system

Abstract

The system according to the embodiment aims to improve the efficiency of various operations in event management. [Solution] A system according to an embodiment includes a material creation unit, a schedule creation unit, a role assignment unit, and a checklist creation unit. The material creation unit creates materials. The schedule creation unit creates a schedule based on the materials created by the material creation unit. The role assignment unit assigns roles based on the schedule created by the schedule creation unit. The checklist creation unit creates a checklist based on the roles assigned by the role assignment unit.

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] With conventional technology, tasks such as creating materials, schedules, assigning roles, and creating checklists for event management required time and effort, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of various operations in event management. [Means for solving the problem]

[0006] The system according to the embodiment includes a material creation unit, a schedule creation unit, a role assignment unit, and a checklist creation unit. The material creation unit creates materials. The schedule creation unit creates a schedule based on the materials created by the material creation unit. The role assignment unit assigns roles based on the schedule created by the schedule creation unit. The checklist creation unit creates a checklist based on the roles assigned by the role assignment unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of various operations in event management. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 event management efficiency system according to an embodiment of the present invention uses generative AI to improve the efficiency of event management. This system partially automates processes such as document creation, schedule creation, role assignment, and checklist creation, reducing the workload of administrators. The generative AI creates a rough draft of each process, and humans only need to review the draft. Furthermore, in the AI-enabled rehearsal mode, the generative AI prepares for various issues through rehearsals, proposes changes to the event in real time, and, if approved by the user, tracks and incorporates the changes throughout the entire event. For example, the generative AI automatically generates an event schedule and assigns roles to each person in charge. For example, it automatically sets the event start time, the timetable for each session, and break times, and notifies the person in charge. It also automatically generates a checklist listing necessary preparations. This significantly reduces the administrator's workload by requiring only the administrator to review and revise the draft created by the generative AI. In rehearsal mode, the generative AI prepares for various issues through rehearsals. For example, it simulates audio problems and equipment malfunctions and proposes countermeasures. Additionally, any changes that arise during rehearsals are proposed in real time, and if approved by the user, the changes are tracked and reflected in all aspects. This ensures the smooth progress of the event. In this way, using generative AI improves the efficiency of event management and improves troubleshooting capabilities. It also reduces the workload of administrators and increases the success rate of events. This allows the event management efficiency system to automate each step of event management and reduce the workload of administrators. Furthermore, using rehearsal mode improves troubleshooting capabilities and ensures the smooth progress of the event.

[0029] An event management efficiency improvement system according to an embodiment includes a material creation unit, a schedule creation unit, a role assignment unit, and a checklist creation unit. The material creation unit creates materials. Examples of materials include, but are not limited to, presentation materials, reports, and manuals. The material creation unit automatically generates content based on, for example, a template. The generation AI can also reference past event materials to automatically generate optimal content. For example, the generation AI can analyze materials from successful past events and automatically generate similar content. The generation AI can also analyze materials from unsuccessful past events and automatically generate content that reflects improvements. Furthermore, the generation AI can extract trends from past event materials and automatically generate content that reflects the latest trends. The schedule creation unit creates a schedule based on the materials created by the material creation unit. Examples of schedules include, but are not limited to, itineraries, timelines, and Gantt charts. The schedule creation unit proposes an optimal schedule based on past data. The generation AI can also automatically generate an optimal schedule by referencing past event data. For example, the generation AI can analyze schedules from successful past events and automatically generate a similar schedule. The generation AI can also analyze schedules of past unsuccessful events and automatically generate schedules that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate schedules that reflect the latest trends. The role allocation unit assigns roles based on the schedule created by the schedule creation unit. Roles include, but are not limited to, project manager, team leader, and team member, for example. The role allocation unit automatically generates optimal role allocations by referring to past event data. The generation AI can also analyze role allocations of past successful events and automatically generate similar role allocations. The generation AI can also analyze role allocations of past unsuccessful events and automatically generate role allocations that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate role allocations that reflect the latest trends.The checklist creation unit creates a checklist based on the roles assigned by the role assignment unit. Examples of checklists include, but are not limited to, task lists and confirmation item lists. The checklist creation unit automatically generates an optimal checklist by referencing past event data. The generation AI can analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends. As a result, the event management efficiency improvement system according to the embodiment automates each process of document creation, schedule creation, role assignment, and checklist creation, thereby reducing the workload of administrators.

[0030] Furthermore, the event management efficiency system includes a rehearsal unit that prepares for various problems through rehearsals. The rehearsal unit prepares for various problems through rehearsals. Rehearsals include, but are not limited to, simulations and mock exercises. The rehearsal unit simulates problems that may occur during rehearsals and proposes countermeasures. For example, it simulates audio problems and equipment malfunctions and proposes countermeasures. The rehearsal unit also proposes changes to the event in real time, and if the changes are approved by the user, tracks and reflects them in all aspects. This allows the rehearsal unit to ensure the smooth progress of the event. Some or all of the above-described processing in the rehearsal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the rehearsal unit may use a generation AI that simulates problems that may occur during rehearsals and proposes countermeasures. This allows the rehearsal unit to prepare for various problems through rehearsals and ensure the smooth progress of the event.

