system

Abstract

We provide the system. [Solution] A method for automatically selecting multiple offering candidates based on participants' past evaluation information and preferences, A means of reserving the best candidate from the selected candidates, A means of automatically obtaining attendance confirmation for an event via a communication network, A method for determining the optimal event date by integrating the schedule information of prospective participants, A means of providing participants with detailed information about the event, A means of dynamically calculating costs based on the participants' roles and providing information for payment, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The work of an organizer involves a wide variety of tasks from event planning to implementation, which require time and labor. Specifically, it includes selecting and reserving candidate offerings that match the preferences of participants, confirming attendance, making optimal schedule adjustments, guiding the meeting place, and even expense sharing and settlement operations among participants. Manually performing these series of tasks places a great burden on the organizer. Also, it is difficult to hold an event that satisfies all participants, and efficient and smooth operation is required.

Means for Solving the Problems

[0005] This system automatically selects multiple venue options based on participants' past ratings and preferences, and then reserves the most suitable option. It also automatically obtains attendance confirmations via the communication network, integrates the schedule information, and determines the optimal event date. Furthermore, it provides participants with detailed event information and dynamically calculates costs based on their roles, providing payment information. This reduces the burden on organizers and ensures event management that satisfies all participants.

[0006] "Participants" refer to individuals or groups who plan to attend the event.

[0007] "Proposal candidates" refer to multiple options presented as choices for event venues and services.

[0008] "Generative AI" refers to artificial intelligence technology that generates new information using past data and algorithms.

[0009] A "communication network" is a digital or analog data transmission system that enables the sending and receiving of information.

[0010] "Attendance confirmation" refers to a series of inquiry procedures to confirm whether or not participants will attend an event.

[0011] "Scheduling" is the process of coordinating the schedules of multiple participants to determine the most suitable date and time.

[0012] "Information about the meeting place" refers to notifying participants of the specific location and time they should gather when an event is held.

[0013] "Cost sharing" refers to the calculation and processing of how to fairly distribute event-related expenses among participants. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of the present invention provides a comprehensive solution for streamlining event planning tasks. This system enables the selection and automatic booking of event candidates based on the user's past evaluation information and preferences, automated attendance confirmation via a communication network, optimal scheduling through the integration of schedule information, guidance to meeting places, and dynamic cost calculation and payment procedures based on roles.

[0036] The server first retrieves the user's past participation history and feedback data, and then uses a generation AI to select the most suitable restaurant options. This information is then communicated to the user via their device. For example, it can suggest new restaurant options based on the types of cuisine the user has previously given high ratings to.

[0037] Next, the server uses the communication platform to send a confirmation message to the user's terminal to verify attendance. The user selects whether or not to participate on their terminal and sends that information back to the server. Based on this information, the server calculates a suitable date for the event.

[0038] Furthermore, the server determines the meeting time and place associated with the reserved service candidate and notifies the user via their terminal, ensuring that participants can gather smoothly. For example, the meeting place may be designated as the ticket gate of the nearest station or in front of a landmark.

[0039] Finally, the server calculates how to fairly distribute the event-related costs among participants and notifies each user of the payment amount via their terminal. Users can then verify the payment information on their terminal and complete the payment using their registered payment method. This ensures transparency and smooth expense settlement.

[0040] This system significantly reduces the burden on users and simplifies event planning and management, enabling event management that satisfies all participants.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects past user ratings and participation history from the database and extracts features that match the user's preferences. Based on this, it uses a generative AI to list multiple potential offerings.

[0044] Step 2:

[0045] The server notifies the user's terminal of the selected service options. The user reviews these options and selects their preferred service. The selected option is sent back to the server, which reserves it.

[0046] Step 3:

[0047] The server sends an attendance confirmation message to the user's device via the communication platform, based on the participant list obtained from the user. The user completes the attendance confirmation by selecting whether to attend or not on their device and sending that information to the server.

[0048] Step 4:

[0049] The server cross-references the calendar information of all prospective participants and calculates the event date that allows the most participants to attend. The calculated result is notified to the user's device, allowing the user to flexibly check the schedule.

[0050] Step 5:

[0051] The server determines the meeting place and time for confirmed events and sends a guidance message to the user via their device. The user can then check the route to the meeting place on their device.

[0052] Step 6:

[0053] The server automatically calculates a fair fee for each participant, taking into account their role and the event budget. The calculated amount is sent to the user's device, where they can confirm their cost and pay using their registered payment method.

[0054] Step 7:

[0055] The terminal communicates with the server after payment is complete to record that the settlement has been completed. The server updates the overall payment status, and the total payment for the event is completed. This completes all processing successfully.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In existing event planning tasks, selecting potential venues based on participants' preferences and determining the optimal event date and time are often done manually, resulting in a significant burden of time and effort. Furthermore, cost sharing and attendance confirmation are complex and difficult to process efficiently.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for automatically selecting multiple offering candidates using an information processing device based on participants' past evaluation information and preferences, means for reserving the most suitable candidate from the automatically selected offering candidates, and means for automatically acquiring event attendance information via a communication path. This reduces the burden on the organizer and enables the holding of efficient and highly satisfying events.

[0061] "Participants" refers to individuals or groups of people who are planning or intending to participate in an event.

[0062] "Evaluation information" refers to feedback, scores, and evaluation comments given by participants to past events and services.

[0063] "Preferences" refer to the tastes and interests that participants have in a particular genre or category.

[0064] An "information processing device" refers to a computer or server system used to perform calculations and analyses of data and execute specific processes.

[0065] "Potential offerings" refer to potential events or services recommended based on participants' preferences and evaluation information.

[0066] "Communication path" refers to the means and protocols for transmitting information to a remote location, including the internet and mobile networks.

[0067] "Attendance information" refers to the status or decision of whether or not a participant will attend a particular event.

[0068] "Reservation procedures" refer to a series of actions taken to secure the right to use a specific service or event in advance.

[0069] The present invention provides technology for efficiently managing and coordinating events. This system consists of interactions between servers, terminals, and users. The system's operation is described in detail below.

[0070] First, the server acquires the user's past evaluation information and preference data using an information processing device. This involves using a database management system and SQL queries. Then, the server processes this data using a generative AI model to select the most suitable candidates for the user. This AI model analyzes the user's preference patterns using machine learning techniques. For example, it generates a new list of candidates by referring to events and services that have received high ratings in the past.

[0071] Next, the server notifies the terminal of the selected candidate information via the communication path. The terminal receives this information and displays it in the user interface. This process is carried out via a REST API and JSON format. The server also uses a communication platform to notify the user's terminal of attendance confirmation. Using the Twilio API and other services, messages are delivered via SMS or push notifications. The user responds to the attendance confirmation on their terminal, and that information is sent back to the server.

[0072] The server calculates the optimal event date based on the attendance information it has collected. An algorithm uses the Calendar API to aggregate participants' availability and determine the best date and time. Furthermore, the server determines the meeting time and location for the event and transmits these details to the participants' devices. The Maps API can be used for this process.

[0073] Finally, the server calculates how to distribute the event costs among the participants and provides payment information to the user via the terminal. This process utilizes a cost-sharing algorithm and a payment API. The user can verify the amount on the terminal and complete the payment via the link.

[0074] As a concrete example, consider a scenario where a user plans a dinner event with friends. The system suggests suitable restaurant options based on the user's past ratings, adjusts the date while considering everyone's schedules, and then fairly calculates and notifies each participant of their share of the costs. This entire process can be triggered by a prompt, such as, "Based on the user's past event participation history and feedback, please suggest the best restaurant options for the next event. I will also optimize the event date and location."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server retrieves participants' past evaluation information and preference data. Participant IDs are provided as input, and the corresponding evaluation information and preference data are extracted as output. Specifically, it uses a database management system to execute SQL queries to collect the necessary information.

[0078] Step 2:

[0079] The server inputs the acquired evaluation information and preference data into a generating AI model. Based on the input data, the model analyzes the data and selects the most suitable offering candidates. A list of candidates is generated as output. In its specific operation, a Python script is executed, and a machine learning model is used to perform scoring and ranking.

[0080] Step 3:

[0081] The server sends the selected candidate offerings to the terminal. The input is a list of candidates, and the output is the terminal displaying the candidates. Specifically, data is sent to the terminal in JSON format using a REST API, and the terminal parses this data and displays it in a GUI.

[0082] Step 4:

[0083] The server uses the communication path to send attendance confirmations to the device. The input requires a list of potential event attendees, and the output is the attendance response from each participant. Specifically, the Twilio API is used to send SMS or push notifications, and the device then sends its selection back to the server.

[0084] Step 5:

[0085] The server calculates the optimal event date based on attendance information. It requires attendance responses from all participants and their associated availability information as input, and outputs the identified optimal date and time. Specifically, it uses a calendar API to execute an algorithm that identifies common availability periods.

[0086] Step 6:

[0087] The server determines the meeting time and place for the event and notifies the terminal. The input is the reserved service candidates and the participants' location information, and the output is instructions for the meeting time and place. Specifically, it uses a map API to select the optimal geographical point and sends the notification.

[0088] Step 7:

[0089] The server calculates the costs associated with the event and provides payment information to participants via their devices. Input data includes the number of participants and the total event cost, and the output calculates the individual payment amounts. Specifically, it uses a cost distribution algorithm, generates payment links using the Stripe API, and sends them to the devices.

[0090] (Application Example 1)

[0091] 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."

[0092] In event planning and management, it is essential to accurately understand participants' needs, efficiently schedule events, and create events that are highly satisfying for all participants. However, current systems struggle to effectively utilize past data and automate the provision of dynamic information, resulting in a significant burden on participants.

[0093] 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.

[0094] This invention includes a server that automatically selects multiple event candidates based on participants' past evaluation information and preferences, provides participants with detailed event information and dynamically guides them to meeting places, and dynamically calculates costs based on participants' roles and provides information for electronic payment. This enables comprehensive management from event planning to operation, maximizing participant satisfaction while achieving efficient event management.

[0095] "Past participant evaluation information" refers to data on feedback and satisfaction levels from participants regarding past events.

[0096] "Preferences" refer to specific tastes or tendencies that participants exhibit based on their past experiences and choices.

[0097] "Event candidates" refers to the options and plans of events that can be offered to participants.

[0098] A "communication network" is a technical infrastructure for sending and receiving digital data between users and servers.

[0099] "Schedule information" refers to information about each participant's schedule and available time, as provided by those planning to attend the event.

[0100] "Meeting place instructions" refer to instructions provided for participants to gather at a specific location designated before the event.

[0101] "Role-based cost calculation" is a method of calculating event expenses to fairly distribute them according to the roles and contributions of the participants.

[0102] "Electronic payment" is a payment method that uses digital technology to send and receive funds online.

[0103] "Generative AI" is a form of artificial intelligence that has the ability to learn from large amounts of data and automatically perform reasoning and decision-making.

[0104] The system for realizing this invention operates via a server and a user's terminal. The server first automatically selects multiple event candidates based on the participant's past evaluation information and preferences. By using a generative AI model, it is possible to prioritize the optimal event candidates for each individual participant.

[0105] The user's device receives event candidate information from the server and presents it to the user. In this process, the user sends an indication of whether or not they can attend the event from their device to the server, which then automatically checks attendance via the communication network.

[0106] Next, the server integrates the participants' schedule information and uses a generating AI to determine the optimal event date. This information is then communicated to the participants via their devices.

[0107] Furthermore, the server provides participants' devices with detailed event information and dynamic directions to the meeting place. Map services and location information can be used to guide participants to the meeting place.

[0108] Finally, the server dynamically calculates the cost based on the participant's role and provides electronic payment information to the terminal. This allows participants to easily complete payment using their own devices. The smooth event management enabled by this system ensures a satisfying experience for all participants.

[0109] As a concrete example, it is possible to recommend new events based on the genres of events that a user has previously given high ratings to. An example of a prompt to the generative AI model would be, "Please suggest new events similar to the event genres that user A has previously given high ratings to."

[0110] This invention enables the efficient implementation of events based on the needs of participants.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server takes the user's past evaluation information and preferences as input and automatically selects multiple event candidates using a generative AI model. During this process, the server inputs prompt messages into the AI ​​model based on the participant's past feedback and participation history, outputting a list of optimal events.

[0114] Step 2:

[0115] The terminal presents the user with event candidate information received from the server. The user then selects whether or not to participate based on this information and sends that information back to the server. Here, the terminal displays event candidate data as input, and the user's decision is returned as output.

