] Participants respond to attendance requests using their devices, and this information is sent to the server. Based on the returned information, the server uses an emotion engine to select the optimal date. Scheduling is adjusted to reduce stress for users and participants.

[0543] Step 7:

[0544] After the event ends, users enter their actual expenses into the system. Based on this data, the server automatically calculates each participant's payment amount, considers appropriate expense distribution based on the emotion engine, and notifies the user's terminal. For example, it may be possible to provide additional consideration as a token of appreciation.

[0545] Step 8:

[0546] Participants review the notified fees and settle the payment. The server reports the final settlement status to the user via their terminal. This completes the entire process of the emotionally sensitive event.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In modern society, event planning requires considering a great deal of information and factors, and making choices that reflect participants' emotions and preferences is particularly difficult. Furthermore, efficiently managing participant attendance and calculating cost sharing is crucial, but existing systems have placed a heavy burden on users. This invention aims to resolve these problems and enable more user-friendly and emotionally resonant event management.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes means for generating optimal recommendation candidates based on participant preference information and user emotional information, means for measuring the emotional state using user input behavior, voice, and facial expression analysis, and means for transmitting attendance confirmations for the plan to relevant parties via an information network. This enables the integration of more data, the proposal of plans that match the user's needs and emotions, and efficient and highly satisfying event management.

[0552] "Preference information" refers to information that indicates a participant's past activity history and personal preferences.

[0553] "Emotional information" refers to information that indicates the user's emotional state, obtained from user input behavior, voice, and facial expression analysis.

[0554] A "recommended list" is a list of suggested facilities and services that meet specific criteria, generated based on participants' preference information and users' emotional information.

[0555] "Attendance confirmation" is the procedure for confirming participants' intention to participate in planned activities or events.

[0556] An "information network" is a network system that connects computers and other devices for the purpose of transmitting data and information.

[0557] "Cost sharing" refers to calculating and distributing the costs associated with a specific activity or event to each participant.

[0558] The system of this invention is an advanced event management technology realized through collaboration between users, terminals, and servers via a computer network. In particular, it specializes in a function that integrates emotional data and preference data to propose the optimal event plan to the user.

[0559] The terminal provides an input interface for users to plan events. Users can enter basic event information and participant lists via the terminal. For example, tablets and smartphones are used, and the entered data is transmitted to the server in real time.

[0560] In acquiring emotional information, the device utilizes facial recognition and voice analysis technologies. Specifically, it uses "voice recognition technology" and "facial analysis software" to analyze the user's voice tone and facial expressions and estimate their emotional state. For example, if the user feels like they want to relax, that information is sent to the server as emotional data.

[0561] The server integrates emotional information received from the terminal and preference data obtained from the user's past participation history. The server implements a "machine learning model" and generates recommended facilities suitable for the user based on this data. A machine learning platform (e.g., "any AI platform") can be used in this process. The generated facility candidates are presented to the user via the terminal, and the user selects their preference from among them.

[0562] For example, if you input a prompt such as "Please suggest a suitable facility for when I want to relax" into a generative AI model, the AI ​​can generate candidates such as spas and parks and present them to the user via the server.

[0563] In this way, users can plan events that match their own emotions and preferences via their devices. This system allows users to hold more satisfying events.

[0564] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0565] Step 1:

[0566] Users use a terminal to input basic event information and participant lists. Specifically, users enter information such as the event name, date and time, location, and participant email addresses into the terminal. This input data is sent to the server in real time. The entered data is stored in a database as basic information for event planning.

[0567] Step 2:

[0568] The device analyzes the user's facial expressions and voice to acquire emotional information. The device is equipped with a camera and microphone, and the resulting video and audio data is processed. Specifically, voice analysis software is used to detect the tone of voice, and facial recognition technology is used to analyze the user's facial expressions. As a result, the user's emotional state is quantified and sent to the server.

[0569] Step 3:

[0570] The server aggregates emotional information received from terminals and preference information obtained from participants' past participation history. The server receives this data as input and queries the database to extract participants' preferences. The obtained preference information is integrated with the user's emotional information and used as necessary information for event planning.

[0571] Step 4:

[0572] The server uses a machine learning model to generate optimal recommendation candidates. In this process, the server inputs integrated preference and sentiment information as prompts to the AI ​​model. For example, it might give the model the command "Recommend relaxing facilities," and the AI ​​generates a list of recommended facilities as output.

[0573] Step 5:

[0574] The server sends a list of recommended facilities back to the terminal and presents it to the user. The user selects a desired facility from this list via the terminal's interface. The selected facility information is sent to the server and recorded.