[0031] The rehearsal department can simulate problems that may occur during rehearsals and propose countermeasures. The rehearsal department can, for example, simulate problems that may occur during rehearsals and propose countermeasures. For example, it can simulate acoustic problems or equipment malfunctions and propose countermeasures. The rehearsal department can also use a generative AI that simulates problems that may occur during rehearsals and proposes countermeasures. This allows the rehearsal department to improve its ability to deal with problems by simulating problems during rehearsals and proposing countermeasures.

[0032] The rehearsal unit can propose changes to an event and, if the user approves, track and reflect the changes in all aspects. The rehearsal unit, for example, proposes changes to an event in real time and, if the user approves, tracks and reflects the changes in all aspects. This allows the rehearsal unit to improve the flexibility of the event. Some or all of the above-described processing in the rehearsal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the rehearsal unit can use a generation AI that proposes changes to an event and, if the user approves, tracks and reflects the changes in all aspects. This allows the rehearsal unit to propose changes to an event in real time and, if the user approves, tracks and reflects the changes in all aspects, improving the flexibility of the event.

[0033] The document creation unit can automatically generate content based on a template. The document creation unit, for example, automatically generates content based on a template. Templates include, for example, standard formats, custom formats, etc., but are not limited to these examples. The document creation unit can automatically generate content based on a template using a generation AI. For example, the generation AI automatically generates content based on a standard format. The generation AI can also automatically generate content based on a custom format. In this way, the document creation unit can improve the efficiency of document creation by automatically generating content based on a template.

[0034] The schedule creation unit can propose a schedule based on past data. The schedule creation unit proposes a schedule based on, for example, past data. Past data includes, for example, past project data, event data, etc., but is not limited to these examples. The schedule creation unit can propose a schedule based on past data using a generation AI. For example, the generation AI can propose a schedule based on past project data. The generation AI can also propose a schedule based on past event data. In this way, the schedule creation unit can propose an optimal schedule based on past data, thereby improving the accuracy of schedule creation.

[0035] The material creation unit can automatically generate content by referencing materials from past events when creating materials. For example, the material creation unit automatically generates content by referencing materials from past events when creating materials. Past event materials include, but are not limited to, past presentation materials and reports. The material creation unit can automatically generate content by referencing materials from past events using a generation AI. For example, the generation AI can analyze materials from past successful events and automatically generate similar content. The generation AI can also analyze materials from past unsuccessful events and automatically generate content that reflects improvements. Furthermore, the generation AI can extract trends from materials from past events and automatically generate content that reflects the latest trends. As a result, the material creation unit can automatically generate optimal content by referencing materials from past events, improving the accuracy of material creation.

[0036] The material creation unit can apply different templates depending on the type of event when creating materials. For example, the material creation unit applies different templates depending on the type of event when creating materials. Types of events include, but are not limited to, conferences, seminars, workshops, etc. The material creation unit can use the generation AI to apply different templates depending on the type of event. For example, the generation AI can apply a template for business events to create professional materials. The generation AI can also apply a template for casual events to create friendly materials. Furthermore, the generation AI can apply a template for academic events to create detailed and specialized materials. This allows the material creation unit to create more appropriate materials by applying different templates depending on the type of event.

[0037] When creating materials, the material creation unit can prioritize inclusion of highly relevant information by taking into account the user's geographical location information. For example, when creating materials, the material creation unit can prioritize inclusion of highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The material creation unit can use a generation AI to prioritize inclusion of highly relevant information by taking into account the user's geographical location information. For example, the generation AI can include local information related to the user's current location in the materials. The generation AI can also reflect region-specific topics in the materials based on the user's geographical location. Furthermore, the generation AI can include optimal examples and data in the materials based on the user's geographical location. This allows the material creation unit to create more appropriate materials by prioritizing inclusion of highly relevant information by taking into account the user's geographical location information.

[0038] The document creation unit can analyze the user's social media activity and reflect related information in the document when creating the document. For example, the document creation unit analyzes the user's social media activity and reflects related information in the document when creating the document. Social media activity includes, but is not limited to, the content of posts and the number of followers. The document creation unit can analyze the user's social media activity using a generation AI and reflect related information in the document. For example, the generation AI can analyze the user's social media posts and reflect related topics in the document. The generation AI can also analyze the interests of the user's followers and include related information in the document. Furthermore, the generation AI can reflect optimal examples and data in the document based on the user's social media activity history. In this way, the document creation unit can create more appropriate documents by analyzing the user's social media activity and reflecting related information in the document.