[0116] Step 3:

[0117] The server uses the received attendance confirmation information to verify attendance via the communication network and integrates participants' schedule information. During this process, it calculates and outputs the optimal date for the event based on several confirmed schedules.

[0118] Step 4:

[0119] The server uses a generative AI model to determine the optimal event date from the integrated schedule information. In this process, the schedule information is presented to the model as input, and the optimal date is obtained as output.

[0120] Step 5:

[0121] The server provides users with detailed event information and dynamic meeting place directions via their terminals. Specifically, it uses location data from a map service as input and provides output to guide users to the meeting place.

[0122] Step 6:

[0123] The server dynamically calculates the cost based on the participant's role and sends the information for electronic payment to the terminal. Inputs include the participant's role and cost, and the calculated payment amount is output and notified to the terminal.

[0124] Step 7:

[0125] The user completes the participation fee by making an electronic payment from their terminal. Here, the payment information is confirmed as input, and the payment processing is output.

[0126] 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.

[0127] The present invention incorporates an emotion engine to further personalize the tasks of event organizers and improve participant satisfaction. This system utilizes the emotion engine to add new functions to enable the selection of event candidates based on participants' past evaluations and preferences, automatic booking, attendance confirmation via a communication network, optimal scheduling through integration of schedule information, guidance to meeting places, and cost calculation and payment information provision based on roles.

[0128] The server first uses an emotion engine to acquire and analyze the user's past participation history and real-time emotion data. This data includes emotions the user expressed during or immediately after past events, as well as their current mood. This allows the generation AI to select more accurate recommendations that reflect the user's current emotions. For example, it can output selection results that include shops that showed high satisfaction in past events, as well as a variety of other options.

[0129] Next, during the event, the server uses an emotion engine to monitor participants' emotional data in real time and measure their satisfaction level. This result can be used as feedback to adjust the service and atmosphere during the event.

[0130] Furthermore, the server utilizes emotional data in the area of ​​cost sharing. It adjusts the cost burden among participants to ensure an emotionally fair distribution, taking into account the emotional burden associated with specific roles and obligations. This allows users to settle their accounts at a price they find acceptable, and promotes the smooth operation of the event as a whole.

[0131] Thus, this system, which incorporates an emotion engine, not only streamlines the tasks of event organizers but also enables event management that is attentive to the emotions of each participant, thereby improving everyone's satisfaction.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects past event participation history and real-time status data from the user's device. Using an emotion engine, it analyzes the user's preferences and current emotional state from this data and generates individually customized suggestions.

[0135] Step 2:

[0136] The server sends the generated list of potential offers to the user's device. The user reviews the list of options on the device and selects the offers they are interested in. The selection results are sent back to the server, which then uses this information to confirm the reservation.

[0137] Step 3:

[0138] The server sends an attendance confirmation message to the terminal via the communication network. The user receives this message, selects their intention to participate on their terminal, and replies to the server. This information is aggregated on the server.

[0139] Step 4:

[0140] The server integrates participants' schedule information and determines the most convenient date. The determined date is then adjusted using an emotion engine to align with the participants' past emotional tendencies. The server notifies the participants of the date on their devices.

[0141] Step 5:

[0142] On the day of the event, the server uses an emotion engine to measure participants' emotions in real time. This data is used as emotional feedback for each participant via their device, and the environment and services are adjusted as needed during the event.

[0143] Step 6:

[0144] After the event ends, the server uses an emotion engine to analyze participant emotional data and calculate a cost distribution that minimizes the psychological burden on each user. The calculation results are sent to the user's device, where they complete the payment.

[0145] Step 7:

[0146] Once settlement is complete, the terminal notifies the server and terminates all processes. The server generates a final event report and sends it to each participant, officially concluding the event.

[0147] (Example 2)

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

[0149] In events involving multiple participants, maximizing individual participant satisfaction requires not only selecting offerings based on past evaluations and preferences, but also responding to real-time changes in emotions. Furthermore, cost sharing that appropriately considers the burden on participants is essential. However, comprehensively managing and achieving all of these elements in event management is challenging.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] This invention includes a server that acquires emotional data and adjusts the environment in real time during the event based on the participants' emotions; a server that uses generative AI to combine participants' past data with their real-time emotional state to select more accurate candidates for provision; and a server that dynamically adjusts cost sharing while taking into account the emotional burden of the participants. This enables effective event management based on the individual emotions of each participant and cost sharing that is less burdensome and considerate of their emotions.

[0152] "Emotional data" refers to information obtained as numerical or text data about participants' emotional states during past events or in real time, and converted into an analyzable format.

[0153] "Generative AI" is an artificial intelligence technology that uses historical data and real-time sentiment data to make optimal selections and adjustments for specific tasks.

[0154] "Proposal candidates" refer to options such as restaurants and activities selected to enhance participant satisfaction when hosting an event.

[0155] "Real-time adjustment" is a process that instantly reflects changes in participants' emotional data during an event, optimizing the environment and service content accordingly.

[0156] "Emotional burden" refers to the degree of psychological stress or dissatisfaction that participants experience as a result of fulfilling a specific role or as a result of events during an event.

[0157] "Cost sharing" is a system in which the expenses that each individual participating in an event should pay are distributed based on rules and standards agreed upon among the participants.

[0158] This system is a platform that utilizes emotional data to streamline event management and improve participant satisfaction. The following elements are used during implementation:

[0159] The server utilizes cloud-based services for primary analysis and data management. Specifically, it leverages sentiment analysis APIs provided by cloud providers to obtain participants' past event history and real-time sentiment data. This allows for trend analysis based on each user's past evaluation information and preferences.

[0160] The generative AI model selects potential event offerings based on the aforementioned data. These offerings include facilities and activities that users have previously shown high satisfaction with, and the AI ​​combines them to make optimal suggestions. For example, by inputting a prompt such as, "Please suggest restaurants that received high ratings at past events as options for the next event," the AI ​​will return appropriate suggestions.

[0161] During the event, the terminal collects real-time emotional data using the user's smart device. This data is transmitted to a server via Bluetooth or Wi-Fi, which uses this information to adjust the environment according to the event's progress. This allows for flexible adjustments to music selection and lighting.

[0162] After the event, users can check the cost sharing results via their devices. The server considers the roles and emotional burdens of participants based on sentiment data to ensure a fair cost distribution. In this way, all participants are satisfied with the settlement.

[0163] The series of operations of this system provides event organizers with a flexible, emotion-focused management approach, enabling them to respond to the emotions of each individual participant and thereby improve overall event satisfaction.

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

[0165] Step 1:

[0166] The server uses cloud services to collect participants' past event participation history and analyzes it in combination with real-time sentiment data. It takes user evaluation information and event-specific sentiment data as input and generates sentiment profiles as output. Specifically, a cloud-based sentiment analysis API extracts sentiment data from photos and social media posts and stores it in a database. This data is then prepared for advanced analysis used in the next step.

[0167] Step 2:

[0168] A generative AI model runs on the server and selects the most suitable candidates based on the collected data. The AI ​​receives the emotional profile and past evaluation information obtained in the previous step as input and provides a list of optimal event candidates as output. Specifically, the AI ​​processes a prompt such as, "Please list restaurants that received high ratings at past events as candidates for the next event," and relevant candidates are selected.

[0169] Step 3:

[0170] The terminal collects real-time emotional data during the event using the user's smart device. It receives user facial expressions and voice data as input via an emotion analysis sensor, and sends an emotional evaluation of that moment to the server as output. Specifically, the terminal transfers emotional information from each user's smartphone to the server via Bluetooth or Wi-Fi, which can then be used to adjust the event environment.

[0171] Step 4:

[0172] The server uses real-time emotional data to adjust the event environment. It receives emotional ratings from each participant as input and generates optimal event environment settings as output. Specifically, it can instruct changes to music and lighting settings, creating a system to enhance the participant experience.

[0173] Step 5:

[0174] The server takes into account the emotional burden on participants when determining cost sharing. It takes emotional data and role assignment information from each participant as input and calculates a fair cost-sharing result as output. For example, it analyzes the emotional stress of the user who acted as the moderator and adjusts the cost accordingly.

[0175] Step 6:

[0176] Users receive the cost sharing results from their device after the event ends. The system receives settlement information sent from the server as input and outputs payment confirmation and adjustments. Specifically, users check the settlement results on their device and, if they have any questions, communicate with the event organizer through the digital platform.

[0177] (Application Example 2)

[0178] 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".

[0179] Planning and running an event is challenging because it accurately reflects the preferences and emotions of all participants and maximizes individual satisfaction. Furthermore, it's necessary to reflect real-time emotional shifts and appropriately adjust the event's atmosphere, but existing systems cannot efficiently accomplish this.

[0180] 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.

[0181] This invention includes a server that automatically selects multiple offering candidates based on participants' past evaluation information and preferences, a means for acquiring participants' emotional data using an emotion engine and monitoring their satisfaction level in real time during the event, and a means for adjusting the service and atmosphere during the event while taking participants' emotions into consideration. This makes it possible to manage events in a way that is attentive to the individual emotions of each participant.

[0182] "Past participant evaluation information" refers to data that records and collects opinions and impressions previously expressed by event participants.

[0183] "Preferences" refer to the elements or conditions that individual participants are particularly interested in, and these serve as criteria for selection and proposals.

[0184] "Possible options" refer to multiple choices or options that are considered when organizing an event.

[0185] "Booking the best option" means determining the most suitable choice based on the participants' conditions and circumstances, and securing the venue and services accordingly.

[0186] A "communication network" is a digital infrastructure used for sending and receiving information.

[0187] "Event attendance confirmation" refers to the act of confirming whether prospective participants intend to attend or not attend an event.

[0188] "Schedule information" is a general term for all temporal information regarding individual participants and the dates and times of events.

[0189] An "emotional engine" is a technology that analyzes participants' emotions and moods and provides that information.

[0190] "Real-time monitoring" means the act of observing and understanding the current situation and data in real time.

[0191] "Adjusting the atmosphere of an event" refers to changing the environment and services offered in accordance with the emotions and circumstances of the participants, in order to provide a more appropriate experience.

[0192] In the system for realizing this invention, the server automatically selects multiple potential events based on each participant's past evaluation information and preferences. The system then determines the best option from the selected candidates and makes a reservation via a communication network. Furthermore, it automatically obtains attendance confirmations for events and integrates the schedule information of prospective participants to determine the optimal event date.

[0193] The server utilizes an emotion engine to monitor participants' emotions in real time. This allows for adjustments to the atmosphere and services during the event to enhance participant satisfaction. Furthermore, it facilitates smooth event operation by using emotion data to ensure fair cost sharing and provide necessary payment information.

[0194] The implementation will utilize devices such as smart assistant robots. From a software perspective, it is crucial to analyze participants' past emotional data using generative AI models and emotion engine APIs to provide real-time feedback.

[0195] For example, in the case of a home party with friends in the neighborhood, the system suggests the most suitable party theme based on past reviews from participants and automatically makes the necessary reservations. During the event, the device analyzes the participants' emotions and makes adjustments such as playing music if a relaxed atmosphere is needed. This kind of operation improves participant satisfaction.

[0196] Example prompt: "Can you suggest a theme for our next party? Please consider the participants' past event reviews and recent moods when making your choice."

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

[0198] Step 1:

[0199] The server retrieves data from a database of past event participant ratings and preferences. The input is participant identification information, which is used to search for relevant past event ratings. The output is rating information and preference data organized for each participant. This enables the selection of personalized offerings.

[0200] Step 2:

[0201] The server uses a generative AI model to select multiple offering candidates that are best suited to the participant from the input data. This process involves data processing to reflect past evaluation information and participant preferences, and prioritization. The output is a list of recommended offering candidates, which prompts the user to make a selection.

[0202] Step 3:

[0203] The server automatically reserves the most suitable option from the selected candidates. The input is a list of candidates selected by the user, and the output is confirmation information for the reservation completion. This includes details of the reservation and the schedule based on it.

[0204] Step 4:

[0205] The server automatically obtains attendance confirmations from users via the communication network. Inputs include a participant list and attendance confirmation messages for each participant. Output is a list of participants who have confirmed their attendance. Based on this, scheduling adjustments are made.

[0206] Step 5:

[0207] The server integrates participants' schedule information to determine the optimal event date. The input is schedule data collected from multiple participants, and the output is the optimal event date. This allows for efficient scheduling.

[0208] Step 6:

[0209] The device utilizes an emotion engine to acquire and analyze participants' emotional data in real time during an event. The input is the emotional data collected from participants in real time, and the output is an analysis result showing emotional trends. Based on this, feedback for the event can be obtained.