[0575] Step 6:

[0576] The server automatically makes facility reservations based on the user's selection. The server calls the API of an external reservation system, using the user's name and desired date and time as input, and confirms the reservation. After completion, reservation confirmation information is generated as output and notified to the user.

[0577] Step 7:

[0578] The server sends attendance confirmation messages to participants via the information network. These messages include event details and a link to ask about attendance, to which participants respond. The response data is sent to the server, where attendance information from participants is collected.

[0579] Step 8:

[0580] The server analyzes the collected attendance information and selects the optimal date. The analysis calculates the date that allows the most participants to attend, based on the attendance response data. The calculated optimal date is then finalized as output and notified to users and participants.

[0581] (Application Example 2)

[0582] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] In modern event management, the focus is not simply on scheduling participants' schedules, but also on providing a more satisfying service that takes into account their emotional state. Furthermore, there is a challenge to improve the quality of daily life through appropriate, real-time suggestions that consider emotions in both home and public settings.

[0584] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0585] In this invention, the server includes means for generating candidate optimal visiting locations based on participants' preference information, means for analyzing residents' emotional states in real time and generating appropriate suggestions based on that analysis, and means for suggesting appropriate activities or relaxation methods to residents based on the emotional analysis. This makes it possible to provide individually optimized services based on emotional states.

[0586] "Participant preference information" refers to data collected based on users' past behavior and preferences, and serves as the basis for presenting specific options and candidates.

[0587] The "optimal place to visit" is a candidate location that is predicted to provide the highest level of satisfaction for the user, taking into account the participant's preferences and emotional state.

[0588] "Automatic booking" refers to a process where, based on user selection, the system completes online bookings without requiring manual intervention.

[0589] "Event attendance confirmation" refers to the act of contacting participants via email or notification to inquire about their availability to attend a scheduled event.

[0590] "Communication infrastructure" refers to the entire network infrastructure that enables the transmission and reception of data, and includes the internet and local networks.

[0591] "Real-time analysis" refers to the ability to perform analysis and processing immediately at the moment data is generated.

[0592] "Appropriate activities or relaxation methods" refer to actions or methods recommended to promote the user's mental and physical health at that particular time, based on their emotional state.

[0593] "Automatically calculating expense settlements" refers to the process where the system automatically calculates and organizes the financial burdens that users are responsible for, and then notifies the participants of the results.

[0594] The system for realizing this invention consists of a server, an emotion recognition engine, and a terminal. The server processes participant preference information and emotion data acquired in real time, and suggests the optimal place to visit according to the participant's emotional state. The terminal has the function of receiving input from the user and displaying output from the server. The user can input necessary information via the terminal and receive suggestions from the terminal, thereby adjusting events based on emotions.

[0595] The specific technologies used in this system include emotion recognition libraries, natural language processing libraries, and machine learning frameworks. For example, the Microsoft Azure Emotion API is used for emotion recognition, acquiring emotional data from the user's facial expressions and voice. The Google Cloud Natural Language API is also used to analyze the user's text input and generate appropriate suggestions. This allows for the presentation of appropriate activities and relaxation methods based on the user's emotions.

[0596] As a concrete example, let's consider a scenario where a user who is tired after work uses this system. The server would use an emotion recognition engine to understand the user's level of fatigue and make a suggestion such as, "You must be tired. Shall we play a recommended music list to help you relax?"

[0597] Examples of prompts to input into a generative AI model include the following:

[0598] "Analyze the user's emotional state and generate suggestions for stress relief."

[0599] "Design an algorithm that suggests music and activities that help users relax."

[0600] In this way, it becomes possible to provide specific services that are tailored to the user's emotional state.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The server receives basic participant information, a participant list, and initial emotional state data from the user's terminal. This input data includes audio files and facial expression images. The server analyzes this data using an emotion recognition library (e.g., Microsoft Azure Emotion API) and outputs the user's emotional state as numerical data.

[0604] Step 2:

[0605] The server integrates the analyzed sentiment data with past participant preference information and uses a generative AI model to generate candidate optimal visiting locations. This model receives two inputs: sentiment data and preference information, and its output is a list of candidate locations.

[0606] Step 3:

[0607] The user receives a list of suggested locations to visit from the server on their device. The user then uses an intuitive interface to select a location from the list and sends that selection information to the server.

[0608] Step 4:

[0609] The server receives the user's location selection and automatically completes the reservation via the internet. The location and date / time to be reserved are determined based on the selection information and confirmed through the online reservation system.

[0610] Step 5:

[0611] The server continuously monitors the user's emotional state and suggests relaxation methods in real time. It continuously analyzes emotional data using an emotion recognition engine and, if necessary, sends automatically generated suggestions to the user's device using the Google Cloud Natural Language API.