[0039] The schedule creation unit can automatically generate a schedule by referring to past event data when creating a schedule. For example, the schedule creation unit automatically generates a schedule by referring to past event data when creating a schedule. Past event data includes, but is not limited to, past schedule data, participant data, etc. The schedule creation unit can automatically generate a schedule by using a generation AI to refer to past event data. For example, the generation AI can analyze schedules of past successful events and automatically generate a similar schedule. The generation AI can also analyze schedules of past unsuccessful events and automatically generate a schedule that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a schedule that reflects the latest trends. In this way, the schedule creation unit can automatically generate an optimal schedule by referring to past event data, thereby improving the accuracy of schedule creation.

[0040] The schedule creation unit can apply different schedule algorithms depending on the type of event when creating a schedule. For example, the schedule creation unit applies different schedule algorithms depending on the type of event when creating a schedule. Types of events include, but are not limited to, conferences, seminars, and workshops. The schedule creation unit can use the generation AI to apply different schedule algorithms depending on the type of event. For example, the generation AI can apply a schedule algorithm for business events to create a professional schedule. The generation AI can also apply a schedule algorithm for casual events to create a friendly schedule. Furthermore, the generation AI can apply a schedule algorithm for academic events to create a detailed and professional schedule. This allows the schedule creation unit to create a more appropriate schedule by applying different schedule algorithms depending on the type of event.

[0041] The schedule creation unit can prioritize creating a highly relevant schedule by taking into consideration the user's geographical location information when creating a schedule. For example, the schedule creation unit prioritizes creating a highly relevant schedule by taking into consideration the user's geographical location information when creating a schedule. Geographical location information includes, but is not limited to, GPS data, address information, etc. The schedule creation unit can prioritize creating a highly relevant schedule by using a generation AI by taking into consideration the user's geographical location information. For example, the generation AI can include local events related to the user's current location in the schedule. The generation AI can also reflect region-specific events in the schedule based on the user's geographical location. Furthermore, the generation AI can create an optimal event schedule based on the user's geographical location. As a result, the schedule creation unit can create a more appropriate schedule by taking into consideration the user's geographical location information and prioritizing creating a highly relevant schedule.

[0042] The schedule creation unit can analyze the user's social media activity and reflect the related schedule when creating a schedule. For example, the schedule creation unit analyzes the user's social media activity and reflects the related schedule when creating a schedule. Social media activity includes, but is not limited to, the content of posts and the number of followers. The schedule creation unit can analyze the user's social media activity using a generation AI and reflect the related schedule. For example, the generation AI can analyze the user's social media posts and reflect related events in the schedule. The generation AI can also analyze the interests of the user's followers and include related events in the schedule. Furthermore, the generation AI can create an optimal event schedule based on the user's social media activity history. As a result, the schedule creation unit can create a more appropriate schedule by analyzing the user's social media activity and reflecting the related schedule.

[0043] The role allocation unit can automatically generate role allocation by referring to past event data when allocating roles. For example, the role allocation unit automatically generates role allocation by referring to past event data when allocating roles. Past event data includes, but is not limited to, past role allocation data, participant data, etc. The role allocation unit can automatically generate role allocation by using a generation AI by referring to past event data. For example, the generation AI can analyze role allocations of successful past events and automatically generate similar role allocations. The generation AI can also analyze role allocations of unsuccessful past events and automatically generate role allocations that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate role allocations that reflect the latest trends. As a result, the role allocation unit can automatically generate optimal role allocations by referring to past event data, thereby improving the accuracy of role allocation.

[0044] The role allocation unit can apply different role allocation algorithms depending on the type of event when allocating roles. For example, the role allocation unit applies different role allocation algorithms depending on the type of event when allocating roles. Types of events include, but are not limited to, conferences, seminars, and workshops. The role allocation unit can use the generation AI to apply different role allocation algorithms depending on the type of event. For example, the generation AI can apply a role allocation algorithm for business events to create professional role allocations. The generation AI can also apply a role allocation algorithm for casual events to create friendly role allocations. Furthermore, the generation AI can apply a role allocation algorithm for academic events to create detailed and specialized role allocations. This allows the role allocation unit to create more appropriate role allocations by applying different role allocation algorithms depending on the type of event.

[0045] The role allocation unit can prioritize highly relevant roles in consideration of the user's geographical location information when allocating roles. For example, the role allocation unit prioritizes highly relevant roles in consideration of the user's geographical location information when allocating roles. Highly relevant roles include, but are not limited to, geographical relevance, skill relevance, and the like. The role allocation unit can prioritize highly relevant roles in consideration of the user's geographical location information using a generation AI. For example, the generation AI prioritizes allocating roles related to the user's current location. The generation AI can also prioritize allocating region-specific roles based on the user's geographical location. Furthermore, the generation AI can prioritize allocating optimal roles based on the user's geographical location. In this way, the role allocation unit can create a more appropriate role allocation by prioritizing highly relevant roles in consideration of the user's geographical location information.