[0210] Step 7:

[0211] The server adjusts the service and atmosphere during an event based on emotional data. The input is the emotional trend analyzed by the emotion engine, and the output is a specific action plan for the necessary adjustments. This enables concrete measures to improve participant satisfaction.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

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

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] The system of the present invention provides a comprehensive solution for streamlining event planning tasks. This system enables the selection and automatic booking of event candidates based on the user's past evaluation information and preferences, automated attendance confirmation via a communication network, optimal scheduling through the integration of schedule information, guidance to meeting places, and dynamic cost calculation and payment procedures based on roles.

[0229] The server first retrieves the user's past participation history and feedback data, and then uses a generation AI to select the most suitable restaurant options. This information is then communicated to the user via their device. For example, it can suggest new restaurant options based on the types of cuisine the user has previously given high ratings to.

[0230] Next, the server uses the communication platform to send a confirmation message to the user's terminal to verify attendance. The user selects whether or not to participate on their terminal and sends that information back to the server. Based on this information, the server calculates a suitable date for the event.

[0231] Furthermore, the server determines the meeting time and place associated with the reserved service candidate and notifies the user via their terminal, ensuring that participants can gather smoothly. For example, the meeting place may be designated as the ticket gate of the nearest station or in front of a landmark.

[0232] Finally, the server calculates how to fairly distribute the event-related costs among participants and notifies each user of the payment amount via their terminal. Users can then verify the payment information on their terminal and complete the payment using their registered payment method. This ensures transparency and smooth expense settlement.

[0233] This system significantly reduces the burden on users and simplifies event planning and management, enabling event management that satisfies all participants.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The server collects past user ratings and participation history from the database and extracts features that match the user's preferences. Based on this, it uses a generative AI to list multiple potential offerings.

[0237] Step 2:

[0238] The server notifies the user's terminal of the selected service options. The user reviews these options and selects their preferred service. The selected option is sent back to the server, which reserves it.

[0239] Step 3:

[0240] The server sends an attendance confirmation message to the user's device via the communication platform, based on the participant list obtained from the user. The user completes the attendance confirmation by selecting whether to attend or not on their device and sending that information to the server.

[0241] Step 4:

[0242] The server cross-references the calendar information of all prospective participants and calculates the event date that allows the most participants to attend. The calculated result is notified to the user's device, allowing the user to flexibly check the schedule.

[0243] Step 5:

[0244] The server determines the meeting place and time for confirmed events and sends a guidance message to the user via their device. The user can then check the route to the meeting place on their device.

[0245] Step 6:

[0246] The server automatically calculates a fair fee for each participant, taking into account their role and the event budget. The calculated amount is sent to the user's device, where they can confirm their cost and pay using their registered payment method.

[0247] Step 7:

[0248] The terminal communicates with the server after payment is complete to record that the settlement has been completed. The server updates the overall payment status, and the total payment for the event is completed. This completes all processing successfully.

[0249] (Example 1)

[0250] 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."

[0251] In existing event planning tasks, selecting potential venues based on participants' preferences and determining the optimal event date and time are often done manually, resulting in a significant burden of time and effort. Furthermore, cost sharing and attendance confirmation are complex and difficult to process efficiently.

[0252] 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.

[0253] In this invention, the server includes means for automatically selecting multiple offering candidates using an information processing device based on participants' past evaluation information and preferences, means for reserving the most suitable candidate from the automatically selected offering candidates, and means for automatically acquiring event attendance information via a communication path. This reduces the burden on the organizer and enables the holding of efficient and highly satisfying events.

[0254] "Participants" refers to individuals or groups of people who are planning or intending to participate in an event.

[0255] "Evaluation information" refers to feedback, scores, and evaluation comments given by participants to past events and services.

[0256] "Preferences" refer to the tastes and interests that participants have in a particular genre or category.

[0257] An "information processing device" refers to a computer or server system used to perform calculations and analyses of data and execute specific processes.

[0258] "Potential offerings" refer to potential events or services recommended based on participants' preferences and evaluation information.

[0259] "Communication path" refers to the means and protocols for transmitting information to a remote location, including the internet and mobile networks.

[0260] "Attendance information" refers to the status or decision of whether or not a participant will attend a particular event.

[0261] "Reservation procedures" refer to a series of actions taken to secure the right to use a specific service or event in advance.

[0262] The present invention provides technology for efficiently managing and coordinating events. This system consists of interactions between servers, terminals, and users. The system's operation is described in detail below.

[0263] First, the server acquires the user's past evaluation information and preference data using an information processing device. This involves using a database management system and SQL queries. Then, the server processes this data using a generative AI model to select the most suitable candidates for the user. This AI model analyzes the user's preference patterns using machine learning techniques. For example, it generates a new list of candidates by referring to events and services that have received high ratings in the past.

[0264] Next, the server notifies the terminal of the selected candidate information via the communication path. The terminal receives this information and displays it in the user interface. This process is carried out via a REST API and JSON format. The server also uses a communication platform to notify the user's terminal of attendance confirmation. Using the Twilio API and other services, messages are delivered via SMS or push notifications. The user responds to the attendance confirmation on their terminal, and that information is sent back to the server.

[0265] The server calculates the optimal event date based on the attendance information it has collected. An algorithm uses the Calendar API to aggregate participants' availability and determine the best date and time. Furthermore, the server determines the meeting time and location for the event and transmits these details to the participants' devices. The Maps API can be used for this process.

[0266] Finally, the server calculates how to distribute the event costs among the participants and provides payment information to the user via the terminal. This process utilizes a cost-sharing algorithm and a payment API. The user can verify the amount on the terminal and complete the payment via the link.

[0267] As a concrete example, consider a scenario where a user plans a dinner event with friends. The system suggests suitable restaurant options based on the user's past ratings, adjusts the date while considering everyone's schedules, and then fairly calculates and notifies each participant of their share of the costs. This entire process can be triggered by a prompt, such as, "Based on the user's past event participation history and feedback, please suggest the best restaurant options for the next event. I will also optimize the event date and location."

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

[0269] Step 1:

[0270] The server retrieves participants' past evaluation information and preference data. Participant IDs are provided as input, and the corresponding evaluation information and preference data are extracted as output. Specifically, it uses a database management system to execute SQL queries to collect the necessary information.

[0271] Step 2:

[0272] The server inputs the acquired evaluation information and preference data into a generating AI model. Based on the input data, the model analyzes the data and selects the most suitable offering candidates. A list of candidates is generated as output. In its specific operation, a Python script is executed, and a machine learning model is used to perform scoring and ranking.

[0273] Step 3:

[0274] The server sends the selected candidate offerings to the terminal. The input is a list of candidates, and the output is the terminal displaying the candidates. Specifically, data is sent to the terminal in JSON format using a REST API, and the terminal parses this data and displays it in a GUI.

[0275] Step 4:

[0276] The server uses the communication path to send attendance confirmations to the device. The input requires a list of potential event attendees, and the output is the attendance response from each participant. Specifically, the Twilio API is used to send SMS or push notifications, and the device then sends its selection back to the server.

[0277] Step 5:

[0278] The server calculates the optimal event date based on attendance information. It requires attendance responses from all participants and their associated availability information as input, and outputs the identified optimal date and time. Specifically, it uses a calendar API to execute an algorithm that identifies common availability periods.

[0279] Step 6:

[0280] The server determines the meeting time and place for the event and notifies the terminal. The input is the reserved service candidates and the participants' location information, and the output is instructions for the meeting time and place. Specifically, it uses a map API to select the optimal geographical point and sends the notification.

[0281] Step 7:

[0282] The server calculates the costs related to the event and provides payment information to the participants through the terminal. The input data includes the number of participants and the total cost of the event, and the individual payment amounts are calculated as the output. Specifically, it uses a cost allocation algorithm, generates a payment link using the Stripe API, and performs the operation of sending it to the terminal.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In the planning and operation of an event, it is required to accurately grasp the needs of the participants, schedule efficiently, and realize an event with a high satisfaction level for all participants. However, in the current system, it is difficult to effectively utilize past data and automate up to dynamic information provision, and there is a problem that the burden on the participants is large.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for automatically selecting a plurality of event candidates based on the past evaluation information and preferences of the participants, means for providing the participants with detailed information about the event and dynamically guiding the gathering place, and means for dynamically calculating the costs based on the roles of the participants and providing information for electronic settlement. Thereby, it becomes possible to comprehensively manage from the planning to the operation of the event, improve the satisfaction level of the participants, and realize efficient event management.

[0288] The "past evaluation information of the participants" is data regarding the feedback and satisfaction from the participants for the events held in the past.

[0289] "Preferences" refer to specific tastes or tendencies that participants exhibit based on their past experiences and choices.

[0290] "Event candidates" refers to the options and plans of events that can be offered to participants.

[0291] A "communication network" is a technical infrastructure for sending and receiving digital data between users and servers.

[0292] "Schedule information" refers to information about each participant's schedule and available time, as provided by those planning to attend the event.

[0293] "Meeting place instructions" refer to instructions provided for participants to gather at a specific location designated before the event.

[0294] "Role-based cost calculation" is a method of calculating event expenses to fairly distribute them according to the roles and contributions of the participants.

[0295] "Electronic payment" is a payment method that uses digital technology to send and receive funds online.

[0296] "Generative AI" is a form of artificial intelligence that has the ability to learn from large amounts of data and automatically perform reasoning and decision-making.

[0297] The system for realizing this invention operates via a server and a user's terminal. The server first automatically selects multiple event candidates based on the participant's past evaluation information and preferences. By using a generative AI model, it is possible to prioritize the optimal event candidates for each individual participant.

[0298] The user's device receives event candidate information from the server and presents it to the user. In this process, the user sends an indication of whether or not they can attend the event from their device to the server, which then automatically checks attendance via the communication network.

[0299] Next, the server integrates the schedule information of the participants and determines the optimal event date using the generative AI. This information is notified to the participants through the terminal.

[0300] Furthermore, the server provides the participants' terminals with detailed event information and guidance on dynamic gathering places. In the guidance on gathering places, map services and location information can be utilized.

[0301] Finally, the server dynamically calculates the fees based on the participants' roles and provides the terminal with electronic payment information. As a result, the participants can easily complete the payment using their own terminals. The smooth event operation realized by this system provides an experience that satisfies all participants.

[0302] As a specific example, it is possible to recommend new events based on the genres of events that the user has highly evaluated in the past. An example of a prompt sentence for the generative AI model is "Please propose a new event similar to the event genres that User A has highly evaluated in the past."

[0303] Thus, the present invention realizes the efficient implementation of events based on the needs of the participants.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server acquires the user's past evaluation information and preferences as inputs and automatically selects a plurality of event candidates using the generative AI model. At this time, as data processing, based on the participants' past feedback and participation history, a prompt sentence is input into the AI model, and a list of optimal events is output.

[0307] Step 2:

[0308] The terminal presents the user with event candidate information received from the server. The user then selects whether or not to participate based on this information and sends that information back to the server. Here, the terminal displays event candidate data as input, and the user's decision is returned as output.

[0309] Step 3:

[0310] The server uses the received attendance confirmation information to verify attendance via the communication network and integrates participants' schedule information. During this process, it calculates and outputs the optimal date for the event based on several confirmed schedules.

[0311] Step 4:

[0312] The server uses a generative AI model to determine the optimal event date from the integrated schedule information. In this process, the schedule information is presented to the model as input, and the optimal date is obtained as output.

[0313] Step 5:

[0314] The server provides users with detailed event information and dynamic meeting place directions via their terminals. Specifically, it uses location data from a map service as input and provides output to guide users to the meeting place.

[0315] Step 6:

[0316] The server dynamically calculates the cost based on the participant's role and sends the information for electronic payment to the terminal. Inputs include the participant's role and cost, and the calculated payment amount is output and notified to the terminal.

[0317] Step 7:

[0318] The user completes the participation fee by making an electronic payment from their terminal. Here, the payment information is confirmed as input, and the payment processing is output.

[0319] 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.

[0320] The present invention incorporates an emotion engine to further personalize the tasks of event organizers and improve participant satisfaction. This system utilizes the emotion engine to add new functions to enable the selection of event candidates based on participants' past evaluations and preferences, automatic booking, attendance confirmation via a communication network, optimal scheduling through integration of schedule information, guidance to meeting places, and cost calculation and payment information provision based on roles.

[0321] The server first uses an emotion engine to acquire and analyze the user's past participation history and real-time emotion data. This data includes emotions the user expressed during or immediately after past events, as well as their current mood. This allows the generation AI to select more accurate recommendations that reflect the user's current emotions. For example, it can output selection results that include shops that showed high satisfaction in past events, as well as a variety of other options.

