System

Abstract

An object of the system according to the embodiment is to efficiently perform schedule adjustment and membership fee management of community registrants.SOLUTION: A system includes a schedule adjustment unit, a guidance notification unit, and a membership fee management unit. A schedule adjustment part analyzes the schedule of the community registrant and proposes an optimum schedule. The guidance notification unit transmits a notification to the participant based on the schedule and the place determined by the schedule adjustment unit. A membership fee management part automatically calculates the membership fee of the event and manages payment from the participants.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that coordinating schedules and managing membership fees for community members is time-consuming and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently manage schedules and membership fees for community members. [Means for solving the problem]

[0006] The system according to the embodiment includes a schedule adjustment unit, a notification unit, and a fee management unit. The schedule adjustment unit analyzes the schedules of community members and proposes optimal dates. The notification unit sends notifications to participants based on the dates and locations determined by the schedule adjustment unit. The fee management unit automatically calculates event fees and manages payments from participants. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage schedules and membership fees for community members. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The total solution package according to the embodiment of the present invention is a system that allows community members to easily and effortlessly adjust schedules, set up and reserve venues, send notifications, manage participants, and process membership fees and payments. As a result, the total solution package can improve the efficiency and convenience of community activities.

[0029] The total solution package according to the embodiment includes a schedule adjustment unit, an information notification unit, and a fee management unit. The schedule adjustment unit analyzes the schedules of community members and proposes an optimal schedule. For example, the generation AI references the calendars of community members and automatically selects a date and time when all members can attend. The schedule adjustment unit also reserves a venue and secures an appropriate location. For example, the generation AI proposes an optimal date and location based on prompts containing the user's desired date and location conditions. The information notification unit sends information to participants based on the date and location determined by the schedule adjustment unit. For example, the generation AI notifies participants of event details via email or a messaging app. The information notification unit also confirms participant attendance and automatically updates the participant list. For example, the generation AI collects participant attendance information and updates the list in real time. The fee management unit automatically calculates event fees and manages payments from participants. For example, the generation AI enables participants to easily pay the fee using QR code payment or electronic money. The fee management unit also monitors payment status in real time and sends reminders to unpaid participants. For example, the generation AI automatically sends reminders when the payment deadline approaches. This allows the total solution package according to the embodiment to improve the efficiency and convenience of community activities. For example, users can plan and manage events without hassle, and participants can smoothly participate in events.

[0030] The schedule adjustment unit learns the user's past participation history and preferences and can suggest optimal dates and locations. For example, the generation AI in the schedule adjustment unit analyzes the user's past event participation history and learns frequently used locations and preferred dates. For example, if there is a high level of participation on a particular day of the week or during a particular time period, the generation AI in the schedule adjustment unit suggests optimal dates based on that pattern. The generation AI in the schedule adjustment unit also suggests optimal locations based on the user's preferences and past feedback. For example, it prioritizes restaurants and conference rooms that have received high ratings in the past. The generation AI in the schedule adjustment unit also references the user's calendar and suggests optimal dates that do not overlap with past events. For example, it adjusts the dates so that they do not overlap with important meetings or family events. This makes it possible to suggest optimal dates and locations based on the user's preferences.

[0031] The schedule adjustment unit can propose the optimal date and location based on the weather forecast data, taking into account the weather. For example, the generation AI of the schedule adjustment unit obtains weather forecast data in real time, and for outdoor events, prioritizes proposing days with good weather. For example, it avoids days when rain is predicted and selects a sunny day. The schedule adjustment unit also proposes indoor and outdoor options based on the weather forecast data. For example, if rain is predicted, it proposes an indoor venue, and if sunny weather is predicted, it proposes an outdoor venue. The generation AI of the schedule adjustment unit also refers to the weather forecast data and adjusts the start time of the event. For example, on a hot summer day, it proposes a cooler time period in the evening or later. This makes it possible to propose the optimal date and location based on the weather.

[0032] The schedule adjustment unit can coordinate events between different communities and propose dates to avoid overlaps. For example, the generation AI analyzes the schedules of multiple communities and adjusts the schedule to avoid overlapping events. For example, it adjusts the schedule so that multiple events do not overlap on the same day. The schedule adjustment unit also coordinates events between different communities and proposes dates so that participants can attend multiple events. For example, it schedules one event in the morning and another event in the afternoon. The schedule adjustment unit also updates the event schedules between communities in real time and proposes optimal dates to avoid overlaps. For example, it adjusts the schedule to accommodate newly added events. This makes it possible to avoid overlapping events between different communities.

[0033] The schedule adjustment unit can refer to the user's health data and suggest optimal dates and locations based on their health condition. For example, the generation AI analyzes the user's health data, selects a time when the user is in good health, and suggests a schedule. For example, it selects a time when the user is in good health, avoiding times when the user is prone to catching a cold. The schedule adjustment unit also suggests locations that take the user's health condition into consideration based on the health data. For example, if the user has allergies, it selects a location with fewer allergens. The schedule adjustment unit also refers to the user's health data in real time and adjusts the schedule based on their physical condition. For example, it avoids times when the user is tired and selects a time when the user can refresh themselves. This makes it possible to suggest optimal dates and locations based on the user's health condition.

[0034] The notification unit can analyze participants' past attendance data and suggest the optimal notification timing to improve attendance rates. For example, the generation AI of the notification unit analyzes participants' past attendance data and identifies the optimal notification timing. For example, it sends notifications based on the time of day or day of the week when participants receive notifications. The notification unit also sends notifications at a time when participants are most likely to respond based on the attendance data. For example, it selects a time period that has shown high attendance rates in the past. The notification unit also uses the generation AI to refer to participants' schedules and suggest the optimal notification timing. For example, it selects a time period when participants are not busy and sends the notification. This makes it possible to suggest the optimal notification timing to improve attendance rates.