[0046] The role assignment unit can analyze the user's social media activity and reflect related roles when assigning roles. For example, the role assignment unit can analyze the user's social media activity and reflect related roles when assigning roles. Related roles include, but are not limited to, associations based on social media activity. The role assignment unit can analyze the user's social media activity using a generation AI and reflect related roles. For example, the generation AI can analyze the user's social media posts and assign related roles. The generation AI can also analyze the interests of the user's followers and assign related roles. Furthermore, the generation AI can assign optimal roles based on the user's social media activity history. In this way, the role assignment unit can create more appropriate role assignments by analyzing the user's social media activity and reflecting related roles.

[0047] The checklist creation unit can automatically generate a checklist by referencing past event data when creating the checklist. For example, the checklist creation unit automatically generates a checklist by referencing past event data when creating the checklist. Past event data includes, but is not limited to, past checklist data, participant data, etc. The checklist creation unit can automatically generate a checklist by using a generation AI to refer to past event data. For example, the generation AI can analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends. As a result, the checklist creation unit can automatically generate optimal checklists by referencing past event data, thereby improving the accuracy of checklist creation.

[0048] The checklist creation unit can apply different checklist algorithms depending on the type of event when creating a checklist. For example, the checklist creation unit applies different checklist algorithms depending on the type of event when creating a checklist. Types of events include, but are not limited to, conferences, seminars, and workshops. The checklist creation unit can use the generation AI to apply different checklist algorithms depending on the type of event. For example, the generation AI can apply a checklist algorithm for business events to create a professional checklist. The generation AI can also apply a checklist algorithm for casual events to create a friendly checklist. Furthermore, the generation AI can apply a checklist algorithm for academic events to create a detailed and specialized checklist. This allows the checklist creation unit to create more appropriate checklists by applying different checklist algorithms depending on the type of event.

[0049] When creating a checklist, the checklist creation unit can prioritize creating a highly relevant checklist by taking into account the user's geographical location information. For example, when creating a checklist, the checklist creation unit prioritizes creating a highly relevant checklist by taking into account the user's geographical location information. Highly relevant checklists include, but are not limited to, geographical relevance and temporal relevance, for example. The checklist creation unit can prioritize creating a highly relevant checklist by using a generation AI. For example, the generation AI can include local information related to the user's current location in the checklist. The generation AI can also reflect region-specific items in the checklist based on the user's geographical location. Furthermore, the generation AI can create an optimal checklist based on the user's geographical location. As a result, the checklist creation unit can create a more appropriate checklist by prioritizing highly relevant checklists by taking into account the user's geographical location information.

[0050] The checklist creation unit can analyze the user's social media activity and reflect relevant checklists when creating a checklist. For example, the checklist creation unit can analyze the user's social media activity and reflect relevant checklists when creating a checklist. Relevant checklists include, but are not limited to, relevance based on social media activity. The checklist creation unit can analyze the user's social media activity using a generation AI and reflect relevant checklists. For example, the generation AI can analyze the user's social media posts and include relevant items in a checklist. The generation AI can also analyze the interests of the user's followers and include relevant items in a checklist. Furthermore, the generation AI can create an optimal checklist based on the user's social media activity history. This allows the checklist creation unit to create a more appropriate checklist by analyzing the user's social media activity and reflecting relevant checklists.

[0051] The rehearsal unit can automatically generate a rehearsal scenario by referring to past event data during a rehearsal. For example, the rehearsal unit automatically generates a rehearsal scenario by referring to past event data during a rehearsal. Past event data includes, but is not limited to, past rehearsal data, participant data, and the like. The rehearsal unit can automatically generate a rehearsal scenario by using a generation AI by referring to past event data. For example, the generation AI can analyze rehearsal scenarios of past successful events and automatically generate a similar rehearsal scenario. The generation AI can also analyze rehearsal scenarios of past unsuccessful events and automatically generate a rehearsal scenario that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a rehearsal scenario that reflects the latest trends. In this way, the rehearsal unit can automatically generate an optimal rehearsal scenario by referring to past event data, thereby improving the accuracy of the rehearsal.

[0052] The rehearsal unit can apply different rehearsal algorithms depending on the type of event during rehearsal. For example, the rehearsal unit applies different rehearsal algorithms depending on the type of event during rehearsal. Types of events include, but are not limited to, conferences, seminars, and workshops. The rehearsal unit can use the generation AI to apply different rehearsal algorithms depending on the type of event. For example, the generation AI can apply a rehearsal algorithm for a business event to create a professional rehearsal scenario. The generation AI can also apply a rehearsal algorithm for a casual event to create a friendly rehearsal scenario. Furthermore, the generation AI can apply a rehearsal algorithm for an academic event to create a detailed and professional rehearsal scenario. This allows the rehearsal unit to perform more appropriate rehearsals by applying different rehearsal algorithms depending on the type of event.