[0322] Next, during the event, the server uses an emotion engine to monitor participants' emotional data in real time and measure their satisfaction level. This result can be used as feedback to adjust the service and atmosphere during the event.

[0323] Furthermore, the server utilizes emotional data in the area of ​​cost sharing. It adjusts the cost burden among participants to ensure an emotionally fair distribution, taking into account the emotional burden associated with specific roles and obligations. This allows users to settle their accounts at a price they find acceptable, and promotes the smooth operation of the event as a whole.

[0324] Thus, this system, which incorporates an emotion engine, not only streamlines the tasks of event organizers but also enables event management that is attentive to the emotions of each participant, thereby improving everyone's satisfaction.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The server collects past event participation history and real-time status data from the user's device. Using an emotion engine, it analyzes the user's preferences and current emotional state from this data and generates individually customized suggestions.

[0328] Step 2:

[0329] The server sends the generated list of potential offers to the user's device. The user reviews the list of options on the device and selects the offers they are interested in. The selection results are sent back to the server, which then uses this information to confirm the reservation.

[0330] Step 3:

[0331] The server sends an attendance confirmation message to the terminal via the communication network. The user receives this message, selects their intention to participate on their terminal, and replies to the server. This information is aggregated on the server.

[0332] Step 4:

[0333] The server integrates participants' schedule information and determines the most convenient date. The determined date is then adjusted using an emotion engine to align with the participants' past emotional tendencies. The server notifies the participants of the date on their devices.

[0334] Step 5:

[0335] On the day of the event, the server uses an emotion engine to measure participants' emotions in real time. This data is used as emotional feedback for each participant via their device, and the environment and services are adjusted as needed during the event.

[0336] Step 6:

[0337] After the event ends, the server uses an emotion engine to analyze participant emotional data and calculate a cost distribution that minimizes the psychological burden on each user. The calculation results are sent to the user's device, where they complete the payment.

[0338] Step 7:

[0339] Once settlement is complete, the terminal notifies the server and terminates all processes. The server generates a final event report and sends it to each participant, officially concluding the event.

[0340] (Example 2)

[0341] 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".

[0342] In events involving multiple participants, maximizing individual participant satisfaction requires not only selecting offerings based on past evaluations and preferences, but also responding to real-time changes in emotions. Furthermore, cost sharing that appropriately considers the burden on participants is essential. However, comprehensively managing and achieving all of these elements in event management is challenging.

[0343] 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.

[0344] This invention includes a server that acquires emotional data and adjusts the environment in real time during the event based on the participants' emotions; a server that uses generative AI to combine participants' past data with their real-time emotional state to select more accurate candidates for provision; and a server that dynamically adjusts cost sharing while taking into account the emotional burden of the participants. This enables effective event management based on the individual emotions of each participant and cost sharing that is less burdensome and considerate of their emotions.

[0345] "Emotional data" refers to information obtained as numerical or text data about participants' emotional states during past events or in real time, and converted into an analyzable format.

[0346] "Generative AI" is an artificial intelligence technology that uses historical data and real-time sentiment data to make optimal selections and adjustments for specific tasks.

[0347] "Proposal candidates" refer to options such as restaurants and activities selected to enhance participant satisfaction when hosting an event.

[0348] "Real-time adjustment" is a process that instantly reflects changes in participants' emotional data during an event, optimizing the environment and service content accordingly.

[0349] "Emotional burden" refers to the degree of psychological stress or dissatisfaction that participants experience as a result of fulfilling a specific role or as a result of events during an event.

[0350] "Cost sharing" is a system in which the expenses that each individual participating in an event should pay are distributed based on rules and standards agreed upon among the participants.

[0351] This system is a platform that utilizes emotional data to streamline event management and improve participant satisfaction. The following elements are used during implementation:

[0352] The server utilizes cloud-based services for primary analysis and data management. Specifically, it leverages sentiment analysis APIs provided by cloud providers to obtain participants' past event history and real-time sentiment data. This allows for trend analysis based on each user's past evaluation information and preferences.

[0353] The generative AI model selects potential event offerings based on the aforementioned data. These offerings include facilities and activities that users have previously shown high satisfaction with, and the AI ​​combines them to make optimal suggestions. For example, by inputting a prompt such as, "Please suggest restaurants that received high ratings at past events as options for the next event," the AI ​​will return appropriate suggestions.

[0354] During the event, the terminal collects real-time emotional data using the user's smart device. This data is transmitted to a server via Bluetooth or Wi-Fi, which uses this information to adjust the environment according to the event's progress. This allows for flexible adjustments to music selection and lighting.

[0355] After the event, users can check the cost sharing results via their devices. The server considers the roles and emotional burdens of participants based on sentiment data to ensure a fair cost distribution. In this way, all participants are satisfied with the settlement.

[0356] The series of operations of this system provides event organizers with a flexible, emotion-focused management approach, enabling them to respond to the emotions of each individual participant and thereby improve overall event satisfaction.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The server uses cloud services to collect participants' past event participation history and analyzes it in combination with real-time sentiment data. It takes user evaluation information and event-specific sentiment data as input and generates sentiment profiles as output. Specifically, a cloud-based sentiment analysis API extracts sentiment data from photos and social media posts and stores it in a database. This data is then prepared for advanced analysis used in the next step.

[0360] Step 2:

[0361] A generative AI model runs on the server and selects the most suitable candidates based on the collected data. The AI ​​receives the emotional profile and past evaluation information obtained in the previous step as input and provides a list of optimal event candidates as output. Specifically, the AI ​​processes a prompt such as, "Please list restaurants that received high ratings at past events as candidates for the next event," and relevant candidates are selected.

[0362] Step 3:

[0363] The terminal collects real-time emotional data during the event using the user's smart device. It receives user facial expressions and voice data as input via an emotion analysis sensor, and sends an emotional evaluation of that moment to the server as output. Specifically, the terminal transfers emotional information from each user's smartphone to the server via Bluetooth or Wi-Fi, which can then be used to adjust the event environment.

[0364] Step 4:

[0365] The server uses real-time emotional data to adjust the event environment. It receives emotional ratings from each participant as input and generates optimal event environment settings as output. Specifically, it can instruct changes to music and lighting settings, creating a system to enhance the participant experience.

[0366] Step 5:

[0367] The server takes into account the emotional burden on participants when determining cost sharing. It takes emotional data and role assignment information from each participant as input and calculates a fair cost-sharing result as output. For example, it analyzes the emotional stress of the user who acted as the moderator and adjusts the cost accordingly.

[0368] Step 6:

[0369] Users receive the cost sharing results from their device after the event ends. The system receives settlement information sent from the server as input and outputs payment confirmation and adjustments. Specifically, users check the settlement results on their device and, if they have any questions, communicate with the event organizer through the digital platform.

[0370] (Application Example 2)

[0371] 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."

[0372] Planning and running an event is challenging because it accurately reflects the preferences and emotions of all participants and maximizes individual satisfaction. Furthermore, it's necessary to reflect real-time emotional shifts and appropriately adjust the event's atmosphere, but existing systems cannot efficiently accomplish this.

[0373] 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.

[0374] This invention includes a server that automatically selects multiple offering candidates based on participants' past evaluation information and preferences, a means for acquiring participants' emotional data using an emotion engine and monitoring their satisfaction level in real time during the event, and a means for adjusting the service and atmosphere during the event while taking participants' emotions into consideration. This makes it possible to manage events in a way that is attentive to the individual emotions of each participant.

[0375] "Past participant evaluation information" refers to data that records and collects opinions and impressions previously expressed by event participants.

[0376] "Preferences" refer to the elements or conditions that individual participants are particularly interested in, and these serve as criteria for selection and proposals.

[0377] "Possible options" refer to multiple choices or options that are considered when organizing an event.

[0378] "Booking the best option" means determining the most suitable choice based on the participants' conditions and circumstances, and securing the venue and services accordingly.

[0379] A "communication network" is a digital infrastructure used for sending and receiving information.

[0380] "Event attendance confirmation" refers to the act of confirming whether prospective participants intend to attend or not attend an event.

[0381] "Schedule information" is a general term for all temporal information regarding individual participants and the dates and times of events.

[0382] An "emotional engine" is a technology that analyzes participants' emotions and moods and provides that information.

[0383] "Real-time monitoring" means the act of observing and understanding the current situation and data in real time.

[0384] "Adjusting the atmosphere of an event" refers to changing the environment and services offered in accordance with the emotions and circumstances of the participants, in order to provide a more appropriate experience.

[0385] In the system for realizing this invention, the server automatically selects multiple potential events based on each participant's past evaluation information and preferences. The system then determines the best option from the selected candidates and makes a reservation via a communication network. Furthermore, it automatically obtains attendance confirmations for events and integrates the schedule information of prospective participants to determine the optimal event date.

[0386] The server utilizes an emotion engine to monitor participants' emotions in real time. This allows for adjustments to the atmosphere and services during the event to enhance participant satisfaction. Furthermore, it facilitates smooth event operation by using emotion data to ensure fair cost sharing and provide necessary payment information.

[0387] The implementation will utilize devices such as smart assistant robots. From a software perspective, it is crucial to analyze participants' past emotional data using generative AI models and emotion engine APIs to provide real-time feedback.

[0388] For example, in the case of a home party with friends in the neighborhood, the system suggests the most suitable party theme based on past reviews from participants and automatically makes the necessary reservations. During the event, the device analyzes the participants' emotions and makes adjustments such as playing music if a relaxed atmosphere is needed. This kind of operation improves participant satisfaction.

[0389] Example prompt: "Can you suggest a theme for our next party? Please consider the participants' past event reviews and recent moods when making your choice."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The server retrieves data from a database of past event participant ratings and preferences. The input is participant identification information, which is used to search for relevant past event ratings. The output is rating information and preference data organized for each participant. This enables the selection of personalized offerings.

[0393] Step 2:

[0394] The server uses a generative AI model to select multiple offering candidates that are best suited to the participant from the input data. This process involves data processing to reflect past evaluation information and participant preferences, and prioritization. The output is a list of recommended offering candidates, which prompts the user to make a selection.

[0395] Step 3:

[0396] The server automatically reserves the most suitable option from the selected candidates. The input is a list of candidates selected by the user, and the output is confirmation information for the reservation completion. This includes details of the reservation and the schedule based on it.

[0397] Step 4:

[0398] The server automatically obtains attendance confirmations from users via the communication network. Inputs include a participant list and attendance confirmation messages for each participant. Output is a list of participants who have confirmed their attendance. Based on this, scheduling adjustments are made.

[0399] Step 5:

[0400] The server integrates participants' schedule information to determine the optimal event date. The input is schedule data collected from multiple participants, and the output is the optimal event date. This allows for efficient scheduling.

[0401] Step 6:

[0402] The device utilizes an emotion engine to acquire and analyze participants' emotional data in real time during an event. The input is the emotional data collected from participants in real time, and the output is an analysis result showing emotional trends. Based on this, feedback for the event can be obtained.

[0403] Step 7:

[0404] The server adjusts the service and atmosphere during an event based on emotional data. The input is the emotional trend analyzed by the emotion engine, and the output is a specific action plan for the necessary adjustments. This enables concrete measures to improve participant satisfaction.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] The system of the present invention provides a comprehensive solution for streamlining event planning tasks. This system enables the selection and automatic booking of event candidates based on the user's past evaluation information and preferences, automated attendance confirmation via a communication network, optimal scheduling through the integration of schedule information, guidance to meeting places, and dynamic cost calculation and payment procedures based on roles.

[0422] The server first retrieves the user's past participation history and feedback data, and then uses a generation AI to select the most suitable restaurant options. This information is then communicated to the user via their device. For example, it can suggest new restaurant options based on the types of cuisine the user has previously given high ratings to.

[0423] Next, the server uses the communication platform to send a confirmation message to the user's terminal to verify attendance. The user selects whether or not to participate on their terminal and sends that information back to the server. Based on this information, the server calculates a suitable date for the event.

[0424] Furthermore, the server determines the meeting time and place associated with the reserved service candidate and notifies the user via their terminal, ensuring that participants can gather smoothly. For example, the meeting place may be designated as the ticket gate of the nearest station or in front of a landmark.

[0425] Finally, the server calculates how to fairly distribute the event-related costs among participants and notifies each user of the payment amount via their terminal. Users can then verify the payment information on their terminal and complete the payment using their registered payment method. This ensures transparency and smooth expense settlement.