[0035] The notification unit can learn the communication styles of participants and send individually optimized notification notifications. For example, the generation AI of the notification unit analyzes the communication styles of participants and identifies the optimal notification method. For example, it learns preferences such as email, messaging app, and phone. The notification unit also sends individually optimized notification notifications based on participants' past response data. For example, it sends concise notifications to participants who prefer short messages. The notification unit also learns the communication styles of participants in real time using the generation AI and suggests the optimal notification method. For example, it selects the method that participants are most likely to respond to. This makes it possible to send individually optimized notification notifications.

[0036] The information notification unit can automatically generate information notifications in different languages ​​to accommodate international participants. For example, the generation AI can automatically generate information notifications in different languages, building a system that can accommodate international participants. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The information notification unit can also automatically generate information notifications in different languages ​​to provide consistent information to international participants. For example, it can provide event details and participation methods in multiple languages. The information notification unit also uses the generation AI to refer to the participant's language settings and send information notifications in the most appropriate language. For example, it can send notifications in the participant's native language. This makes it possible to accommodate international participants.

[0037] The notification unit can refer to the participants' social media accounts and send individually customized notification notifications. For example, the generation AI analyzes the participants' social media accounts and sends individually customized notification notifications. For example, it includes content based on the participants' interests. The notification unit also creates an optimal notification for the participants based on social media data. For example, it includes information related to topics and events that the participants are interested in. The notification unit also refers to the participants' social media activity in real time and sends individually customized notification notifications. For example, it reflects content that the participants have recently shown interest in. This makes it possible to send individually customized notification notifications.

[0038] The membership fee management department can analyze participants' past payment history and suggest the most suitable payment method. For example, the generation AI analyzes participants' past payment history and suggests the most suitable payment method. For example, it presents options such as credit card, electronic money, and QR code payment. The membership fee management department also suggests the payment method that is easiest for participants to use based on their payment history. For example, it will prioritize the payment method that has been used most frequently in the past. The membership fee management department also uses the generation AI to refer to participants' payment history in real time and suggest the most suitable payment method. For example, it will make suggestions based on the payment method that the participant has used recently. This makes it possible to suggest the most suitable payment method.

[0039] The fee management department can propose flexible payment plans taking into account the participant's financial situation. For example, the generation AI in the fee management department analyzes the participant's financial situation and proposes flexible payment plans. For example, it presents options such as installment payments, deferred payment, and discount plans. The fee management department also proposes payment plans that participants will not feel burdened by based on their financial situation. For example, it references income and expenditure data to select the optimal plan. The generation AI in the fee management department also references the participant's financial situation in real time and proposes flexible payment plans. For example, it presents plans that respond to changes in the participant's financial situation. This makes it possible to propose flexible payment plans.

[0040] The fee management unit automatically processes payments in different currencies and can accommodate international participants. For example, the generation AI automatically processes payments in different currencies, building a system that can accommodate international participants. For example, it supports multiple currencies such as dollars, euros, and yen. The fee management unit also automatically processes payments in different currencies and reflects exchange rates in real time. For example, it calculates the payment amount based on the latest exchange rate. The generation AI also references the participant's currency settings and suggests the most appropriate currency for payment. For example, it selects the currency based on the participant's country of residence. This allows it to accommodate international participants.

[0041] The membership fee management department can refer to the participant's point program and suggest payment using points. For example, the generation AI in the membership fee management department analyzes the participant's point program and suggests payment using points. For example, using credit card points or mileage. The membership fee management department also suggests the most cost-effective payment method for the participant based on the point program. For example, converting points into cash and using them for payment. The generation AI in the membership fee management department also refers to the participant's point balance in real time and suggests payment using points. For example, it suggests using points before they expire. This makes it possible to suggest payment using points.

[0042] The system can automatically organize photos and videos taken during an event and create the most suitable album. For example, the system's generative AI can automatically organize photos and videos taken during an event and create the most suitable album. For example, it can select important moments and highlights and compile them into an album. The system can also analyze the metadata of photos and videos to create an album that follows the flow of the event. For example, it can sort them chronologically to create an album with a story. The system's generative AI can also create the most suitable album based on participant feedback. For example, it can prioritize including photos and videos that participants have given high ratings in the album. This allows the system to automatically organize photos and videos taken during an event and create the most suitable album.

[0043] The system can collect participant feedback and automatically generate event highlights. For example, the generation AI of the system collects participant feedback and automatically generates event highlights. For example, highlights are created based on moments that were particularly memorable to participants. The system also analyzes the feedback data to identify important moments of the event and generate highlights. For example, it selects scenes where participants left many comments. The generation AI of the system also automatically generates event highlights based on real-time feedback from participants. For example, it compiles moments that received high ratings during the event as highlights. In this way, event highlights can be automatically generated.

[0044] The system can automatically share across different platforms, enabling broader access. For example, the system uses a generation AI to automatically share across different platforms, enabling broader access. For example, it supports multiple platforms, including social media, cloud storage, and email. The system also automatically shares across different platforms, allowing easy access for all participants. For example, it uploads event photos and videos all at once. The system also uses a generation AI to refer to participants' platform settings and suggest the optimal sharing method. For example, it prioritizes sharing on the platform most used by participants. This enables broader access.