[0053] The rehearsal unit can prioritize highly relevant rehearsals during rehearsals, taking into account the user's geographical location information. For example, the rehearsal unit can prioritize highly relevant rehearsals during rehearsals, taking into account the user's geographical location information. Highly relevant rehearsals include, but are not limited to, geographical relevance and temporal relevance, for example. The rehearsal unit can prioritize highly relevant rehearsals using the generation AI, taking into account the user's geographical location information. For example, the generation AI can include local information related to the user's current location in the rehearsal. The generation AI can also reflect region-specific scenarios in the rehearsal based on the user's geographical location. Furthermore, the generation AI can create an optimal rehearsal scenario based on the user's geographical location. This allows the rehearsal unit to prioritize highly relevant rehearsals by taking into account the user's geographical location information, thereby enabling more appropriate rehearsals.

[0054] The rehearsal unit can analyze the user's social media activity and reflect a relevant rehearsal scenario during a rehearsal. For example, the rehearsal unit can analyze the user's social media activity and reflect a relevant rehearsal scenario during a rehearsal. The relevant rehearsal scenario includes, but is not limited to, relevance based on social media activity. The rehearsal unit can analyze the user's social media activity using a generation AI and reflect a relevant rehearsal scenario. For example, the generation AI can analyze the user's social media posts and include a relevant scenario in the rehearsal. The generation AI can also analyze the interests of the user's followers and include a relevant scenario in the rehearsal. Furthermore, the generation AI can create an optimal rehearsal scenario based on the user's social media activity history. This allows the rehearsal unit to perform a more appropriate rehearsal by analyzing the user's social media activity and reflecting a relevant rehearsal scenario.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The event management efficiency system can further include a budget management unit. The budget management unit automatically manages the event budget and proposes optimal budget allocation. For example, the budget management unit can optimize budget allocation based on past event data and reduce wasteful spending. The budget management unit can also monitor budget usage in real time and warn of the risk of budget overruns. Furthermore, the budget management unit can propose a budget plan for the next event based on budget usage. This allows event organizers to improve budget management efficiency and reduce costs.

[0057] The event management efficiency system can further include a security management unit. The security management unit automatically manages event security and proposes optimal security measures. For example, the security management unit can analyze security risks based on past event data and propose optimal security measures. The security management unit can also monitor security situations in real time and issue warnings if an abnormality is detected. Furthermore, the security management unit can improve security plans for the next event based on security data. This allows event organizers to improve security management efficiency and ensure the safety of participants.

[0058] The event management efficiency improvement system can further include a traffic management unit. The traffic management unit automatically manages traffic to the event venue and suggests the optimal transportation method. For example, the traffic management unit can analyze traffic conditions based on past event data and suggest the optimal transportation method. The traffic management unit can also monitor traffic conditions in real time and suggest alternative routes in the event of congestion or delays. Furthermore, the traffic management unit can improve the traffic plan for the next event based on the traffic data. This allows event organizers to manage traffic more efficiently and ensure smooth movement of participants.

[0059] The event management efficiency improvement system can further include a data analysis unit. The data analysis unit automatically collects and analyzes various data about the event. For example, the data analysis unit can collect participant behavior data, feedback data, security data, etc., and analyze the factors that contributed to the success of the event. The data analysis unit can also suggest improvements for the next event based on the collected data. Furthermore, the data analysis unit can analyze data in real time, immediately identify problems during the event, and propose countermeasures. This allows event managers to make data-based decisions and improve the quality of their events.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The material creation department creates materials. Materials include presentation materials, reports, manuals, etc. The material creation department automatically generates content based on templates. The generation AI can reference past event materials and automatically generate optimal content. For example, the generation AI can analyze materials from past successful events and automatically generate similar content. The generation AI can also analyze materials from past unsuccessful events and automatically generate content that reflects the areas for improvement. Furthermore, the generation AI can extract trends from past event materials and automatically generate content that reflects the latest trends. Step 2: The schedule creation unit creates a schedule based on the materials created by the material creation unit. Schedules include timetables, timelines, Gantt charts, etc. The schedule creation unit proposes an optimal schedule based on past data. The generation AI can also automatically generate an optimal schedule by referencing past event data. For example, the generation AI can analyze schedules of successful past events and automatically generate a similar schedule. The generation AI can also analyze schedules of unsuccessful past events and automatically generate a schedule that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a schedule that reflects the latest trends. Step 3: The Role Allocation Department assigns roles based on the schedule created by the Schedule Creation Department. Roles include project manager, team leader, and members. The Role Allocation Department references past event data to automatically generate the optimal role allocation. The Generation AI can also analyze the role allocation of past successful events and automatically generate a similar role allocation. The Generation AI can also analyze the role allocation of past unsuccessful events and automatically generate a role allocation that reflects improvements. Furthermore, the Generation AI can extract trends from past event data and automatically generate a role allocation that reflects the latest trends. Step 4: The checklist creation unit creates a checklist based on the roles assigned by the role allocation unit. Checklists include task lists and confirmation item lists. The checklist creation unit references past event data to automatically generate the optimal checklist. The generation AI can also analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends.