[0612] Step 6:

[0613] Users review suggested activities and relaxation methods on their devices and select actions based on them. This information is also sent back to the server and stored as feedback. This data is used to improve the accuracy of future suggestions.

[0614] 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.

[0615] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0621] 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.

[0622] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0623] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0624] 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.

[0625] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0626] 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.

[0627] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0628] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0629] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0630] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] This invention specifically describes a management system for users to efficiently plan and manage events. In this system, the roles of servers, terminals, and users are important elements.

[0632] First, users access this system for the next event and enter basic event information and participant details. This information is sent to the server and processed by the system.

[0633] The server uses an artificial intelligence model to generate optimal facility suggestions based on participants' past participation history and preference data. These facility suggestions are customized according to the type of event and the participants' preferences. For example, if a participant has a history of liking Japanese food, Japanese restaurants will be prioritized in the list. The generated facility suggestions are sent to the user's terminal via the server.

[0634] The terminal presents the user with a list of available facilities, and the user selects their preferred facility from the list. Once the user selects a facility, the server automatically makes a reservation for that facility. The reservation status is notified to the user in real time.

[0635] Next, when scheduling the event, the user enters several possible dates into the system. The server sends an attendance confirmation message to all participants via the communication network. Participants reply to the server using their devices to indicate whether they can attend or not.

[0636] The server analyzes attendance responses from participants and selects the optimal date that allows the most participants to attend. This determined date is then notified to all participants, and the schedule is finalized.

[0637] After the event, users enter the actual expenses incurred into the system. The server automatically calculates the payment amount for each participant based on the entered expenses and notifies each participant. Each participant can check their payment amount and settle their account via their terminal.

[0638] This system significantly reduces the workload of event organizers and improves the satisfaction of all participants. Furthermore, it enables real-time information updates and efficient processing, ensuring smooth event management.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] Users access the system and enter basic event information and participant lists. This sends the necessary data to the server.

[0642] Step 2:

[0643] The server analyzes past participation history and preference data based on the information received from participants. Using an artificial intelligence model, it generates a list of candidate facilities that are most suitable for the participant.

[0644] Step 3:

[0645] The server sends a list of generated potential facilities to the terminal. The terminal presents this list to the user, who then selects the desired facility from the presented list.

[0646] Step 4:

[0647] When a user selects a facility, the server automatically makes a reservation for that facility. This reservation status is notified to the user in real time via their device.

[0648] Step 5:

[0649] The user enters suggested dates and times for the event into the system. Based on this, the server sends attendance confirmation messages to participants via the communication network.

[0650] Step 6:

[0651] The terminal sends attendance responses from participants to the server. The server analyzes these responses and selects the date that is available to the most participants.

[0652] Step 7:

[0653] The server notifies all participants of the confirmed optimal date and determines the event schedule. This information is provided to participants in real time via their devices.

[0654] Step 8:

[0655] After the event ends, users enter the total actual cost into the system. The server calculates the payment amount for each participant based on the entered costs and notifies the participants via their terminals.

[0656] Step 9:

[0657] Participants use their devices to check their payment amounts and settle their expenses appropriately. This entire process significantly streamlines the user's event management tasks.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] Events with a large number of participants require significant time and effort to plan and manage, making it particularly difficult to select a venue that matches participants' preferences and to adjust the schedule to the optimal level. Furthermore, expense settlement presents challenges such as manual calculation errors and failure to notify participants.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes means for generating optimal options based on participant preference information, means for automatically making reservations for confirmed options from the generated options, and means for transmitting confirmation of participation in the event to members via information communication. This makes it possible to improve the efficiency of event planning and operation and increase the satisfaction of all participants.

[0663] "Participants" refers to individuals or groups who are planning to participate in an event or occurrence.

[0664] "Preference information" refers to data based on participants' preferences, interests, and past selection history, and is information that reflects individual preferences.

[0665] "Options" refers to selectable items such as venues and dates that participants can choose from at an event.

[0666] A "reservation" is a procedure that secures the use of designated facilities or services in advance, based on the options selected by the participant.

[0667] "Information and communication" refers to the technology or protocol used to send and receive data over a digital network.

[0668] A "member" refers to a person who belongs to a specific group or organization and participates in its activities.

[0669] "Expense calculation" is the process of determining the amount each participant should bear based on the expenses incurred.

[0670] This invention is a system for streamlining event planning and management, in which servers, terminals, and users cooperate with each other to realize its functions.