[0045] The system can refer to participants' social media accounts and share memories that are individually customized. For example, the system's generation AI analyzes participants' social media accounts and shares memories that are individually customized. For example, it may include content based on the participants' interests. The system also suggests the best way to share memories for participants based on social media data. For example, it may include information related to topics or events that interest participants. The system also uses the generation AI to refer to participants' social media activity in real time and share memories that are individually customized. For example, it may reflect content that the participant has recently shown interest in. This makes it possible to share memories that are individually customized.

[0046] The system can analyze the relationships between participants and suggest the optimal seating arrangement. For example, the generation AI of the system analyzes the relationships between participants and suggests the optimal seating arrangement. For example, placing friends or colleagues close to each other. The system also suggests a seating arrangement that will make participants comfortable based on relationship data. For example, it refers to feedback from past events. The generation AI of the system also refers to the relationships between participants in real time and suggests the optimal seating arrangement. For example, it suggests an arrangement that corresponds to changes in the relationships between participants. This makes it possible to suggest the optimal seating arrangement that takes into account the relationships between participants.

[0047] The system can refer to participants' past seating arrangement data and propose the optimal arrangement. For example, the generation AI of the system analyzes participants' past seating arrangement data and proposes the optimal arrangement. For example, it refers to arrangements that have been well received in the past. The system can also propose an arrangement that will make participants comfortable based on the seating arrangement data. For example, it refers to feedback from past events. The generation AI of the system can also refer to participants' past seating arrangement data in real time and propose the optimal arrangement. For example, it can propose an arrangement that corresponds to changes in the relationships between participants. This makes it possible to propose the optimal arrangement based on past seating arrangement data.

[0048] The system can automatically generate seating arrangements that correspond to different event formats. For example, the system constructs a system in which a generation AI automatically generates seating arrangements that correspond to different event formats. For example, it proposes arrangements that correspond to the format, such as a conference, party, seminar, etc. The system also automatically generates the optimal seating arrangement according to the event format. For example, it proposes a round table arrangement for a conference format and an open seating arrangement for a party format. The system also uses a generation AI to analyze the event format in real time and automatically generate the optimal seating arrangement. For example, it proposes an arrangement that corresponds to the number of participants and the purpose. This makes it possible to automatically generate seating arrangements that correspond to different event formats.

[0049] The system can refer to participants' health data and suggest seating arrangements according to their health condition. For example, the system's generating AI can analyze participants' health data and suggest seating arrangements according to their health condition. For example, participants with allergies can be placed in a specific location. The system can also suggest seating arrangements that will make participants comfortable based on their health data. For example, it can select an arrangement that takes wheelchair users into consideration. The system can also use the generating AI to refer to participants' health data in real time and suggest seating arrangements according to their health condition. For example, participants who are not feeling well can be placed closer to the entrance / exit. This makes it possible to suggest seating arrangements according to health condition.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The schedule adjustment unit can refer to the user's health data and suggest the optimal date and location based on their health condition. For example, the generation AI analyzes the user's health data, selects a time when the user is in good health, and suggests a date. For example, it selects a time when the user is in good health, avoiding times when the user is prone to catching a cold. The schedule adjustment unit also suggests a location that takes the user's health condition into consideration based on the health data. For example, if the user has allergies, it selects a location with fewer allergens. The schedule adjustment unit also refers to the user's health data in real time and adjusts the schedule based on their physical condition. For example, it avoids times when the user is tired and selects a time when the user can refresh themselves. This makes it possible to suggest the optimal date and location based on the user's health condition.

[0052] The schedule adjustment unit can coordinate events between different communities and suggest dates to avoid overlaps. For example, the generation AI analyzes the schedules of multiple communities and adjusts the schedule to avoid overlapping events. For example, adjustments are made to prevent multiple events from overlapping on the same day. The schedule adjustment unit also coordinates events between different communities and suggests dates that allow participants to attend multiple events. For example, one event is scheduled in the morning and another event in the afternoon. The schedule adjustment unit also updates the event schedules between communities in real time and suggests optimal dates to avoid overlaps. For example, the schedule is adjusted to accommodate newly added events. This makes it possible to avoid overlapping events between different communities.

[0053] The notification unit can analyze participants' past attendance data and suggest the optimal notification timing to improve attendance rates. For example, the generation AI analyzes participants' past attendance data and identifies the optimal notification timing. For example, notifications are sent based on the time of day or day of the week that participants receive notifications. The notification unit also sends notifications at a time when participants are most likely to respond based on the attendance data. For example, it selects a time period that has shown high attendance rates in the past. The notification unit also uses the generation AI to refer to participants' schedules and suggest the optimal notification timing. For example, it selects a time period when participants are not busy and sends the notification. This makes it possible to suggest the optimal notification timing to improve attendance rates.

[0054] The notification unit can learn the communication styles of participants and send individually optimized notification methods. For example, the generation AI analyzes participants' communication styles and identifies the optimal notification method. For example, it learns preferences such as email, messaging apps, and phone calls. The notification unit also sends individually optimized notification methods based on participants' past response data. For example, it sends concise notifications to participants who prefer short messages. The notification unit also learns participants' communication styles in real time and suggests the optimal notification method. For example, it selects the method that participants are most likely to respond to. This makes it possible to send individually optimized notification methods.