[0426] This system significantly reduces the burden on users and simplifies event planning and management, enabling event management that satisfies all participants.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The server collects past user ratings and participation history from the database and extracts features that match the user's preferences. Based on this, it uses a generative AI to list multiple potential offerings.

[0430] Step 2:

[0431] The server notifies the user's terminal of the selected service options. The user reviews these options and selects their preferred service. The selected option is sent back to the server, which reserves it.

[0432] Step 3:

[0433] The server sends an attendance confirmation message to the user's device via the communication platform, based on the participant list obtained from the user. The user completes the attendance confirmation by selecting whether to attend or not on their device and sending that information to the server.

[0434] Step 4:

[0435] The server cross-references the calendar information of all prospective participants and calculates the event date that allows the most participants to attend. The calculated result is notified to the user's device, allowing the user to flexibly check the schedule.

[0436] Step 5:

[0437] The server determines the meeting place and time for confirmed events and sends a guidance message to the user via their device. The user can then check the route to the meeting place on their device.

[0438] Step 6:

[0439] The server automatically calculates a fair fee for each participant, taking into account their role and the event budget. The calculated amount is sent to the user's device, where they can confirm their cost and pay using their registered payment method.

[0440] Step 7:

[0441] The terminal communicates with the server after payment is complete to record that the settlement has been completed. The server updates the overall payment status, and the total payment for the event is completed. This completes all processing successfully.

[0442] (Example 1)

[0443] 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."

[0444] In existing event planning tasks, selecting potential venues based on participants' preferences and determining the optimal event date and time are often done manually, resulting in a significant burden of time and effort. Furthermore, cost sharing and attendance confirmation are complex and difficult to process efficiently.

[0445] 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.

[0446] In this invention, the server includes means for automatically selecting multiple offering candidates using an information processing device based on participants' past evaluation information and preferences, means for reserving the most suitable candidate from the automatically selected offering candidates, and means for automatically acquiring event attendance information via a communication path. This reduces the burden on the organizer and enables the holding of efficient and highly satisfying events.

[0447] "Participants" refers to individuals or groups of people who are planning or intending to participate in an event.

[0448] "Evaluation information" refers to feedback, scores, and evaluation comments given by participants to past events and services.

[0449] "Preferences" refer to the tastes and interests that participants have in a particular genre or category.

[0450] An "information processing device" refers to a computer or server system used to perform calculations and analyses of data and execute specific processes.

[0451] "Potential offerings" refer to potential events or services recommended based on participants' preferences and evaluation information.

[0452] "Communication path" refers to the means and protocols for transmitting information to a remote location, including the internet and mobile networks.

[0453] "Attendance information" refers to the status or decision of whether or not a participant will attend a particular event.

[0454] "Reservation procedures" refer to a series of actions taken to secure the right to use a specific service or event in advance.

[0455] The present invention provides technology for efficiently managing and coordinating events. This system consists of interactions between servers, terminals, and users. The system's operation is described in detail below.

[0456] First, the server acquires the user's past evaluation information and preference data using an information processing device. This involves using a database management system and SQL queries. Then, the server processes this data using a generative AI model to select the most suitable candidates for the user. This AI model analyzes the user's preference patterns using machine learning techniques. For example, it generates a new list of candidates by referring to events and services that have received high ratings in the past.

[0457] Next, the server notifies the terminal of the selected candidate information via the communication path. The terminal receives this information and displays it in the user interface. This process is carried out via a REST API and JSON format. The server also uses a communication platform to notify the user's terminal of attendance confirmation. Using the Twilio API and other services, messages are delivered via SMS or push notifications. The user responds to the attendance confirmation on their terminal, and that information is sent back to the server.

[0458] The server calculates the optimal event date based on the attendance information it has collected. An algorithm uses the Calendar API to aggregate participants' availability and determine the best date and time. Furthermore, the server determines the meeting time and location for the event and transmits these details to the participants' devices. The Maps API can be used for this process.

[0459] Finally, the server calculates how to distribute the event costs among the participants and provides payment information to the user via the terminal. This process utilizes a cost-sharing algorithm and a payment API. The user can verify the amount on the terminal and complete the payment via the link.

[0460] As a concrete example, consider a scenario where a user plans a dinner event with friends. The system suggests suitable restaurant options based on the user's past ratings, adjusts the date while considering everyone's schedules, and then fairly calculates and notifies each participant of their share of the costs. This entire process can be triggered by a prompt, such as, "Based on the user's past event participation history and feedback, please suggest the best restaurant options for the next event. I will also optimize the event date and location."

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server retrieves participants' past evaluation information and preference data. Participant IDs are provided as input, and the corresponding evaluation information and preference data are extracted as output. Specifically, it uses a database management system to execute SQL queries to collect the necessary information.

[0464] Step 2:

[0465] The server inputs the acquired evaluation information and preference data into a generating AI model. Based on the input data, the model analyzes the data and selects the most suitable offering candidates. A list of candidates is generated as output. In its specific operation, a Python script is executed, and a machine learning model is used to perform scoring and ranking.

[0466] Step 3:

[0467] The server sends the selected candidate offerings to the terminal. The input is a list of candidates, and the output is the terminal displaying the candidates. Specifically, data is sent to the terminal in JSON format using a REST API, and the terminal parses this data and displays it in a GUI.

[0468] Step 4:

[0469] The server uses the communication path to send attendance confirmations to the device. The input requires a list of potential event attendees, and the output is the attendance response from each participant. Specifically, the Twilio API is used to send SMS or push notifications, and the device then sends its selection back to the server.

[0470] Step 5:

[0471] The server calculates the optimal event date based on attendance information. It requires attendance responses from all participants and their associated availability information as input, and outputs the identified optimal date and time. Specifically, it uses a calendar API to execute an algorithm that identifies common availability periods.

[0472] Step 6:

[0473] The server determines the meeting time and place for the event and notifies the terminal. The input is the reserved service candidates and the participants' location information, and the output is instructions for the meeting time and place. Specifically, it uses a map API to select the optimal geographical point and sends the notification.

[0474] Step 7:

[0475] The server calculates the costs associated with the event and provides payment information to participants via their devices. Input data includes the number of participants and the total event cost, and the output calculates the individual payment amounts. Specifically, it uses a cost distribution algorithm, generates payment links using the Stripe API, and sends them to the devices.

[0476] (Application Example 1)

[0477] 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."

[0478] In event planning and management, it is essential to accurately understand participants' needs, efficiently schedule events, and create events that are highly satisfying for all participants. However, current systems struggle to effectively utilize past data and automate the provision of dynamic information, resulting in a significant burden on participants.

[0479] 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.

[0480] This invention includes a server that automatically selects multiple event candidates based on participants' past evaluation information and preferences, provides participants with detailed event information and dynamically guides them to meeting places, and dynamically calculates costs based on participants' roles and provides information for electronic payment. This enables comprehensive management from event planning to operation, maximizing participant satisfaction while achieving efficient event management.

[0481] "Past participant evaluation information" refers to data on feedback and satisfaction levels from participants regarding past events.

[0482] "Preferences" refer to specific tastes or tendencies that participants exhibit based on their past experiences and choices.

[0483] "Event candidates" refers to the options and plans of events that can be offered to participants.

[0484] A "communication network" is a technical infrastructure for sending and receiving digital data between users and servers.

[0485] "Schedule information" refers to information about each participant's schedule and available time, as provided by those planning to attend the event.

[0486] "Meeting place instructions" refer to instructions provided for participants to gather at a specific location designated before the event.

[0487] "Role-based cost calculation" is a method of calculating event expenses to fairly distribute them according to the roles and contributions of the participants.

[0488] "Electronic payment" is a payment method that uses digital technology to send and receive funds online.

[0489] "Generative AI" is a form of artificial intelligence that has the ability to learn from large amounts of data and automatically perform reasoning and decision-making.

[0490] The system for realizing this invention operates via a server and a user's terminal. The server first automatically selects multiple event candidates based on the participant's past evaluation information and preferences. By using a generative AI model, it is possible to prioritize the optimal event candidates for each individual participant.

[0491] The user's device receives event candidate information from the server and presents it to the user. In this process, the user sends an indication of whether or not they can attend the event from their device to the server, which then automatically checks attendance via the communication network.

[0492] Next, the server integrates the participants' schedule information and uses a generating AI to determine the optimal event date. This information is then communicated to the participants via their devices.

[0493] Furthermore, the server provides participants' devices with detailed event information and dynamic directions to the meeting place. Map services and location information can be used to guide participants to the meeting place.

[0494] Finally, the server dynamically calculates the cost based on the participant's role and provides electronic payment information to the terminal. This allows participants to easily complete payment using their own devices. The smooth event management enabled by this system ensures a satisfying experience for all participants.

[0495] As a concrete example, it is possible to recommend new events based on the genres of events that a user has previously given high ratings to. An example of a prompt to the generative AI model would be, "Please suggest new events similar to the event genres that user A has previously given high ratings to."

[0496] This invention enables the efficient implementation of events based on the needs of participants.

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The server takes the user's past evaluation information and preferences as input and automatically selects multiple event candidates using a generative AI model. During this process, the server inputs prompt messages into the AI ​​model based on the participant's past feedback and participation history, outputting a list of optimal events.

[0500] Step 2:

[0501] The terminal presents the user with event candidate information received from the server. The user then selects whether or not to participate based on this information and sends that information back to the server. Here, the terminal displays event candidate data as input, and the user's decision is returned as output.

[0502] Step 3:

[0503] The server uses the received attendance confirmation information to verify attendance via the communication network and integrates participants' schedule information. During this process, it calculates and outputs the optimal date for the event based on several confirmed schedules.

[0504] Step 4:

[0505] The server uses a generative AI model to determine the optimal event date from the integrated schedule information. In this process, the schedule information is presented to the model as input, and the optimal date is obtained as output.

[0506] Step 5:

[0507] The server provides users with detailed event information and dynamic meeting place directions via their terminals. Specifically, it uses location data from a map service as input and provides output to guide users to the meeting place.

[0508] Step 6:

[0509] The server dynamically calculates the cost based on the participant's role and sends the information for electronic payment to the terminal. Inputs include the participant's role and cost, and the calculated payment amount is output and notified to the terminal.

[0510] Step 7:

[0511] The user completes the participation fee by making an electronic payment from their terminal. Here, the payment information is confirmed as input, and the payment processing is output.

[0512] 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.

[0513] The present invention incorporates an emotion engine to further personalize the tasks of event organizers and improve participant satisfaction. This system utilizes the emotion engine to add new functions to enable the selection of event candidates based on participants' past evaluations and preferences, automatic booking, attendance confirmation via a communication network, optimal scheduling through integration of schedule information, guidance to meeting places, and cost calculation and payment information provision based on roles.

[0514] The server first uses an emotion engine to acquire and analyze the user's past participation history and real-time emotion data. This data includes emotions the user expressed during or immediately after past events, as well as their current mood. This allows the generation AI to select more accurate recommendations that reflect the user's current emotions. For example, it can output selection results that include shops that showed high satisfaction in past events, as well as a variety of other options.

[0515] Next, during the event, the server uses an emotion engine to monitor participants' emotional data in real time and measure their satisfaction level. This result can be used as feedback to adjust the service and atmosphere during the event.

[0516] Furthermore, the server utilizes emotional data in the area of ​​cost sharing. It adjusts the cost burden among participants to ensure an emotionally fair distribution, taking into account the emotional burden associated with specific roles and obligations. This allows users to settle their accounts at a price they find acceptable, and promotes the smooth operation of the event as a whole.

[0517] Thus, this system, which incorporates an emotion engine, not only streamlines the tasks of event organizers but also enables event management that is attentive to the emotions of each participant, thereby improving everyone's satisfaction.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The server collects past event participation history and real-time status data from the user's device. Using an emotion engine, it analyzes the user's preferences and current emotional state from this data and generates individually customized suggestions.

[0521] Step 2:

[0522] The server sends the generated list of potential offers to the user's device. The user reviews the list of options on the device and selects the offers they are interested in. The selection results are sent back to the server, which then uses this information to confirm the reservation.

[0523] Step 3:

[0524] The server sends an attendance confirmation message to the terminal via the communication network. The user receives this message, selects their intention to participate on their terminal, and replies to the server. This information is aggregated on the server.

[0525] Step 4:

[0526] The server integrates participants' schedule information and determines the most convenient date. The determined date is then adjusted using an emotion engine to align with the participants' past emotional tendencies. The server notifies the participants of the date on their devices.

[0527] Step 5:

[0528] On the day of the event, the server uses an emotion engine to measure participants' emotions in real time. This data is used as emotional feedback for each participant via their device, and the environment and services are adjusted as needed during the event.