[0671] Users access the system from their devices and send basic event information and participant details to the server via prompt messages. By using specific prompts such as, "I want to plan our next company trip. Please suggest the best accommodation and meal plan considering the participants' preferences," the server utilizes participants' past event participation history and preference information, and uses a generative AI model to list the best options.

[0672] The server generates optimal choices based on participant data and then sends them to the user's device. This process utilizes machine learning algorithms, such as generative AI models, and the system reflects the participant's preferences, resulting in more personalized choices. For example, if data shows a past preference for Japanese food, Japanese restaurants will be listed as recommended options.

[0673] The user selects their desired option from a list presented via their device, and this selection is sent to the server, automatically processing the reservation. The reservation status is notified to the user in real time and can be checked on their device.

[0674] Furthermore, the server receives schedule input from users, sends participation confirmations to participants via the information and communication network, and determines the optimal schedule based on the responses. Automating this process reduces the workload for users and ensures optimal scheduling.

[0675] Furthermore, after the event, users enter their expenses, and the server automatically calculates the cost for each participant. This allows each participant to easily check and settle their expenses from their own device, streamlining the settlement process.

[0676] In this way, the entire process from event planning to execution and settlement is efficiently managed, resulting in smoother operation.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user accesses the system using a terminal and enters basic event information and participant details. The entered data is sent to the server via prompts. This input includes the event name, proposed dates, and participant list. Based on this information, the server prepares for the next steps.

[0680] Step 2:

[0681] The server analyzes the received event information and participant data, and uses a generative AI model to generate optimal facility and schedule options. This process utilizes past participation history and preference information to process the data and create a list of candidates tailored to the participant's preferences. This candidate list is then sent from the server to the user's terminal as available options.

[0682] Step 3:

[0683] The terminal presents the user with a list of available facilities and dates received from the server. The user selects their preferred option from the presented list. Based on this selection, the terminal sends the user's intended reservation details to the server. This then enables the next reservation process.

[0684] Step 4:

[0685] The server automatically makes reservations for facilities and dates based on the user's selections. Once the reservation process is complete, the results are notified to the user's device in real time. The server generates data for reservation confirmation and confirms the reservation in the relevant system.

[0686] Step 5:

[0687] The user enters potential event dates into the server, which then receives them. The server sends confirmation of participation to all participants via the information and communication network. This confirmation includes a message asking about their availability for multiple dates.

[0688] Step 6:

[0689] Participants open the participation confirmation message they receive on their device, select whether they can participate or not, and reply to the server. This response data is compiled by the server and used to automatically select the most suitable date for participation.

[0690] Step 7:

[0691] The server selects the most suitable date based on the collected participant responses and notifies all participants of the selection result. This notification includes the confirmed date, and participants can add the event to their own schedules.

[0692] Step 8:

[0693] After the event ends, users enter the actual costs incurred into the system via their terminals, and the server receives and analyzes this data. The server calculates each participant's cost and sends a notification to each participant. This calculation uses data such as the total cost and the number of participants.

[0694] (Application Example 1)

[0695] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0696] In planning and managing events, there are challenges in efficiently coordinating with participants' diverse preferences and schedules, as well as settling expenses. Furthermore, selecting destinations that satisfy all participants, and ensuring a smooth booking and payment process are essential.

[0697] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0698] In this invention, the server includes means for generating optimal visit candidates based on participant preference data, means for generating location candidates tailored to preferences using prompts, and means for processing payments through the platform after selection. This enables efficient event management that improves participant satisfaction.

[0699] "Participant preference data" refers to information collected based on each member's past activity history and preferences, and serves as the basis for suggesting the most suitable destinations.

[0700] "Possible Visits" is a list of locations and places that participants may visit during the event, generated based on their preference data.

[0701] "Generative intelligence technology" is an artificial intelligence technology used to analyze large amounts of data to understand participants' preferences and patterns, and then make suggestions.

[0702] A "reservation" is the process of securing a seat, time, and other details in advance for a chosen place to visit.

[0703] "Participation confirmation" is a communication to confirm whether a member is able to attend an event on a specific date.

[0704] "Expenses" refers to the calculated cost that each member will be responsible for in connection with the event.

[0705] A "platform" is a digital environment where users can access information and manage event schedules and payment procedures.

[0706] The system of this invention has a configuration for streamlining event planning and management. The server manages participant preference data and uses generative intelligence technology to analyze the data based on past activity history. This makes it possible to generate visitor suggestions tailored to individual preferences.

[0707] In this system, users input event information using a terminal, present specific requests through prompts, and receive results based on those requests. The terminal is developed using React Native and is cross-platform compatible. It can be operated from smartphones and smart glasses.