[0062] (Example 2) The event management efficiency system according to an embodiment of the present invention uses generative AI to improve the efficiency of event management. This system partially automates processes such as document creation, schedule creation, role assignment, and checklist creation, reducing the workload of administrators. The generative AI creates a rough draft of each process, and humans only need to review the draft. Furthermore, in the AI-enabled rehearsal mode, the generative AI prepares for various issues through rehearsals, proposes changes to the event in real time, and, if approved by the user, tracks and incorporates the changes throughout the entire event. For example, the generative AI automatically generates an event schedule and assigns roles to each person in charge. For example, it automatically sets the event start time, the timetable for each session, and break times, and notifies the person in charge. It also automatically generates a checklist listing necessary preparations. This significantly reduces the administrator's workload by requiring only the administrator to review and revise the draft created by the generative AI. In rehearsal mode, the generative AI prepares for various issues through rehearsals. For example, it simulates audio problems and equipment malfunctions and proposes countermeasures. Additionally, any changes that arise during rehearsals are proposed in real time, and if approved by the user, the changes are tracked and reflected in all aspects. This ensures the smooth progress of the event. In this way, using generative AI improves the efficiency of event management and improves troubleshooting capabilities. It also reduces the workload of administrators and increases the success rate of events. This allows the event management efficiency system to automate each step of event management and reduce the workload of administrators. Furthermore, using rehearsal mode improves troubleshooting capabilities and ensures the smooth progress of the event.

[0063] An event management efficiency improvement system according to an embodiment includes a material creation unit, a schedule creation unit, a role assignment unit, and a checklist creation unit. The material creation unit creates materials. Examples of materials include, but are not limited to, presentation materials, reports, and manuals. The material creation unit automatically generates content based on, for example, a template. The generation AI can also reference past event materials to automatically generate optimal content. For example, the generation AI can analyze materials from successful past events and automatically generate similar content. The generation AI can also analyze materials from unsuccessful past events and automatically generate content that reflects improvements. Furthermore, the generation AI can extract trends from past event materials and automatically generate content that reflects the latest trends. The schedule creation unit creates a schedule based on the materials created by the material creation unit. Examples of schedules include, but are not limited to, itineraries, timelines, and Gantt charts. The schedule creation unit proposes an optimal schedule based on past data. The generation AI can also automatically generate an optimal schedule by referencing past event data. For example, the generation AI can analyze schedules from successful past events and automatically generate a similar schedule. The generation AI can also analyze schedules of past unsuccessful events and automatically generate schedules that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate schedules that reflect the latest trends. The role allocation unit assigns roles based on the schedule created by the schedule creation unit. Roles include, but are not limited to, project manager, team leader, and team member, for example. The role allocation unit automatically generates optimal role allocations by referring to past event data. The generation AI can also analyze role allocations of past successful events and automatically generate similar role allocations. The generation AI can also analyze role allocations of past unsuccessful events and automatically generate role allocations that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate role allocations that reflect the latest trends.The checklist creation unit creates a checklist based on the roles assigned by the role assignment unit. Examples of checklists include, but are not limited to, task lists and confirmation item lists. The checklist creation unit automatically generates an optimal checklist by referencing past event data. The generation AI can analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends. As a result, the event management efficiency improvement system according to the embodiment automates each process of document creation, schedule creation, role assignment, and checklist creation, thereby reducing the workload of administrators.

[0064] Furthermore, the event management efficiency system includes a rehearsal unit that prepares for various problems through rehearsals. The rehearsal unit prepares for various problems through rehearsals. Rehearsals include, but are not limited to, simulations and mock exercises. The rehearsal unit simulates problems that may occur during rehearsals and proposes countermeasures. For example, it simulates audio problems and equipment malfunctions and proposes countermeasures. The rehearsal unit also proposes changes to the event in real time, and if the changes are approved by the user, tracks and reflects them in all aspects. This allows the rehearsal unit to ensure the smooth progress of the event. Some or all of the above-described processing in the rehearsal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the rehearsal unit may use a generation AI that simulates problems that may occur during rehearsals and proposes countermeasures. This allows the rehearsal unit to prepare for various problems through rehearsals and ensure the smooth progress of the event.