[0055] The information notification unit can automatically generate information notifications in different languages ​​to accommodate international participants. For example, the generation AI can automatically generate information notifications in different languages, building a system that can accommodate international participants. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The information notification unit can also automatically generate information notifications in different languages ​​to provide consistent information to international participants. For example, it can provide event details and participation methods in multiple languages. The information notification unit also uses the generation AI to refer to the participant's language settings and send information notifications in the most appropriate language. For example, it can send notifications in the participant's native language. This makes it possible to accommodate international participants.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The schedule adjustment unit analyzes the schedules of community members and proposes the optimal date. For example, the generation AI references the calendars of community members and automatically selects a date and time when everyone can attend. The schedule adjustment unit also reserves the venue and secures an appropriate location. For example, the generation AI proposes the optimal date and location based on prompts that include the user's desired date and location conditions. Step 2: The notification unit sends notification to participants based on the date and location determined by the schedule adjustment unit. For example, the generation AI notifies participants of the event details via email or a messaging app. The notification unit also confirms participants' attendance and automatically updates the participant list. For example, the generation AI collects participants' attendance information and updates the list in real time. Step 3: The fee management unit automatically calculates the event fee and manages payments from participants. For example, the generation AI can use QR code payments or electronic money to make it easy for participants to pay the fee. The fee management unit also monitors payment status in real time and sends reminders to participants who have not paid. For example, the generation AI can automatically send reminders when the payment deadline approaches.

[0058] (Example 2) The total solution package according to the embodiment of the present invention is a system that allows community members to easily and effortlessly adjust schedules, set up and reserve venues, send notifications, manage participants, and process membership fees and payments. As a result, the total solution package can improve the efficiency and convenience of community activities.

[0059] The total solution package according to the embodiment includes a schedule adjustment unit, an information notification unit, and a fee management unit. The schedule adjustment unit analyzes the schedules of community members and proposes an optimal schedule. For example, the generation AI references the calendars of community members and automatically selects a date and time when all members can attend. The schedule adjustment unit also reserves a venue and secures an appropriate location. For example, the generation AI proposes an optimal date and location based on prompts containing the user's desired date and location conditions. The information notification unit sends information to participants based on the date and location determined by the schedule adjustment unit. For example, the generation AI notifies participants of event details via email or a messaging app. The information notification unit also confirms participant attendance and automatically updates the participant list. For example, the generation AI collects participant attendance information and updates the list in real time. The fee management unit automatically calculates event fees and manages payments from participants. For example, the generation AI enables participants to easily pay the fee using QR code payment or electronic money. The fee management unit also monitors payment status in real time and sends reminders to unpaid participants. For example, the generation AI automatically sends reminders when the payment deadline approaches. This allows the total solution package according to the embodiment to improve the efficiency and convenience of community activities. For example, users can plan and manage events without hassle, and participants can smoothly participate in events.

[0060] The schedule adjustment unit learns the user's past participation history and preferences and can suggest optimal dates and locations. For example, the generation AI in the schedule adjustment unit analyzes the user's past event participation history and learns frequently used locations and preferred dates. For example, if there is a high level of participation on a particular day of the week or during a particular time period, the generation AI in the schedule adjustment unit suggests optimal dates based on that pattern. The generation AI in the schedule adjustment unit also suggests optimal locations based on the user's preferences and past feedback. For example, it prioritizes restaurants and conference rooms that have received high ratings in the past. The generation AI in the schedule adjustment unit also references the user's calendar and suggests optimal dates that do not overlap with past events. For example, it adjusts the dates so that they do not overlap with important meetings or family events. This makes it possible to suggest optimal dates and locations based on the user's preferences.

[0061] The schedule adjustment unit can propose the optimal date and location based on the weather forecast data, taking into account the weather. For example, the generation AI of the schedule adjustment unit obtains weather forecast data in real time, and for outdoor events, prioritizes proposing days with good weather. For example, it avoids days when rain is predicted and selects a sunny day. The schedule adjustment unit also proposes indoor and outdoor options based on the weather forecast data. For example, if rain is predicted, it proposes an indoor venue, and if sunny weather is predicted, it proposes an outdoor venue. The generation AI of the schedule adjustment unit also refers to the weather forecast data and adjusts the start time of the event. For example, on a hot summer day, it proposes a cooler time period in the evening or later. This makes it possible to propose the optimal date and location based on the weather.

[0062] The schedule adjustment unit can use the emotion estimation function to suggest less stressful dates and locations based on the user's emotional state. For example, the schedule adjustment unit uses the emotion estimation function to analyze the user's stress level and suggest dates and locations where the user can relax. For example, it selects a time when the user has more free time, avoiding busy periods. The schedule adjustment unit also monitors the user's emotional state in real time and suggests dates that are determined to be less stressful. For example, it prioritizes selecting times when the user is relaxed. The schedule adjustment unit also suggests places where the user can relax based on the emotion estimation data. For example, it selects a place surrounded by nature or a quiet cafe. This makes it possible to suggest optimal dates and locations that take the user's emotional state into consideration.

[0063] The schedule adjustment unit can coordinate events between different communities and propose dates to avoid overlaps. For example, the generation AI analyzes the schedules of multiple communities and adjusts the schedule to avoid overlapping events. For example, it adjusts the schedule so that multiple events do not overlap on the same day. The schedule adjustment unit also coordinates events between different communities and proposes dates so that participants can attend multiple events. For example, it schedules one event in the morning and another event in the afternoon. The schedule adjustment unit also updates the event schedules between communities in real time and proposes optimal dates to avoid overlaps. For example, it adjusts the schedule to accommodate newly added events. This makes it possible to avoid overlapping events between different communities.