[0529] Step 6:

[0530] After the event ends, the server uses an emotion engine to analyze participant emotional data and calculate a cost distribution that minimizes the psychological burden on each user. The calculation results are sent to the user's device, where they complete the payment.

[0531] Step 7:

[0532] Once settlement is complete, the terminal notifies the server and terminates all processes. The server generates a final event report and sends it to each participant, officially concluding the event.

[0533] (Example 2)

[0534] 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."

[0535] In events involving multiple participants, maximizing individual participant satisfaction requires not only selecting offerings based on past evaluations and preferences, but also responding to real-time changes in emotions. Furthermore, cost sharing that appropriately considers the burden on participants is essential. However, comprehensively managing and achieving all of these elements in event management is challenging.

[0536] 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.

[0537] This invention includes a server that acquires emotional data and adjusts the environment in real time during the event based on the participants' emotions; a server that uses generative AI to combine participants' past data with their real-time emotional state to select more accurate candidates for provision; and a server that dynamically adjusts cost sharing while taking into account the emotional burden of the participants. This enables effective event management based on the individual emotions of each participant and cost sharing that is less burdensome and considerate of their emotions.

[0538] "Emotional data" refers to information obtained as numerical or text data about participants' emotional states during past events or in real time, and converted into an analyzable format.

[0539] "Generative AI" is an artificial intelligence technology that uses historical data and real-time sentiment data to make optimal selections and adjustments for specific tasks.

[0540] "Proposal candidates" refer to options such as restaurants and activities selected to enhance participant satisfaction when hosting an event.

[0541] "Real-time adjustment" is a process that instantly reflects changes in participants' emotional data during an event, optimizing the environment and service content accordingly.

[0542] "Emotional burden" refers to the degree of psychological stress or dissatisfaction that participants experience as a result of fulfilling a specific role or as a result of events during an event.

[0543] "Cost sharing" is a system in which the expenses that each individual participating in an event should pay are distributed based on rules and standards agreed upon among the participants.

[0544] This system is a platform that utilizes emotional data to streamline event management and improve participant satisfaction. The following elements are used during implementation:

[0545] The server utilizes cloud-based services for primary analysis and data management. Specifically, it leverages sentiment analysis APIs provided by cloud providers to obtain participants' past event history and real-time sentiment data. This allows for trend analysis based on each user's past evaluation information and preferences.

[0546] The generative AI model selects potential event offerings based on the aforementioned data. These offerings include facilities and activities that users have previously shown high satisfaction with, and the AI ​​combines them to make optimal suggestions. For example, by inputting a prompt such as, "Please suggest restaurants that received high ratings at past events as options for the next event," the AI ​​will return appropriate suggestions.

[0547] During the event, the terminal collects real-time emotional data using the user's smart device. This data is transmitted to a server via Bluetooth or Wi-Fi, which uses this information to adjust the environment according to the event's progress. This allows for flexible adjustments to music selection and lighting.

[0548] After the event, users can check the cost sharing results via their devices. The server considers the roles and emotional burdens of participants based on sentiment data to ensure a fair cost distribution. In this way, all participants are satisfied with the settlement.

[0549] The series of operations of this system provides event organizers with a flexible, emotion-focused management approach, enabling them to respond to the emotions of each individual participant and thereby improve overall event satisfaction.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The server uses cloud services to collect participants' past event participation history and analyzes it in combination with real-time sentiment data. It takes user evaluation information and event-specific sentiment data as input and generates sentiment profiles as output. Specifically, a cloud-based sentiment analysis API extracts sentiment data from photos and social media posts and stores it in a database. This data is then prepared for advanced analysis used in the next step.

[0553] Step 2:

[0554] A generative AI model runs on the server and selects the most suitable candidates based on the collected data. The AI ​​receives the emotional profile and past evaluation information obtained in the previous step as input and provides a list of optimal event candidates as output. Specifically, the AI ​​processes a prompt such as, "Please list restaurants that received high ratings at past events as candidates for the next event," and relevant candidates are selected.

[0555] Step 3:

[0556] The terminal collects real-time emotional data during the event using the user's smart device. It receives user facial expressions and voice data as input via an emotion analysis sensor, and sends an emotional evaluation of that moment to the server as output. Specifically, the terminal transfers emotional information from each user's smartphone to the server via Bluetooth or Wi-Fi, which can then be used to adjust the event environment.

[0557] Step 4:

[0558] The server uses real-time emotional data to adjust the event environment. It receives emotional ratings from each participant as input and generates optimal event environment settings as output. Specifically, it can instruct changes to music and lighting settings, creating a system to enhance the participant experience.

[0559] Step 5:

[0560] The server takes into account the emotional burden on participants when determining cost sharing. It takes emotional data and role assignment information from each participant as input and calculates a fair cost-sharing result as output. For example, it analyzes the emotional stress of the user who acted as the moderator and adjusts the cost accordingly.

[0561] Step 6:

[0562] Users receive the cost sharing results from their device after the event ends. The system receives settlement information sent from the server as input and outputs payment confirmation and adjustments. Specifically, users check the settlement results on their device and, if they have any questions, communicate with the event organizer through the digital platform.

[0563] (Application Example 2)

[0564] 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."

[0565] Planning and running an event is challenging because it accurately reflects the preferences and emotions of all participants and maximizes individual satisfaction. Furthermore, it's necessary to reflect real-time emotional shifts and appropriately adjust the event's atmosphere, but existing systems cannot efficiently accomplish this.

[0566] 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.

[0567] This invention includes a server that automatically selects multiple offering candidates based on participants' past evaluation information and preferences, a means for acquiring participants' emotional data using an emotion engine and monitoring their satisfaction level in real time during the event, and a means for adjusting the service and atmosphere during the event while taking participants' emotions into consideration. This makes it possible to manage events in a way that is attentive to the individual emotions of each participant.

[0568] "Past participant evaluation information" refers to data that records and collects opinions and impressions previously expressed by event participants.

[0569] "Preferences" refer to the elements or conditions that individual participants are particularly interested in, and these serve as criteria for selection and proposals.

[0570] "Possible options" refer to multiple choices or options that are considered when organizing an event.

[0571] "Booking the best option" means determining the most suitable choice based on the participants' conditions and circumstances, and securing the venue and services accordingly.

[0572] A "communication network" is a digital infrastructure used for sending and receiving information.

[0573] "Event attendance confirmation" refers to the act of confirming whether prospective participants intend to attend or not attend an event.

[0574] "Schedule information" is a general term for all temporal information regarding individual participants and the dates and times of events.

[0575] An "emotional engine" is a technology that analyzes participants' emotions and moods and provides that information.

[0576] "Real-time monitoring" means the act of observing and understanding the current situation and data in real time.

[0577] "Adjusting the atmosphere of an event" refers to changing the environment and services offered in accordance with the emotions and circumstances of the participants, in order to provide a more appropriate experience.

[0578] In the system for realizing this invention, the server automatically selects multiple potential events based on each participant's past evaluation information and preferences. The system then determines the best option from the selected candidates and makes a reservation via a communication network. Furthermore, it automatically obtains attendance confirmations for events and integrates the schedule information of prospective participants to determine the optimal event date.

[0579] The server utilizes an emotion engine to monitor participants' emotions in real time. This allows for adjustments to the atmosphere and services during the event to enhance participant satisfaction. Furthermore, it facilitates smooth event operation by using emotion data to ensure fair cost sharing and provide necessary payment information.

[0580] The implementation will utilize devices such as smart assistant robots. From a software perspective, it is crucial to analyze participants' past emotional data using generative AI models and emotion engine APIs to provide real-time feedback.

[0581] For example, in the case of a home party with friends in the neighborhood, the system suggests the most suitable party theme based on past reviews from participants and automatically makes the necessary reservations. During the event, the device analyzes the participants' emotions and makes adjustments such as playing music if a relaxed atmosphere is needed. This kind of operation improves participant satisfaction.

[0582] Example prompt: "Can you suggest a theme for our next party? Please consider the participants' past event reviews and recent moods when making your choice."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The server retrieves data from a database of past event participant ratings and preferences. The input is participant identification information, which is used to search for relevant past event ratings. The output is rating information and preference data organized for each participant. This enables the selection of personalized offerings.

[0586] Step 2:

[0587] The server uses a generative AI model to select multiple offering candidates that are best suited to the participant from the input data. This process involves data processing to reflect past evaluation information and participant preferences, and prioritization. The output is a list of recommended offering candidates, which prompts the user to make a selection.

[0588] Step 3:

[0589] The server automatically reserves the most suitable option from the selected candidates. The input is a list of candidates selected by the user, and the output is confirmation information for the reservation completion. This includes details of the reservation and the schedule based on it.

[0590] Step 4:

[0591] The server automatically obtains attendance confirmations from users via the communication network. Inputs include a participant list and attendance confirmation messages for each participant. Output is a list of participants who have confirmed their attendance. Based on this, scheduling adjustments are made.

[0592] Step 5:

[0593] The server integrates participants' schedule information to determine the optimal event date. The input is schedule data collected from multiple participants, and the output is the optimal event date. This allows for efficient scheduling.

[0594] Step 6:

[0595] The device utilizes an emotion engine to acquire and analyze participants' emotional data in real time during an event. The input is the emotional data collected from participants in real time, and the output is an analysis result showing emotional trends. Based on this, feedback for the event can be obtained.

[0596] Step 7:

[0597] The server adjusts the service and atmosphere during an event based on emotional data. The input is the emotional trend analyzed by the emotion engine, and the output is a specific action plan for the necessary adjustments. This enables concrete measures to improve participant satisfaction.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] The system of the present invention provides a comprehensive solution for streamlining event planning tasks. This system enables the selection and automatic booking of event candidates based on the user's past evaluation information and preferences, automated attendance confirmation via a communication network, optimal scheduling through the integration of schedule information, guidance to meeting places, and dynamic cost calculation and payment procedures based on roles.

[0616] The server first retrieves the user's past participation history and feedback data, and then uses a generation AI to select the most suitable restaurant options. This information is then communicated to the user via their device. For example, it can suggest new restaurant options based on the types of cuisine the user has previously given high ratings to.

[0617] Next, the server uses the communication platform to send a confirmation message to the user's terminal to verify attendance. The user selects whether or not to participate on their terminal and sends that information back to the server. Based on this information, the server calculates a suitable date for the event.

[0618] Furthermore, the server determines the meeting time and place associated with the reserved service candidate and notifies the user via their terminal, ensuring that participants can gather smoothly. For example, the meeting place may be designated as the ticket gate of the nearest station or in front of a landmark.

[0619] Finally, the server calculates how to fairly distribute the event-related costs among participants and notifies each user of the payment amount via their terminal. Users can then verify the payment information on their terminal and complete the payment using their registered payment method. This ensures transparency and smooth expense settlement.

[0620] This system significantly reduces the burden on users and simplifies event planning and management, enabling event management that satisfies all participants.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The server collects past user ratings and participation history from the database and extracts features that match the user's preferences. Based on this, it uses a generative AI to list multiple potential offerings.

[0624] Step 2:

[0625] The server notifies the user's terminal of the selected service options. The user reviews these options and selects their preferred service. The selected option is sent back to the server, which reserves it.

[0626] Step 3:

[0627] The server sends an attendance confirmation message to the user's device via the communication platform, based on the participant list obtained from the user. The user completes the attendance confirmation by selecting whether to attend or not on their device and sending that information to the server.

[0628] Step 4:

[0629] The server cross-references the calendar information of all prospective participants and calculates the event date that allows the most participants to attend. The calculated result is notified to the user's device, allowing the user to flexibly check the schedule.

[0630] Step 5:

[0631] The server determines the meeting place and time for confirmed events and sends a guidance message to the user via their device. The user can then check the route to the meeting place on their device.

[0632] Step 6:

[0633] The server automatically calculates a fair fee for each participant, taking into account their role and the event budget. The calculated amount is sent to the user's device, where they can confirm their cost and pay using their registered payment method.

[0634] Step 7:

[0635] The terminal communicates with the server after payment is complete to record that the settlement has been completed. The server updates the overall payment status, and the total payment for the event is completed. This completes all processing successfully.

[0636] (Example 1)

[0637] 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".

[0638] In existing event planning tasks, selecting potential venues based on participants' preferences and determining the optimal event date and time are often done manually, resulting in a significant burden of time and effort. Furthermore, cost sharing and attendance confirmation are complex and difficult to process efficiently.

[0639] 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.