[0065] The rehearsal department can simulate problems that may occur during rehearsals and propose countermeasures. The rehearsal department can, for example, simulate problems that may occur during rehearsals and propose countermeasures. For example, it can simulate acoustic problems or equipment malfunctions and propose countermeasures. The rehearsal department can also use a generative AI that simulates problems that may occur during rehearsals and proposes countermeasures. This allows the rehearsal department to improve its ability to deal with problems by simulating problems during rehearsals and proposing countermeasures.

[0066] The rehearsal unit can propose changes to an event and, if the user approves, track and reflect the changes in all aspects. The rehearsal unit, for example, proposes changes to an event in real time and, if the user approves, tracks and reflects the changes in all aspects. This allows the rehearsal unit to improve the flexibility of the event. Some or all of the above-described processing in the rehearsal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the rehearsal unit can use a generation AI that proposes changes to an event and, if the user approves, tracks and reflects the changes in all aspects. This allows the rehearsal unit to propose changes to an event in real time and, if the user approves, tracks and reflects the changes in all aspects, improving the flexibility of the event.

[0067] The document creation unit can automatically generate content based on a template. The document creation unit, for example, automatically generates content based on a template. Templates include, for example, standard formats, custom formats, etc., but are not limited to these examples. The document creation unit can automatically generate content based on a template using a generation AI. For example, the generation AI automatically generates content based on a standard format. The generation AI can also automatically generate content based on a custom format. In this way, the document creation unit can improve the efficiency of document creation by automatically generating content based on a template.

[0068] The schedule creation unit can propose a schedule based on past data. The schedule creation unit proposes a schedule based on, for example, past data. Past data includes, for example, past project data, event data, etc., but is not limited to these examples. The schedule creation unit can propose a schedule based on past data using a generation AI. For example, the generation AI can propose a schedule based on past project data. The generation AI can also propose a schedule based on past event data. In this way, the schedule creation unit can propose an optimal schedule based on past data, thereby improving the accuracy of schedule creation.

[0069] The material creation unit can estimate the user's emotions and adjust the presentation of the materials based on the estimated user emotions. The material creation unit, for example, estimates the user's emotions and adjusts the presentation of the materials based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The material creation unit can estimate the user's emotions using a generation AI and adjust the presentation of the materials based on the estimated user emotions. For example, if the user is nervous, the generation AI can create simple, highly visible materials. Alternatively, if the user is relaxed, the generation AI can create materials containing detailed information. Furthermore, if the user is excited, the generation AI can create materials incorporating a visually stimulating design. This allows the material creation unit to create more appropriate materials by adjusting the presentation of the materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The material creation unit can automatically generate content by referencing materials from past events when creating materials. For example, the material creation unit automatically generates content by referencing materials from past events when creating materials. Past event materials include, but are not limited to, past presentation materials and reports. The material creation unit can automatically generate content by referencing materials from past events using a generation AI. For example, the generation AI can analyze materials from past successful events and automatically generate similar content. The generation AI can also analyze materials from past unsuccessful events and automatically generate content that reflects improvements. Furthermore, the generation AI can extract trends from materials from past events and automatically generate content that reflects the latest trends. As a result, the material creation unit can automatically generate optimal content by referencing materials from past events, improving the accuracy of material creation.

[0071] The material creation unit can apply different templates depending on the type of event when creating materials. For example, the material creation unit applies different templates depending on the type of event when creating materials. Types of events include, but are not limited to, conferences, seminars, workshops, etc. The material creation unit can use the generation AI to apply different templates depending on the type of event. For example, the generation AI can apply a template for business events to create professional materials. The generation AI can also apply a template for casual events to create friendly materials. Furthermore, the generation AI can apply a template for academic events to create detailed and specialized materials. This allows the material creation unit to create more appropriate materials by applying different templates depending on the type of event.

[0072] The material creation unit can estimate the user's emotions and adjust the length of the materials based on the estimated user emotions. The material creation unit, for example, estimates the user's emotions and adjusts the length of the materials based on the estimated user emotions. The length of the materials includes, but is not limited to, the number of pages and the number of characters. The material creation unit can estimate the user's emotions using a generation AI and adjust the length of the materials based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can create short, concise materials. Alternatively, if the user is relaxed, the generation AI can create longer materials with detailed explanations. Furthermore, if the user is excited, the generation AI can create materials with visually stimulating effects. This allows the material creation unit to create more appropriate materials by adjusting the length of the materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When creating materials, the material creation unit can prioritize inclusion of highly relevant information by taking into account the user's geographical location information. For example, when creating materials, the material creation unit can prioritize inclusion of highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. The material creation unit can use a generation AI to prioritize inclusion of highly relevant information by taking into account the user's geographical location information. For example, the generation AI can include local information related to the user's current location in the materials. The generation AI can also reflect region-specific topics in the materials based on the user's geographical location. Furthermore, the generation AI can include optimal examples and data in the materials based on the user's geographical location. This allows the material creation unit to create more appropriate materials by prioritizing inclusion of highly relevant information by taking into account the user's geographical location information.