[0064] The schedule adjustment unit can refer to the user's health data and suggest optimal dates and locations based on their health condition. For example, the generation AI analyzes the user's health data, selects a time when the user is in good health, and suggests a schedule. For example, it selects a time when the user is in good health, avoiding times when the user is prone to catching a cold. The schedule adjustment unit also suggests locations that take the user's health condition into consideration based on the health data. For example, if the user has allergies, it selects a location with fewer allergens. The schedule adjustment unit also refers to the user's health data in real time and adjusts the schedule based on their physical condition. For example, it avoids times when the user is tired and selects a time when the user can refresh themselves. This makes it possible to suggest optimal dates and locations based on the user's health condition.

[0065] The notification unit can analyze participants' past attendance data and suggest the optimal notification timing to improve attendance rates. For example, the generation AI of the notification unit analyzes participants' past attendance data and identifies the optimal notification timing. For example, it sends notifications based on the time of day or day of the week when participants receive notifications. The notification unit also sends notifications at a time when participants are most likely to respond based on the attendance data. For example, it selects a time period that has shown high attendance rates in the past. The notification unit also uses the generation AI to refer to participants' schedules and suggest the optimal notification timing. For example, it selects a time period when participants are not busy and sends the notification. This makes it possible to suggest the optimal notification timing to improve attendance rates.

[0066] The notification unit can learn the communication styles of participants and send individually optimized notification notifications. For example, the generation AI of the notification unit analyzes the communication styles of participants and identifies the optimal notification method. For example, it learns preferences such as email, messaging app, and phone. The notification unit also sends individually optimized notification notifications based on participants' past response data. For example, it sends concise notifications to participants who prefer short messages. The notification unit also learns the communication styles of participants in real time using the generation AI and suggests the optimal notification method. For example, it selects the method that participants are most likely to respond to. This makes it possible to send individually optimized notification notifications.

[0067] The notification unit can use the emotion estimation function to create a notification that takes into account the emotional state of the participants and elicits a positive response. The notification unit, for example, uses the emotion estimation function to analyze the emotional state of the participants and create a notification that elicits a positive response. For example, the notification unit sends a notification during a time period when the participants are relaxed. The notification unit also monitors the emotional state of the participants in real time and suggests notification content that elicits a positive response. For example, it includes encouraging messages and positive words. The notification unit also creates a notification that makes the participants feel positive emotions based on the emotion estimation data. For example, it refers to notification content that has been well received in the past. This makes it possible to create a positive notification that takes into account the emotional state of the participants.

[0068] The information notification unit can automatically generate information notifications in different languages ​​to accommodate international participants. For example, the generation AI can automatically generate information notifications in different languages, building a system that can accommodate international participants. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The information notification unit can also automatically generate information notifications in different languages ​​to provide consistent information to international participants. For example, it can provide event details and participation methods in multiple languages. The information notification unit also uses the generation AI to refer to the participant's language settings and send information notifications in the most appropriate language. For example, it can send notifications in the participant's native language. This makes it possible to accommodate international participants.

[0069] The notification unit can refer to the participants' social media accounts and send individually customized notification notifications. For example, the generation AI analyzes the participants' social media accounts and sends individually customized notification notifications. For example, it includes content based on the participants' interests. The notification unit also creates an optimal notification for the participants based on social media data. For example, it includes information related to topics and events that the participants are interested in. The notification unit also refers to the participants' social media activity in real time and sends individually customized notification notifications. For example, it reflects content that the participants have recently shown interest in. This makes it possible to send individually customized notification notifications.

[0070] The notification unit can use the emotion estimation function to monitor the emotional state of participants in real time and send reminders at optimal timing. The notification unit, for example, uses the emotion estimation function to monitor the emotional state of participants in real time and send reminders at optimal timing. For example, it sends reminders during times when participants are relaxed. The notification unit also analyzes the emotional state of participants and creates reminders to elicit positive responses. For example, it includes encouraging messages and positive words. The notification unit also sends reminders based on the emotion estimation data at times when participants are most likely to respond. For example, it selects times when participants have shown high responses in the past. This allows reminders to be sent at optimal timing.

[0071] The membership fee management department can analyze participants' past payment history and suggest the most suitable payment method. For example, the generation AI analyzes participants' past payment history and suggests the most suitable payment method. For example, it presents options such as credit card, electronic money, and QR code payment. The membership fee management department also suggests the payment method that is easiest for participants to use based on their payment history. For example, it will prioritize the payment method that has been used most frequently in the past. The membership fee management department also uses the generation AI to refer to participants' payment history in real time and suggest the most suitable payment method. For example, it will make suggestions based on the payment method that the participant has used recently. This makes it possible to suggest the most suitable payment method.

[0072] The fee management department can propose flexible payment plans taking into account the participant's financial situation. For example, the generation AI in the fee management department analyzes the participant's financial situation and proposes flexible payment plans. For example, it presents options such as installment payments, deferred payment, and discount plans. The fee management department also proposes payment plans that participants will not feel burdened by based on their financial situation. For example, it references income and expenditure data to select the optimal plan. The generation AI in the fee management department also references the participant's financial situation in real time and proposes flexible payment plans. For example, it presents plans that respond to changes in the participant's financial situation. This makes it possible to propose flexible payment plans.

[0073] The membership fee management unit can use the emotion estimation function to design an interface that reduces stress associated with payment. The membership fee management unit, for example, uses the emotion estimation function to design an interface that reduces stress associated with payment. For example, it may adopt a design and colors that allow the user to relax. The membership fee management unit also monitors the user's emotional state in real time during the payment process and provides feedback to reduce stress. For example, it may display encouraging messages or positive words. The membership fee management unit also designs a payment interface that reduces stress for the user based on the emotion estimation data. For example, it may adopt a design that allows for simple and intuitive operation. This makes it possible to design an interface that reduces stress associated with payment.