[0708] The server manages data using AWS Lambda and DynamoDB, and performs real-time information processing. Generative intelligence technology leverages Google Cloud AI to analyze user preferences through prompts and provide results. Facility reservations and payment processing are handled through a dedicated platform. This enables smooth and efficient reservations and payments after selection.

[0709] For example, if a user enters a prompt such as, "I'd like to have a lunch gathering at an organic restaurant this weekend," the server generates a list of suitable restaurants based on the user's past visit history and preferences, and notifies the user's device in real time. The user can then immediately complete a reservation through the platform.

[0710] Furthermore, to coordinate everyone's schedules, the server has a system that checks attendance via the network and selects the date that is most likely to attract participants. After the event, individual expenses are automatically calculated and notified to each participant. This reduces the burden on the organizer (the user) and improves participant satisfaction.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] Users use their devices to enter basic event information and participant details. The entered information is sent to the server. The server receives the information, verifies its integrity, and stores it in its database.

[0714] Step 2:

[0715] The server collects participants' past activity history and preference data, and uses a generative AI model to generate prompt-based visit suggestions. The server invokes the AI ​​model to identify and list potential locations that match the participants' preferences from that data. Data filtering and ranking are performed during the process of generating this list.

[0716] Step 3:

[0717] The terminal receives a list of potential destinations sent from the server and presents it to the user. Once the user makes a selection, the terminal sends that selection information to the server. The server automatically starts the reservation process for the selected destinations and completes the actual reservation by calling an external reservation system.

[0718] Step 4:

[0719] The server sends real-time confirmation messages to all participants based on the candidate dates set by the user. Participants select whether they can attend or not on their respective devices and reply to the server with that information. Based on these responses, the server selects the optimal date and notifies all participants.

[0720] Step 5:

[0721] After the event ends, users enter the actual expenses incurred into the server via their devices. The server uses this expense information to automatically calculate each participant's expenditure and sends a notification to each of them. Participants receive the notification via their devices and complete the payment through the designated platform.

[0722] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0723] This invention describes a specific embodiment of an event management system that combines an emotion engine for recognizing user emotions and reflecting them in the plan. In this system, server, terminal, and user interaction play important roles.

[0724] Specifically, when a user plans an event, they input basic information and a list of participants into the system via a terminal. At this stage, the terminal's emotion engine measures the user's emotional state through user input behavior, voice, and facial expression analysis. For example, speech recognition technology is used to analyze the user's tone of voice and speaking style to estimate their emotional state.

[0725] The server combines emotional data obtained from the emotion engine with preference data based on the participant's past participation history. Based on this data, an artificial intelligence model is prepared to generate optimal candidate facilities for visits. For example, if a user is feeling stressed, the system will prioritize suggesting restaurants and facilities with a relaxing atmosphere.

[0726] Next, the server sends a generated list of potential visiting facilities to the terminal and presents it to the user. The user can then select their desired facility from the presented list. Based on this selection, the server automatically makes a reservation for the facility and notifies the user of the result in real time.

[0727] During event scheduling, the server continues to send attendance confirmation messages to participants via the communication network. Participant responses arrive at the server via their devices. Here again, the server analyzes the responses, and the emotion engine is incorporated into the process of selecting the optimal date based on the user's emotional state.

[0728] After the event ends, users enter the incurred expenses into the system, and the server calculates and notifies each participant of their individual expenses. An emotion engine may also be used to consider how to distribute expenses in a way that maximizes participant satisfaction.

[0729] This system allows users not only to improve operational efficiency but also to plan and manage more satisfying events that take emotional aspects into consideration. Real-time planning tailored to the individual emotions of each user is achieved.

[0730] The following describes the processing flow.

[0731] Step 1:

[0732] The user logs into the system and enters information about a new event. During this process, the terminal is equipped with a function to analyze the user's voice and facial expressions, and the emotion engine detects the user's emotional state.

[0733] Step 2:

[0734] The device sends detected emotion data to the server. The server combines the emotion data with the participant's preference data and uses an artificial intelligence model to generate optimal list of places to visit. For example, if the user is seeking relaxation, the server will create a list that includes quiet cafes and relaxation facilities.

[0735] Step 3:

[0736] The server sends the generated list of potential facilities to the terminal, which then presents this list to the user. The user then selects the desired facilities from the presented list.

[0737] Step 4:

[0738] When a user selects a facility to visit, the server automatically makes a reservation for that facility. This reservation information is then notified to the user via their device.

[0739] Step 5:

[0740] The user enters the dates of potential events into the server. The server then sends attendance confirmation messages to all participants via the communication network.