[0640] In this invention, the server includes means for automatically selecting multiple offering candidates using an information processing device based on participants' past evaluation information and preferences, means for reserving the most suitable candidate from the automatically selected offering candidates, and means for automatically acquiring event attendance information via a communication path. This reduces the burden on the organizer and enables the holding of efficient and highly satisfying events.

[0641] "Participants" refers to individuals or groups of people who are planning or intending to participate in an event.

[0642] "Evaluation information" refers to feedback, scores, and evaluation comments given by participants to past events and services.

[0643] "Preferences" refer to the tastes and interests that participants have in a particular genre or category.

[0644] An "information processing device" refers to a computer or server system used to perform calculations and analyses of data and execute specific processes.

[0645] "Potential offerings" refer to potential events or services recommended based on participants' preferences and evaluation information.

[0646] "Communication path" refers to the means and protocols for transmitting information to a remote location, including the internet and mobile networks.

[0647] "Attendance information" refers to the status or decision of whether or not a participant will attend a particular event.

[0648] "Reservation procedures" refer to a series of actions taken to secure the right to use a specific service or event in advance.

[0649] The present invention provides technology for efficiently managing and coordinating events. This system consists of interactions between servers, terminals, and users. The system's operation is described in detail below.

[0650] First, the server acquires the user's past evaluation information and preference data using an information processing device. This involves using a database management system and SQL queries. Then, the server processes this data using a generative AI model to select the most suitable candidates for the user. This AI model analyzes the user's preference patterns using machine learning techniques. For example, it generates a new list of candidates by referring to events and services that have received high ratings in the past.

[0651] Next, the server notifies the terminal of the selected candidate information via the communication path. The terminal receives this information and displays it in the user interface. This process is carried out via a REST API and JSON format. The server also uses a communication platform to notify the user's terminal of attendance confirmation. Using the Twilio API and other services, messages are delivered via SMS or push notifications. The user responds to the attendance confirmation on their terminal, and that information is sent back to the server.

[0652] The server calculates the optimal event date based on the attendance information it has collected. An algorithm uses the Calendar API to aggregate participants' availability and determine the best date and time. Furthermore, the server determines the meeting time and location for the event and transmits these details to the participants' devices. The Maps API can be used for this process.

[0653] Finally, the server calculates how to distribute the event costs among the participants and provides payment information to the user via the terminal. This process utilizes a cost-sharing algorithm and a payment API. The user can verify the amount on the terminal and complete the payment via the link.

[0654] As a concrete example, consider a scenario where a user plans a dinner event with friends. The system suggests suitable restaurant options based on the user's past ratings, adjusts the date while considering everyone's schedules, and then fairly calculates and notifies each participant of their share of the costs. This entire process can be triggered by a prompt, such as, "Based on the user's past event participation history and feedback, please suggest the best restaurant options for the next event. I will also optimize the event date and location."

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The server retrieves participants' past evaluation information and preference data. Participant IDs are provided as input, and the corresponding evaluation information and preference data are extracted as output. Specifically, it uses a database management system to execute SQL queries to collect the necessary information.

[0658] Step 2:

[0659] The server inputs the acquired evaluation information and preference data into a generating AI model. Based on the input data, the model analyzes the data and selects the most suitable offering candidates. A list of candidates is generated as output. In its specific operation, a Python script is executed, and a machine learning model is used to perform scoring and ranking.

[0660] Step 3:

[0661] The server sends the selected candidate offerings to the terminal. The input is a list of candidates, and the output is the terminal displaying the candidates. Specifically, data is sent to the terminal in JSON format using a REST API, and the terminal parses this data and displays it in a GUI.

[0662] Step 4:

[0663] The server uses the communication path to send attendance confirmations to the device. The input requires a list of potential event attendees, and the output is the attendance response from each participant. Specifically, the Twilio API is used to send SMS or push notifications, and the device then sends its selection back to the server.

[0664] Step 5:

[0665] The server calculates the optimal event date based on attendance information. It requires attendance responses from all participants and their associated availability information as input, and outputs the identified optimal date and time. Specifically, it uses a calendar API to execute an algorithm that identifies common availability periods.

[0666] Step 6:

[0667] The server determines the meeting time and place for the event and notifies the terminal. The input is the reserved service candidates and the participants' location information, and the output is instructions for the meeting time and place. Specifically, it uses a map API to select the optimal geographical point and sends the notification.

[0668] Step 7:

[0669] The server calculates the costs associated with the event and provides payment information to participants via their devices. Input data includes the number of participants and the total event cost, and the output calculates the individual payment amounts. Specifically, it uses a cost distribution algorithm, generates payment links using the Stripe API, and sends them to the devices.

[0670] (Application Example 1)

[0671] 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".

[0672] In event planning and management, it is essential to accurately understand participants' needs, efficiently schedule events, and create events that are highly satisfying for all participants. However, current systems struggle to effectively utilize past data and automate the provision of dynamic information, resulting in a significant burden on participants.

[0673] 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.

[0674] This invention includes a server that automatically selects multiple event candidates based on participants' past evaluation information and preferences, provides participants with detailed event information and dynamically guides them to meeting places, and dynamically calculates costs based on participants' roles and provides information for electronic payment. This enables comprehensive management from event planning to operation, maximizing participant satisfaction while achieving efficient event management.

[0675] "Past participant evaluation information" refers to data on feedback and satisfaction levels from participants regarding past events.

[0676] "Preferences" refer to specific tastes or tendencies that participants exhibit based on their past experiences and choices.

[0677] "Event candidates" refers to the options and plans of events that can be offered to participants.

[0678] A "communication network" is a technical infrastructure for sending and receiving digital data between users and servers.

[0679] "Schedule information" refers to information about each participant's schedule and available time, as provided by those planning to attend the event.

[0680] "Meeting place instructions" refer to instructions provided for participants to gather at a specific location designated before the event.

[0681] "Role-based cost calculation" is a method of calculating event expenses to fairly distribute them according to the roles and contributions of the participants.

[0682] "Electronic payment" is a payment method that uses digital technology to send and receive funds online.

[0683] "Generative AI" is a form of artificial intelligence that has the ability to learn from large amounts of data and automatically perform reasoning and decision-making.

[0684] The system for realizing this invention operates via a server and a user's terminal. The server first automatically selects multiple event candidates based on the participant's past evaluation information and preferences. By using a generative AI model, it is possible to prioritize the optimal event candidates for each individual participant.

[0685] The user's device receives event candidate information from the server and presents it to the user. In this process, the user sends an indication of whether or not they can attend the event from their device to the server, which then automatically checks attendance via the communication network.

[0686] Next, the server integrates the participants' schedule information and uses a generating AI to determine the optimal event date. This information is then communicated to the participants via their devices.

[0687] Furthermore, the server provides participants' devices with detailed event information and dynamic directions to the meeting place. Map services and location information can be used to guide participants to the meeting place.

[0688] Finally, the server dynamically calculates the cost based on the participant's role and provides electronic payment information to the terminal. This allows participants to easily complete payment using their own devices. The smooth event management enabled by this system ensures a satisfying experience for all participants.

[0689] As a concrete example, it is possible to recommend new events based on the genres of events that a user has previously given high ratings to. An example of a prompt to the generative AI model would be, "Please suggest new events similar to the event genres that user A has previously given high ratings to."

[0690] This invention enables the efficient implementation of events based on the needs of participants.

[0691] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0692] Step 1:

[0693] The server takes the user's past evaluation information and preferences as input and automatically selects multiple event candidates using a generative AI model. During this process, the server inputs prompt messages into the AI ​​model based on the participant's past feedback and participation history, outputting a list of optimal events.

[0694] Step 2:

[0695] The terminal presents the user with event candidate information received from the server. The user then selects whether or not to participate based on this information and sends that information back to the server. Here, the terminal displays event candidate data as input, and the user's decision is returned as output.

[0696] Step 3:

[0697] The server uses the received attendance confirmation information to verify attendance via the communication network and integrates participants' schedule information. During this process, it calculates and outputs the optimal date for the event based on several confirmed schedules.

[0698] Step 4:

[0699] The server uses a generative AI model to determine the optimal event date from the integrated schedule information. In this process, the schedule information is presented to the model as input, and the optimal date is obtained as output.

[0700] Step 5:

[0701] The server provides users with detailed event information and dynamic meeting place directions via their terminals. Specifically, it uses location data from a map service as input and provides output to guide users to the meeting place.

[0702] Step 6:

[0703] The server dynamically calculates the cost based on the participant's role and sends the information for electronic payment to the terminal. Inputs include the participant's role and cost, and the calculated payment amount is output and notified to the terminal.

[0704] Step 7:

[0705] The user completes the participation fee by making an electronic payment from their terminal. Here, the payment information is confirmed as input, and the payment processing is output.

[0706] 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.

[0707] The present invention incorporates an emotion engine to further personalize the tasks of event organizers and improve participant satisfaction. This system utilizes the emotion engine to add new functions to enable the selection of event candidates based on participants' past evaluations and preferences, automatic booking, attendance confirmation via a communication network, optimal scheduling through integration of schedule information, guidance to meeting places, and cost calculation and payment information provision based on roles.

[0708] The server first uses an emotion engine to acquire and analyze the user's past participation history and real-time emotion data. This data includes emotions the user expressed during or immediately after past events, as well as their current mood. This allows the generation AI to select more accurate recommendations that reflect the user's current emotions. For example, it can output selection results that include shops that showed high satisfaction in past events, as well as a variety of other options.

[0709] Next, during the event, the server uses an emotion engine to monitor participants' emotional data in real time and measure their satisfaction level. This result can be used as feedback to adjust the service and atmosphere during the event.

[0710] Furthermore, the server utilizes emotional data in the area of ​​cost sharing. It adjusts the cost burden among participants to ensure an emotionally fair distribution, taking into account the emotional burden associated with specific roles and obligations. This allows users to settle their accounts at a price they find acceptable, and promotes the smooth operation of the event as a whole.

[0711] Thus, this system, which incorporates an emotion engine, not only streamlines the tasks of event organizers but also enables event management that is attentive to the emotions of each participant, thereby improving everyone's satisfaction.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The server collects past event participation history and real-time status data from the user's device. Using an emotion engine, it analyzes the user's preferences and current emotional state from this data and generates individually customized suggestions.

[0715] Step 2:

[0716] The server sends the generated list of potential offers to the user's device. The user reviews the list of options on the device and selects the offers they are interested in. The selection results are sent back to the server, which then uses this information to confirm the reservation.

[0717] Step 3:

[0718] The server sends an attendance confirmation message to the terminal via the communication network. The user receives this message, selects their intention to participate on their terminal, and replies to the server. This information is aggregated on the server.

[0719] Step 4:

[0720] The server integrates participants' schedule information and determines the most convenient date. The determined date is then adjusted using an emotion engine to align with the participants' past emotional tendencies. The server notifies the participants of the date on their devices.

[0721] Step 5:

[0722] On the day of the event, the server uses an emotion engine to measure participants' emotions in real time. This data is used as emotional feedback for each participant via their device, and the environment and services are adjusted as needed during the event.

[0723] Step 6:

[0724] After the event ends, the server uses an emotion engine to analyze participant emotional data and calculate a cost distribution that minimizes the psychological burden on each user. The calculation results are sent to the user's device, where they complete the payment.

[0725] Step 7:

[0726] Once settlement is complete, the terminal notifies the server and terminates all processes. The server generates a final event report and sends it to each participant, officially concluding the event.

[0727] (Example 2)

[0728] 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".

[0729] In events involving multiple participants, maximizing individual participant satisfaction requires not only selecting offerings based on past evaluations and preferences, but also responding to real-time changes in emotions. Furthermore, cost sharing that appropriately considers the burden on participants is essential. However, comprehensively managing and achieving all of these elements in event management is challenging.

[0730] 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.

[0731] This invention includes a server that acquires emotional data and adjusts the environment in real time during the event based on the participants' emotions; a server that uses generative AI to combine participants' past data with their real-time emotional state to select more accurate candidates for provision; and a server that dynamically adjusts cost sharing while taking into account the emotional burden of the participants. This enables effective event management based on the individual emotions of each participant and cost sharing that is less burdensome and considerate of their emotions.

[0732] "Emotional data" refers to information obtained as numerical or text data about participants' emotional states during past events or in real time, and converted into an analyzable format.

[0733] "Generative AI" is an artificial intelligence technology that uses historical data and real-time sentiment data to make optimal selections and adjustments for specific tasks.

[0734] "Proposal candidates" refer to options such as restaurants and activities selected to enhance participant satisfaction when hosting an event.

[0735] "Real-time adjustment" is a process that instantly reflects changes in participants' emotional data during an event, optimizing the environment and service content accordingly.