[0074] The fee management unit automatically processes payments in different currencies and can accommodate international participants. For example, the generation AI automatically processes payments in different currencies, building a system that can accommodate international participants. For example, it supports multiple currencies such as dollars, euros, and yen. The fee management unit also automatically processes payments in different currencies and reflects exchange rates in real time. For example, it calculates the payment amount based on the latest exchange rate. The generation AI also references the participant's currency settings and suggests the most appropriate currency for payment. For example, it selects the currency based on the participant's country of residence. This allows it to accommodate international participants.

[0075] The membership fee management department can refer to the participant's point program and suggest payment using points. For example, the generation AI in the membership fee management department analyzes the participant's point program and suggests payment using points. For example, using credit card points or mileage. The membership fee management department also suggests the most cost-effective payment method for the participant based on the point program. For example, converting points into cash and using them for payment. The generation AI in the membership fee management department also refers to the participant's point balance in real time and suggests payment using points. For example, it suggests using points before they expire. This makes it possible to suggest payment using points.

[0076] The membership fee management unit can use the emotion estimation function to provide incentives to elicit positive emotions toward payment. The membership fee management unit, for example, uses the emotion estimation function to provide incentives to elicit positive emotions toward payment. For example, it may display a thank you message or a special benefit when payment is completed. The membership fee management unit also monitors the user's emotional state during the payment process in real time and provides incentives to elicit positive emotions. For example, it may grant coupons or points after payment is completed. The membership fee management unit also provides incentives to elicit positive emotions toward payment based on the emotion estimation data. For example, it may provide a surprise gift when payment is completed. In this way, it is possible to provide incentives to elicit positive emotions toward payment.

[0077] The system can automatically organize photos and videos taken during an event and create the most suitable album. For example, the system's generative AI can automatically organize photos and videos taken during an event and create the most suitable album. For example, it can select important moments and highlights and compile them into an album. The system can also analyze the metadata of photos and videos to create an album that follows the flow of the event. For example, it can sort them chronologically to create an album with a story. The system's generative AI can also create the most suitable album based on participant feedback. For example, it can prioritize including photos and videos that participants have given high ratings in the album. This allows the system to automatically organize photos and videos taken during an event and create the most suitable album.

[0078] The system can collect participant feedback and automatically generate event highlights. For example, the generation AI of the system collects participant feedback and automatically generates event highlights. For example, highlights are created based on moments that were particularly memorable to participants. The system also analyzes the feedback data to identify important moments of the event and generate highlights. For example, it selects scenes where participants left many comments. The generation AI of the system also automatically generates event highlights based on real-time feedback from participants. For example, it compiles moments that received high ratings during the event as highlights. In this way, event highlights can be automatically generated.

[0079] The system can use the emotion estimation function to suggest memory sharing methods that reflect the participants' emotional state. For example, the system uses the emotion estimation function to suggest memory sharing methods that reflect the participants' emotional state. For example, photos and videos are shared that focus on moments when participants felt positive emotions. The system also monitors the participants' emotional state in real time and suggests memory sharing methods based on emotions. For example, emotional moments are emphasized and shared. The system also identifies the moments that moved participants most based on the emotion estimation data and shares memories that focus on those moments. For example, scenes with high emotion scores are shared preferentially. This makes it possible to suggest memory sharing methods that reflect the participants' emotional state.

[0080] The system can automatically share across different platforms, enabling broader access. For example, the system uses a generation AI to automatically share across different platforms, enabling broader access. For example, it supports multiple platforms, including social media, cloud storage, and email. The system also automatically shares across different platforms, allowing easy access for all participants. For example, it uploads event photos and videos all at once. The system also uses a generation AI to refer to participants' platform settings and suggest the optimal sharing method. For example, it prioritizes sharing on the platform most used by participants. This enables broader access.

[0081] The system can refer to participants' social media accounts and share memories that are individually customized. For example, the system's generation AI analyzes participants' social media accounts and shares memories that are individually customized. For example, it may include content based on the participants' interests. The system also suggests the best way to share memories for participants based on social media data. For example, it may include information related to topics or events that interest participants. The system also uses the generation AI to refer to participants' social media activity in real time and share memories that are individually customized. For example, it may reflect content that the participant has recently shown interest in. This makes it possible to share memories that are individually customized.

[0082] The system uses an emotion estimation function to monitor participants' emotional states in real time and share memories at the optimal time. For example, the system uses the emotion estimation function to monitor participants' emotional states in real time and share memories at the optimal time. For example, memories are shared when participants are relaxed. The system also analyzes participants' emotional states and suggests ways to share memories that will elicit positive responses. For example, highlighting and sharing moving moments. The system also identifies the moments that moved participants most based on emotion estimation data and shares memories centered around those moments. For example, scenes with high emotion scores are shared preferentially. This allows memories to be shared at the optimal time.

[0083] The system can analyze the relationships between participants and suggest the optimal seating arrangement. For example, the generation AI of the system analyzes the relationships between participants and suggests the optimal seating arrangement. For example, placing friends or colleagues close to each other. The system also suggests a seating arrangement that will make participants comfortable based on relationship data. For example, it refers to feedback from past events. The generation AI of the system also refers to the relationships between participants in real time and suggests the optimal seating arrangement. For example, it suggests an arrangement that corresponds to changes in the relationships between participants. This makes it possible to suggest the optimal seating arrangement that takes into account the relationships between participants.