[0741] Step 6:

[0742] Participants respond to attendance requests using their devices, and this information is sent to the server. Based on the returned information, the server uses an emotion engine to select the optimal date. Scheduling is adjusted to reduce stress for users and participants.

[0743] Step 7:

[0744] After the event ends, users enter their actual expenses into the system. Based on this data, the server automatically calculates each participant's payment amount, considers appropriate expense distribution based on the emotion engine, and notifies the user's terminal. For example, it may be possible to provide additional consideration as a token of appreciation.

[0745] Step 8:

[0746] Participants review the notified fees and settle the payment. The server reports the final settlement status to the user via their terminal. This completes the entire process of the emotionally sensitive event.

[0747] (Example 2)

[0748] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] In modern society, event planning requires considering a great deal of information and factors, and making choices that reflect participants' emotions and preferences is particularly difficult. Furthermore, efficiently managing participant attendance and calculating cost sharing is crucial, but existing systems have placed a heavy burden on users. This invention aims to resolve these problems and enable more user-friendly and emotionally resonant event management.

[0750] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0751] In this invention, the server includes means for generating optimal recommendation candidates based on participant preference information and user emotional information, means for measuring the emotional state using user input behavior, voice, and facial expression analysis, and means for transmitting attendance confirmations for the plan to relevant parties via an information network. This enables the integration of more data, the proposal of plans that match the user's needs and emotions, and efficient and highly satisfying event management.

[0752] "Preference information" refers to information that indicates a participant's past activity history and personal preferences.

[0753] "Emotional information" refers to information that indicates the user's emotional state, obtained from user input behavior, voice, and facial expression analysis.

[0754] A "recommended list" is a list of suggested facilities and services that meet specific criteria, generated based on participants' preference information and users' emotional information.

[0755] "Attendance confirmation" is the procedure for confirming participants' intention to participate in planned activities or events.

[0756] An "information network" is a network system that connects computers and other devices for the purpose of transmitting data and information.

[0757] "Cost sharing" refers to calculating and distributing the costs associated with a specific activity or event to each participant.

[0758] The system of this invention is an advanced event management technology realized through collaboration between users, terminals, and servers via a computer network. In particular, it specializes in a function that integrates emotional data and preference data to propose the optimal event plan to the user.

[0759] The terminal provides an input interface for users to plan events. Users can enter basic event information and participant lists via the terminal. For example, tablets and smartphones are used, and the entered data is transmitted to the server in real time.

[0760] In acquiring emotional information, the device utilizes facial recognition and voice analysis technologies. Specifically, it uses "voice recognition technology" and "facial analysis software" to analyze the user's voice tone and facial expressions and estimate their emotional state. For example, if the user feels like they want to relax, that information is sent to the server as emotional data.

[0761] The server integrates emotional information received from the terminal and preference data obtained from the user's past participation history. The server implements a "machine learning model" and generates recommended facilities suitable for the user based on this data. A machine learning platform (e.g., "any AI platform") can be used in this process. The generated facility candidates are presented to the user via the terminal, and the user selects their preference from among them.

[0762] For example, if you input a prompt such as "Please suggest a suitable facility for when I want to relax" into a generative AI model, the AI ​​can generate candidates such as spas and parks and present them to the user via the server.

[0763] In this way, users can plan events that match their own emotions and preferences via their devices. This system allows users to hold more satisfying events.

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] Users use a terminal to input basic event information and participant lists. Specifically, users enter information such as the event name, date and time, location, and participant email addresses into the terminal. This input data is sent to the server in real time. The entered data is stored in a database as basic information for event planning.

[0767] Step 2:

[0768] The device analyzes the user's facial expressions and voice to acquire emotional information. The device is equipped with a camera and microphone, and the resulting video and audio data is processed. Specifically, voice analysis software is used to detect the tone of voice, and facial recognition technology is used to analyze the user's facial expressions. As a result, the user's emotional state is quantified and sent to the server.

[0769] Step 3:

[0770] The server aggregates emotional information received from terminals and preference information obtained from participants' past participation history. The server receives this data as input and queries the database to extract participants' preferences. The obtained preference information is integrated with the user's emotional information and used as necessary information for event planning.

[0771] Step 4:

[0772] The server uses a machine learning model to generate optimal recommendation candidates. In this process, the server inputs integrated preference and sentiment information as prompts to the AI ​​model. For example, it might give the model the command "Recommend relaxing facilities," and the AI ​​generates a list of recommended facilities as output.

[0773] Step 5:

[0774] The server sends a list of recommended facilities back to the terminal and presents it to the user. The user selects a desired facility from this list via the terminal's interface. The selected facility information is sent to the server and recorded.