[0736] "Emotional burden" refers to the degree of psychological stress or dissatisfaction that participants experience as a result of fulfilling a specific role or as a result of events during an event.

[0737] "Cost sharing" is a system in which the expenses that each individual participating in an event should pay are distributed based on rules and standards agreed upon among the participants.

[0738] This system is a platform that utilizes emotional data to streamline event management and improve participant satisfaction. The following elements are used during implementation:

[0739] The server utilizes cloud-based services for primary analysis and data management. Specifically, it leverages sentiment analysis APIs provided by cloud providers to obtain participants' past event history and real-time sentiment data. This allows for trend analysis based on each user's past evaluation information and preferences.

[0740] The generative AI model selects potential event offerings based on the aforementioned data. These offerings include facilities and activities that users have previously shown high satisfaction with, and the AI ​​combines them to make optimal suggestions. For example, by inputting a prompt such as, "Please suggest restaurants that received high ratings at past events as options for the next event," the AI ​​will return appropriate suggestions.

[0741] During the event, the terminal collects real-time emotional data using the user's smart device. This data is transmitted to a server via Bluetooth or Wi-Fi, which uses this information to adjust the environment according to the event's progress. This allows for flexible adjustments to music selection and lighting.

[0742] After the event, users can check the cost sharing results via their devices. The server considers the roles and emotional burdens of participants based on sentiment data to ensure a fair cost distribution. In this way, all participants are satisfied with the settlement.

[0743] The series of operations of this system provides event organizers with a flexible, emotion-focused management approach, enabling them to respond to the emotions of each individual participant and thereby improve overall event satisfaction.

[0744] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0745] Step 1:

[0746] The server uses cloud services to collect participants' past event participation history and analyzes it in combination with real-time sentiment data. It takes user evaluation information and event-specific sentiment data as input and generates sentiment profiles as output. Specifically, a cloud-based sentiment analysis API extracts sentiment data from photos and social media posts and stores it in a database. This data is then prepared for advanced analysis used in the next step.

[0747] Step 2:

[0748] A generative AI model runs on the server and selects the most suitable candidates based on the collected data. The AI ​​receives the emotional profile and past evaluation information obtained in the previous step as input and provides a list of optimal event candidates as output. Specifically, the AI ​​processes a prompt such as, "Please list restaurants that received high ratings at past events as candidates for the next event," and relevant candidates are selected.

[0749] Step 3:

[0750] The terminal collects real-time emotional data during the event using the user's smart device. It receives user facial expressions and voice data as input via an emotion analysis sensor, and sends an emotional evaluation of that moment to the server as output. Specifically, the terminal transfers emotional information from each user's smartphone to the server via Bluetooth or Wi-Fi, which can then be used to adjust the event environment.

[0751] Step 4:

[0752] The server uses real-time emotional data to adjust the event environment. It receives emotional ratings from each participant as input and generates optimal event environment settings as output. Specifically, it can instruct changes to music and lighting settings, creating a system to enhance the participant experience.

[0753] Step 5:

[0754] The server takes into account the emotional burden on participants when determining cost sharing. It takes emotional data and role assignment information from each participant as input and calculates a fair cost-sharing result as output. For example, it analyzes the emotional stress of the user who acted as the moderator and adjusts the cost accordingly.

[0755] Step 6:

[0756] Users receive the cost sharing results from their device after the event ends. The system receives settlement information sent from the server as input and outputs payment confirmation and adjustments. Specifically, users check the settlement results on their device and, if they have any questions, communicate with the event organizer through the digital platform.

[0757] (Application Example 2)

[0758] 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".

[0759] Planning and running an event is challenging because it accurately reflects the preferences and emotions of all participants and maximizes individual satisfaction. Furthermore, it's necessary to reflect real-time emotional shifts and appropriately adjust the event's atmosphere, but existing systems cannot efficiently accomplish this.

[0760] 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.

[0761] This invention includes a server that automatically selects multiple offering candidates based on participants' past evaluation information and preferences, a means for acquiring participants' emotional data using an emotion engine and monitoring their satisfaction level in real time during the event, and a means for adjusting the service and atmosphere during the event while taking participants' emotions into consideration. This makes it possible to manage events in a way that is attentive to the individual emotions of each participant.

[0762] "Past participant evaluation information" refers to data that records and collects opinions and impressions previously expressed by event participants.

[0763] "Preferences" refer to the elements or conditions that individual participants are particularly interested in, and these serve as criteria for selection and proposals.

[0764] "Possible options" refer to multiple choices or options that are considered when organizing an event.

[0765] "Booking the best option" means determining the most suitable choice based on the participants' conditions and circumstances, and securing the venue and services accordingly.

[0766] A "communication network" is a digital infrastructure used for sending and receiving information.

[0767] "Event attendance confirmation" refers to the act of confirming whether prospective participants intend to attend or not attend an event.

[0768] "Schedule information" is a general term for all temporal information regarding individual participants and the dates and times of events.

[0769] An "emotional engine" is a technology that analyzes participants' emotions and moods and provides that information.

[0770] "Real-time monitoring" means the act of observing and understanding the current situation and data in real time.

[0771] "Adjusting the atmosphere of an event" refers to changing the environment and services offered in accordance with the emotions and circumstances of the participants, in order to provide a more appropriate experience.

[0772] In the system for realizing this invention, the server automatically selects multiple potential events based on each participant's past evaluation information and preferences. The system then determines the best option from the selected candidates and makes a reservation via a communication network. Furthermore, it automatically obtains attendance confirmations for events and integrates the schedule information of prospective participants to determine the optimal event date.

[0773] The server utilizes an emotion engine to monitor participants' emotions in real time. This allows for adjustments to the atmosphere and services during the event to enhance participant satisfaction. Furthermore, it facilitates smooth event operation by using emotion data to ensure fair cost sharing and provide necessary payment information.

[0774] The implementation will utilize devices such as smart assistant robots. From a software perspective, it is crucial to analyze participants' past emotional data using generative AI models and emotion engine APIs to provide real-time feedback.

[0775] For example, in the case of a home party with friends in the neighborhood, the system suggests the most suitable party theme based on past reviews from participants and automatically makes the necessary reservations. During the event, the device analyzes the participants' emotions and makes adjustments such as playing music if a relaxed atmosphere is needed. This kind of operation improves participant satisfaction.

[0776] Example prompt: "Can you suggest a theme for our next party? Please consider the participants' past event reviews and recent moods when making your choice."

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The server retrieves data from a database of past event participant ratings and preferences. The input is participant identification information, which is used to search for relevant past event ratings. The output is rating information and preference data organized for each participant. This enables the selection of personalized offerings.

[0780] Step 2:

[0781] The server uses a generative AI model to select multiple offering candidates that are best suited to the participant from the input data. This process involves data processing to reflect past evaluation information and participant preferences, and prioritization. The output is a list of recommended offering candidates, which prompts the user to make a selection.

[0782] Step 3:

[0783] The server automatically reserves the most suitable option from the selected candidates. The input is a list of candidates selected by the user, and the output is confirmation information for the reservation completion. This includes details of the reservation and the schedule based on it.

[0784] Step 4:

[0785] The server automatically obtains attendance confirmations from users via the communication network. Inputs include a participant list and attendance confirmation messages for each participant. Output is a list of participants who have confirmed their attendance. Based on this, scheduling adjustments are made.

[0786] Step 5:

[0787] The server integrates participants' schedule information to determine the optimal event date. The input is schedule data collected from multiple participants, and the output is the optimal event date. This allows for efficient scheduling.

[0788] Step 6:

[0789] The device utilizes an emotion engine to acquire and analyze participants' emotional data in real time during an event. The input is the emotional data collected from participants in real time, and the output is an analysis result showing emotional trends. Based on this, feedback for the event can be obtained.

[0790] Step 7:

[0791] The server adjusts the service and atmosphere during an event based on emotional data. The input is the emotional trend analyzed by the emotion engine, and the output is a specific action plan for the necessary adjustments. This enables concrete measures to improve participant satisfaction.

[0792] 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.

[0793] 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.

[0794] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] A method for automatically selecting multiple offering candidates based on participants' past evaluation information and preferences,

[0816] A means of reserving the best candidate from the selected candidates,

[0817] A means of automatically obtaining attendance confirmation for an event via a communication network,

[0818] A method for determining the optimal event date by integrating the schedule information of prospective participants,

[0819] A means of providing participants with detailed information about the event,

[0820] A means of dynamically calculating costs based on the participants' roles and providing information for payment,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which uses generative AI to prioritize participants based on their past data in the process of proposing and selecting candidates for provision.

[0824] (Claim 3)

[0825] The system according to claim 1, which automatically transmits attendance confirmations and event announcements via a communication platform.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A means of automatically selecting multiple offering candidates using an information processing device based on participants' past evaluation information and preferences,

[0829] A method for reserving the most suitable candidate from the automatically selected list of candidates,

[0830] A means of automatically acquiring event attendance information via a communication channel,

[0831] A method for integrating the time information of prospective participants to determine the optimal event date and time,

[0832] A means of providing participants with detailed event information via information terminals,

[0833] A means of dynamically calculating costs based on participant role information and providing payment information to the information terminal,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which uses a generative algorithm model to evaluate priorities from participants' past data in the process of proposing and selecting candidates for provision.

[0837] (Claim 3)

[0838] The system according to claim 1, which automatically transmits attendance information and event instructions to participants via a communication device.

[0839] "Application Example 1"

[0840] (Claim 1)

[0841] A method for automatically selecting multiple event candidates based on participants' past evaluation information and preferences,

[0842] A method for booking the best candidate from the selected event candidates,

[0843] A means of automatically obtaining attendance confirmation for an event via a communication network,

[0844] A method for determining the optimal event date by integrating the schedule information of prospective participants,

[0845] A means of providing participants with detailed event information and dynamically guiding them to the meeting place,

[0846] A means of dynamically calculating costs based on the participants' roles and providing information for electronic payment,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which uses generative AI to prioritize participants' past data and propose similar events in the process of proposing and selecting candidates for provision.

[0850] (Claim 3)

[0851] The system according to claim 1, which automatically transmits attendance confirmation, event information, and meeting place information via a communication platform, and supports detailed payment procedures.

[0852] "Example 2 of combining an emotion engine"

[0853] (Claim 1)

[0854] A method for automatically selecting multiple offering candidates based on participants' past evaluation information and preferences,

[0855] A means of reserving the best candidate from the selected candidates,

[0856] A means of automatically obtaining attendance confirmation for an event via a communication network,

[0857] A method for determining the optimal event date by integrating the schedule information of prospective participants,

[0858] A means of providing participants with detailed information about the event,

[0859] A means of dynamically calculating costs based on the participants' roles and providing information for payment,

[0860] A means of acquiring emotional data and adjusting the environment in real time during an event based on the participants' emotions,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which uses a generative AI to combine participants' past data with their real-time emotional state to select more accurate candidates for offerings.

[0864] (Claim 3)

[0865] The system according to claim 1, which dynamically adjusts cost sharing that takes into account the emotional burden of participants in the calculation.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A method for automatically selecting multiple offering candidates based on participants' past evaluation information and preferences,

[0869] A means of reserving the best candidate from the selected candidates,

[0870] A means of automatically obtaining attendance confirmation for an event via a communication network,

[0871] A method for determining the optimal event date by integrating the schedule information of prospective participants,

[0872] A means of providing participants with detailed information about the event,

[0873] A means of dynamically calculating costs based on the participants' roles and providing information for payment,

[0874] A method for acquiring participant emotional data using an emotion engine and monitoring satisfaction levels during the event in real time,

[0875] Means of adjusting services and atmosphere during an event while taking participants' feelings into consideration,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which uses generative AI to prioritize participants based on their past data in the process of proposing and selecting candidates for provision.

[0879] (Claim 3)

[0880] The system according to claim 1, which automatically transmits attendance confirmations and event announcements via a communication platform. [Explanation of symbols]

[0881] 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 automatically selecting multiple offering candidates based on participants' past evaluation information and preferences, A means of reserving the best candidate from the selected candidates, A means of automatically obtaining attendance confirmation for an event via a communication network, A method for determining the optimal event date by integrating the schedule information of prospective participants, A means of providing participants with detailed information about the event, A means of dynamically calculating costs based on the participants' roles and providing information for payment, A system that includes this.

2. The system according to claim 1, which uses generative AI to prioritize participants based on their past data in the process of proposing and selecting candidates for provision.

3. The system according to claim 1, which automatically transmits attendance confirmations and event announcements via a communication platform.

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