[0084] The system can refer to participants' past seating arrangement data and propose the optimal arrangement. For example, the generation AI of the system analyzes participants' past seating arrangement data and proposes the optimal arrangement. For example, it refers to arrangements that have been well received in the past. The system can also propose an arrangement that will make participants comfortable based on the seating arrangement data. For example, it refers to feedback from past events. The generation AI of the system can also refer to participants' past seating arrangement data in real time and propose the optimal arrangement. For example, it can propose an arrangement that corresponds to changes in the relationships between participants. This makes it possible to propose the optimal arrangement based on past seating arrangement data.

[0085] The system can use the emotion estimation function to propose seating arrangements that take into account the emotional state of participants. For example, the system uses the emotion estimation function to analyze the emotional state of participants and propose seating arrangements that will allow them to be comfortable. For example, it selects an arrangement that will allow them to relax. The system can also monitor the emotional state of participants in real time and propose seating arrangements based on their emotions. For example, it selects an arrangement that will reduce stress. The system can also propose seating arrangements that will allow participants to be comfortable based on the emotion estimation data. For example, it selects an arrangement that will elicit positive emotions. This makes it possible to propose seating arrangements that take into account the emotional state of participants.

[0086] The system can automatically generate seating arrangements that correspond to different event formats. For example, the system constructs a system in which a generation AI automatically generates seating arrangements that correspond to different event formats. For example, it proposes arrangements that correspond to the format, such as a conference, party, seminar, etc. The system also automatically generates the optimal seating arrangement according to the event format. For example, it proposes a round table arrangement for a conference format and an open seating arrangement for a party format. The system also uses a generation AI to analyze the event format in real time and automatically generate the optimal seating arrangement. For example, it proposes an arrangement that corresponds to the number of participants and the purpose. This makes it possible to automatically generate seating arrangements that correspond to different event formats.

[0087] The system can refer to participants' health data and suggest seating arrangements according to their health condition. For example, the system's generating AI can analyze participants' health data and suggest seating arrangements according to their health condition. For example, participants with allergies can be placed in a specific location. The system can also suggest seating arrangements that will make participants comfortable based on their health data. For example, it can select an arrangement that takes wheelchair users into consideration. The system can also use the generating AI to refer to participants' health data in real time and suggest seating arrangements according to their health condition. For example, participants who are not feeling well can be placed closer to the entrance / exit. This makes it possible to suggest seating arrangements according to health condition.

[0088] The system can use the emotion estimation function to monitor the emotional state of participants in real time and continuously adjust the optimal seating arrangement. For example, the system uses the emotion estimation function to build a system that monitors the emotional state of participants in real time and continuously adjusts the optimal seating arrangement. For example, the arrangement is readjusted if the participant's emotion changes. The system also analyzes the emotional state of participants and continuously adjusts the optimal seating arrangement during the event. For example, it moves a participant who is feeling stressed to a place where they can relax. The system also continuously adjusts the seating arrangement based on the emotion estimation data to ensure that participants are comfortable. For example, it maintains an arrangement that elicits positive emotions. This allows the system to continuously adjust the optimal seating arrangement.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The schedule adjustment unit can refer to the user's health data and suggest the optimal date and location based on their health condition. For example, the generation AI analyzes the user's health data, selects a time when the user is in good health, and suggests a date. For example, it selects a time when the user is in good health, avoiding times when the user is prone to catching a cold. The schedule adjustment unit also suggests a location that takes the user's health condition into consideration based on the health data. For example, if the user has allergies, it selects a location with fewer allergens. The schedule adjustment unit also refers to the user's health data in real time and adjusts the schedule based on their physical condition. For example, it avoids times when the user is tired and selects a time when the user can refresh themselves. This makes it possible to suggest the optimal date and location based on the user's health condition.

[0091] The schedule adjustment unit can coordinate events between different communities and suggest dates to avoid overlaps. For example, the generation AI analyzes the schedules of multiple communities and adjusts the schedule to avoid overlapping events. For example, adjustments are made to prevent multiple events from overlapping on the same day. The schedule adjustment unit also coordinates events between different communities and suggests dates that allow participants to attend multiple events. For example, one event is scheduled in the morning and another event in the afternoon. The schedule adjustment unit also updates the event schedules between communities in real time and suggests optimal dates to avoid overlaps. For example, the schedule is adjusted to accommodate newly added events. This makes it possible to avoid overlapping events between different communities.

[0092] The notification unit can analyze participants' past attendance data and suggest the optimal notification timing to improve attendance rates. For example, the generation AI analyzes participants' past attendance data and identifies the optimal notification timing. For example, notifications are sent based on the time of day or day of the week that participants receive notifications. The notification unit also sends notifications at a time when participants are most likely to respond based on the attendance data. For example, it selects a time period that has shown high attendance rates in the past. The notification unit also uses the generation AI to refer to participants' schedules and suggest the optimal notification timing. For example, it selects a time period when participants are not busy and sends the notification. This makes it possible to suggest the optimal notification timing to improve attendance rates.

[0093] The notification unit can learn the communication styles of participants and send individually optimized notification methods. For example, the generation AI analyzes participants' communication styles and identifies the optimal notification method. For example, it learns preferences such as email, messaging apps, and phone calls. The notification unit also sends individually optimized notification methods based on participants' past response data. For example, it sends concise notifications to participants who prefer short messages. The notification unit also learns participants' communication styles in real time and suggests the optimal notification method. For example, it selects the method that participants are most likely to respond to. This makes it possible to send individually optimized notification methods.