[0775] Step 6:

[0776] The server automatically makes facility reservations based on the user's selection. The server calls the API of an external reservation system, using the user's name and desired date and time as input, and confirms the reservation. After completion, reservation confirmation information is generated as output and notified to the user.

[0777] Step 7:

[0778] The server sends attendance confirmation messages to participants via the information network. These messages include event details and a link to ask about attendance, to which participants respond. The response data is sent to the server, where attendance information from participants is collected.

[0779] Step 8:

[0780] The server analyzes the collected attendance information and selects the optimal date. The analysis calculates the date that allows the most participants to attend, based on the attendance response data. The calculated optimal date is then finalized as output and notified to users and participants.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] In modern event management, the focus is not simply on scheduling participants' schedules, but also on providing a more satisfying service that takes into account their emotional state. Furthermore, there is a challenge to improve the quality of daily life through appropriate, real-time suggestions that consider emotions in both home and public settings.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for generating candidate optimal visiting locations based on participants' preference information, means for analyzing residents' emotional states in real time and generating appropriate suggestions based on that analysis, and means for suggesting appropriate activities or relaxation methods to residents based on the emotional analysis. This makes it possible to provide individually optimized services based on emotional states.

[0786] "Participant preference information" refers to data collected based on users' past behavior and preferences, and serves as the basis for presenting specific options and candidates.

[0787] The "optimal place to visit" is a candidate location that is predicted to provide the highest level of satisfaction for the user, taking into account the participant's preferences and emotional state.

[0788] "Automatic booking" refers to a process where, based on user selection, the system completes online bookings without requiring manual intervention.

[0789] "Event attendance confirmation" refers to the act of contacting participants via email or notification to inquire about their availability to attend a scheduled event.

[0790] "Communication infrastructure" refers to the entire network infrastructure that enables the transmission and reception of data, and includes the internet and local networks.

[0791] "Real-time analysis" refers to the ability to perform analysis and processing immediately at the moment data is generated.

[0792] "Appropriate activities or relaxation methods" refer to actions or methods recommended to promote the user's mental and physical health at that particular time, based on their emotional state.

[0793] "Automatically calculating expense settlements" refers to the process where the system automatically calculates and organizes the financial burdens that users are responsible for, and then notifies the participants of the results.

[0794] The system for realizing this invention consists of a server, an emotion recognition engine, and a terminal. The server processes participant preference information and emotion data acquired in real time, and suggests the optimal place to visit according to the participant's emotional state. The terminal has the function of receiving input from the user and displaying output from the server. The user can input necessary information via the terminal and receive suggestions from the terminal, thereby adjusting events based on emotions.

[0795] The specific technologies used in this system include emotion recognition libraries, natural language processing libraries, and machine learning frameworks. For example, the Microsoft Azure Emotion API is used for emotion recognition, acquiring emotional data from the user's facial expressions and voice. The Google Cloud Natural Language API is also used to analyze the user's text input and generate appropriate suggestions. This allows for the presentation of appropriate activities and relaxation methods based on the user's emotions.

[0796] As a concrete example, let's consider a scenario where a user who is tired after work uses this system. The server would use an emotion recognition engine to understand the user's level of fatigue and make a suggestion such as, "You must be tired. Shall we play a recommended music list to help you relax?"

[0797] Examples of prompts to input into a generative AI model include the following:

[0798] "Analyze the user's emotional state and generate suggestions for stress relief."

[0799] "Design an algorithm that suggests music and activities that help users relax."

[0800] In this way, it becomes possible to provide specific services that are tailored to the user's emotional state.

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server receives basic participant information, a participant list, and initial emotional state data from the user's terminal. This input data includes audio files and facial expression images. The server analyzes this data using an emotion recognition library (e.g., Microsoft Azure Emotion API) and outputs the user's emotional state as numerical data.

[0804] Step 2:

[0805] The server integrates the analyzed sentiment data with past participant preference information and uses a generative AI model to generate candidate optimal visiting locations. This model receives two inputs: sentiment data and preference information, and its output is a list of candidate locations.

[0806] Step 3:

[0807] The user receives a list of suggested locations to visit from the server on their device. The user then uses an intuitive interface to select a location from the list and sends that selection information to the server.

[0808] Step 4:

[0809] The server receives the user's location selection and automatically completes the reservation via the internet. The location and date / time to be reserved are determined based on the selection information and confirmed through the online reservation system.

[0810] Step 5:

[0811] The server continuously monitors the user's emotional state and suggests relaxation methods in real time. It continuously analyzes emotional data using an emotion recognition engine and, if necessary, sends automatically generated suggestions to the user's device using the Google Cloud Natural Language API.

[0812] Step 6:

[0813] Users review suggested activities and relaxation methods on their devices and select actions based on them. This information is also sent back to the server and stored as feedback. This data is used to improve the accuracy of future suggestions.

[0814] 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.

[0815] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0816] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] 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.

[0818] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0819] 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.

[0820] 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.

[0821] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0822] 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."

[0823] 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.

[0824] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0825] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A method for generating optimal visitor facility candidates based on participant preference data,

[0838] A means for automatically making a reservation for a facility selected from the aforementioned generated list of potential visiting facilities,

[0839] A means of sending event attendance confirmations to participants via a communication network,

[0840] A means of selecting a date and time that is available to the most participants, based on the responses to the attendance confirmation.

[0841] A means for automatically calculating the expense settlement for each participant and notifying the participant,

[0842] ...

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the preference data of the aforementioned participant is generated based on past event participation history.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein an artificial intelligence model is used in generating the aforementioned candidate facilities.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A means of generating the optimal choice based on participants' preference information,

[0851] A means for automatically making a reservation for a confirmed option from the generated options,

[0852] A means of sending confirmation of participation in an event to members via information and communication,

[0853] A means for selecting a time when the largest number of members can participate, based on the responses to the aforementioned participation confirmation,

[0854] A means for automatically calculating the costs for each member and notifying the relevant member,

[0855] ...

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, wherein the preference information of the participant is generated based on a history of participation in events.

[0859] (Claim 3)

[0860] The system according to claim 1, wherein an artificial intelligence model is used in generating the aforementioned options.

[0861] "Application Example 1"

[0862] (Claim 1)

[0863] A method for generating optimal visit candidates based on participant preference data,

[0864] A means of presenting the generated list of potential visits and automatically making reservations for the selected locations,

[0865] A means of sending confirmation of event participation to members via a communication network,

[0866] A means of selecting a date on which the most members can participate, based on the responses to the aforementioned confirmation of participation,

[0867] A means for automatically calculating the expenditure amount for each member and notifying the member thereof,

[0868] A means of generating location candidates tailored to preferences using prompts,

[0869] A method of payment through the platform after selection,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the preference data of the aforementioned participant is generated based on past activity history.

[0873] (Claim 3)

[0874] The system according to claim 1, which uses generative intelligence technology when generating the aforementioned visit candidates.

[0875] "Example 2 of combining an emotion engine"

[0876] (Claim 1)

[0877] A method for generating optimal recommendation candidates based on participant preference information and user sentiment information,

[0878] A means for automatically making a reservation with a provider selected from the aforementioned generated recommendation candidates,

[0879] A means of sending attendance confirmations for the plan to relevant parties via an information network,

[0880] Based on the responses to the attendance confirmation, a means of selecting a date that allows the most stakeholders to participate,

[0881] A means for automatically calculating the cost burden for each of the aforementioned parties and notifying them accordingly,

[0882] A means of measuring emotional state using user input behavior, voice, and facial expression analysis,

[0883] ...

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the preference information of the aforementioned participant is generated based on their past activity participation history.

[0887] (Claim 3)

[0888] The system according to claim 1, which uses a machine learning model when generating the aforementioned recommendation candidates.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A means for generating optimal destination candidates based on participants' preference information,

[0892] A means for automatically making a reservation for a location selected from the aforementioned generated list of potential destinations,

[0893] A means of sending event attendance confirmations to participants via a communication infrastructure,

[0894] A means of selecting a date and time that is available to the most participants, based on the responses to the attendance confirmation.

[0895] A means of analyzing residents' emotional states in real time and generating appropriate suggestions based on that analysis,

[0896] A means of suggesting appropriate activities or relaxation methods to residents based on emotional analysis,

[0897] A means for automatically calculating the expense settlement for each participant and notifying the participant,

[0898] ...

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the preference information of the aforementioned participant is generated based on their past event participation history.

[0902] (Claim 3)

[0903] The system according to claim 1, which uses an artificial intelligence model when generating the aforementioned location candidates. [Explanation of Symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for generating optimal visit candidates based on participant preference data, A means of presenting the generated list of potential visits and automatically making reservations for the selected locations, A means of sending confirmation of event participation to members via a communication network, A means of selecting a date on which the most members can participate, based on the responses to the aforementioned confirmation of participation, A means for automatically calculating the expenditure amount for each member and notifying the member thereof, A means of generating location candidates tailored to preferences using prompts, A method of payment through the platform after selection, A system that includes this.

2. The system according to claim 1, wherein the preference data of the aforementioned participant is generated based on past activity history.

3. The system according to claim 1, which uses generative intelligence technology when generating the aforementioned visit candidates.

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