[0094] The information notification unit can automatically generate information notifications in different languages ​​to accommodate international participants. For example, the generation AI can automatically generate information notifications in different languages, building a system that can accommodate international participants. For example, it can translate into multiple languages ​​such as English, French, and Chinese. The information notification unit can also automatically generate information notifications in different languages ​​to provide consistent information to international participants. For example, it can provide event details and participation methods in multiple languages. The information notification unit also uses the generation AI to refer to the participant's language settings and send information notifications in the most appropriate language. For example, it can send notifications in the participant's native language. This makes it possible to accommodate international participants.

[0095] The schedule adjustment unit can use the emotion estimation function to suggest less stressful dates and locations based on the user's emotional state. For example, the emotion estimation function can be used to analyze the user's stress level and suggest dates and locations where the user can relax. For example, the emotion estimation function can be used to select a time when the user has more free time, avoiding busy periods. The schedule adjustment unit can also monitor the user's emotional state in real time and suggest dates that are deemed less stressful. For example, the schedule adjustment unit can prioritize selecting times when the user is relaxed. The schedule adjustment unit can also suggest locations where the user can relax based on the emotion estimation data. For example, the schedule adjustment unit can select a place surrounded by nature or a quiet cafe. This makes it possible to suggest optimal dates and locations that take the user's emotional state into consideration.

[0096] The notification unit can use the emotion estimation function to create notification messages that take into account the emotional state of participants and elicit positive responses. For example, the emotion estimation function can be used to analyze the emotional state of participants and create notification messages that elicit positive responses. For example, notifications can be sent during times when participants are relaxed. The notification unit can also monitor the emotional state of participants in real time and suggest notification content that elicits positive responses. For example, encouraging messages and positive words can be included. The notification unit can also create notification messages that make participants feel positive emotions based on the emotion estimation data. For example, it can refer to notification content that has been well received in the past. This makes it possible to create positive notification messages that take into account the emotional state of participants.

[0097] The notification unit can use the emotion estimation function to monitor the emotional state of participants in real time and send reminders at the optimal timing. For example, the emotion estimation function can be used to monitor the emotional state of participants in real time and send reminders at the optimal timing. For example, reminders can be sent during times when participants are relaxed. The notification unit can also analyze the emotional state of participants and create reminders to elicit positive responses. For example, encouraging messages or positive words can be included. The notification unit can also send reminders based on the emotion estimation data at times when participants are most likely to respond. For example, it can select times when participants have shown high responses in the past. This allows reminders to be sent at the optimal timing.

[0098] The membership fee management unit can use the emotion estimation function to design an interface that reduces stress when making payments. For example, the emotion estimation function can be used to design an interface that reduces stress when making payments, for example, by adopting a design and color scheme that allows the user to relax. The membership fee management unit can also monitor the user's emotional state in real time during the payment process and provide feedback to reduce stress, for example, by displaying encouraging messages or positive words. The membership fee management unit can also use the emotion estimation data to design a payment interface that reduces stress for the user, for example, by adopting a design that allows for simple and intuitive operation. This makes it possible to design an interface that reduces stress when making payments.

[0099] The membership fee management unit can use the emotion estimation function to provide incentives to elicit positive emotions toward payment. For example, the emotion estimation function can be used to provide incentives to elicit positive emotions toward payment. For example, a thank you message or a special offer can be displayed when payment is completed. The membership fee management unit can also monitor the user's emotional state during the payment process in real time and provide incentives to elicit positive emotions. For example, a coupon or points can be awarded after payment is completed. The membership fee management unit can also provide incentives to elicit positive emotions toward payment based on the emotion estimation data. For example, a surprise gift can be provided when payment is completed. In this way, incentives to elicit positive emotions toward payment can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The schedule adjustment unit analyzes the schedules of community members and proposes the optimal date. For example, the generation AI references the calendars of community members and automatically selects a date and time when everyone can attend. The schedule adjustment unit also reserves the venue and secures an appropriate location. For example, the generation AI proposes the optimal date and location based on prompts that include the user's desired date and location conditions. Step 2: The notification unit sends notification to participants based on the date and location determined by the schedule adjustment unit. For example, the generation AI notifies participants of the event details via email or a messaging app. The notification unit also confirms participants' attendance and automatically updates the participant list. For example, the generation AI collects participants' attendance information and updates the list in real time. Step 3: The fee management unit automatically calculates the event fee and manages payments from participants. For example, the generation AI can use QR code payments or electronic money to make it easy for participants to pay the fee. The fee management unit also monitors payment status in real time and sends reminders to participants who have not paid. For example, the generation AI can automatically send reminders when the payment deadline approaches.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A schedule adjustment department analyzes the schedules of community members and proposes optimal dates; a notification unit that sends notifications to participants based on the date and location determined by the schedule adjustment unit; A fee management unit that automatically calculates the event fee and manages payments from participants. A system characterized by:

2. The schedule adjustment unit Based on weather forecast data, we suggest the best dates and locations depending on the weather.

2. The system of claim 1.

3. The guidance notification unit Analyze past attendance data of participants and suggest optimal notification timing to improve attendance rates 2. The system of claim 1.

4. The membership fee management department Analyzing the participant's past payment history and suggesting the most suitable payment method 2. The system of claim 1.

5. The system comprises: Automatically organize photos and videos taken during an event into the perfect album 2. The system of claim 1.

6. The schedule adjustment unit Suggesting less stressful dates and locations based on the user's emotional state 2. The system of claim 1.

7. The guidance notification unit Consider the emotional state of the participant and create an invitation that will elicit a positive response.

2. The system of claim 1.

8. The membership fee management department Design an interface to reduce stress about payments 2. The system of claim 1.

Images