[0074] The document creation unit can analyze the user's social media activity and reflect related information in the document when creating the document. For example, the document creation unit analyzes the user's social media activity and reflects related information in the document when creating the document. Social media activity includes, but is not limited to, the content of posts and the number of followers. The document creation unit can analyze the user's social media activity using a generation AI and reflect related information in the document. For example, the generation AI can analyze the user's social media posts and reflect related topics in the document. The generation AI can also analyze the interests of the user's followers and include related information in the document. Furthermore, the generation AI can reflect optimal examples and data in the document based on the user's social media activity history. In this way, the document creation unit can create more appropriate documents by analyzing the user's social media activity and reflecting related information in the document.

[0075] The schedule creation unit can estimate the user's emotions and adjust the presentation of the schedule based on the estimated user emotions. The schedule creation unit, for example, estimates the user's emotions and adjusts the presentation of the schedule based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The schedule creation unit can estimate the user's emotions using a generation AI and adjust the presentation of the schedule based on the estimated user emotions. For example, if the user is nervous, the generation AI can create a simple, highly visible schedule. Alternatively, if the user is relaxed, the generation AI can create a schedule that includes detailed information. Furthermore, if the user is excited, the generation AI can create a schedule that incorporates a visually stimulating design. This allows the schedule creation unit to create a more appropriate schedule by adjusting the presentation of the schedule based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The schedule creation unit can automatically generate a schedule by referring to past event data when creating a schedule. For example, the schedule creation unit automatically generates a schedule by referring to past event data when creating a schedule. Past event data includes, but is not limited to, past schedule data, participant data, etc. The schedule creation unit can automatically generate a schedule by using a generation AI to refer to past event data. For example, the generation AI can analyze schedules of past successful events and automatically generate a similar schedule. The generation AI can also analyze schedules of past unsuccessful events and automatically generate a schedule that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a schedule that reflects the latest trends. In this way, the schedule creation unit can automatically generate an optimal schedule by referring to past event data, thereby improving the accuracy of schedule creation.

[0077] The schedule creation unit can apply different schedule algorithms depending on the type of event when creating a schedule. For example, the schedule creation unit applies different schedule algorithms depending on the type of event when creating a schedule. Types of events include, but are not limited to, conferences, seminars, and workshops. The schedule creation unit can use the generation AI to apply different schedule algorithms depending on the type of event. For example, the generation AI can apply a schedule algorithm for business events to create a professional schedule. The generation AI can also apply a schedule algorithm for casual events to create a friendly schedule. Furthermore, the generation AI can apply a schedule algorithm for academic events to create a detailed and professional schedule. This allows the schedule creation unit to create a more appropriate schedule by applying different schedule algorithms depending on the type of event.

[0078] The schedule creation unit can estimate the user's emotions and adjust the length of the schedule based on the estimated user emotions. The schedule creation unit, for example, estimates the user's emotions and adjusts the length of the schedule based on the estimated user emotions. The schedule length can be measured in units of hours or days, but is not limited to these examples. The schedule creation unit can estimate the user's emotions using a generation AI and adjust the length of the schedule based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can create a short, concise schedule. Alternatively, if the user is relaxed, the generation AI can create a longer schedule with detailed explanations. Furthermore, if the user is excited, the generation AI can create a schedule with visually stimulating effects. This allows the schedule creation unit to create a more appropriate schedule by adjusting the length of the schedule based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] The schedule creation unit can prioritize creating a highly relevant schedule by taking into consideration the user's geographical location information when creating a schedule. For example, the schedule creation unit prioritizes creating a highly relevant schedule by taking into consideration the user's geographical location information when creating a schedule. Geographical location information includes, but is not limited to, GPS data, address information, etc. The schedule creation unit can prioritize creating a highly relevant schedule by using a generation AI by taking into consideration the user's geographical location information. For example, the generation AI can include local events related to the user's current location in the schedule. The generation AI can also reflect region-specific events in the schedule based on the user's geographical location. Furthermore, the generation AI can create an optimal event schedule based on the user's geographical location. As a result, the schedule creation unit can create a more appropriate schedule by taking into consideration the user's geographical location information and prioritizing creating a highly relevant schedule.

[0080] The schedule creation unit can analyze the user's social media activity and reflect the related schedule when creating a schedule. For example, the schedule creation unit analyzes the user's social media activity and reflects the related schedule when creating a schedule. Social media activity includes, but is not limited to, the content of posts and the number of followers. The schedule creation unit can analyze the user's social media activity using a generation AI and reflect the related schedule. For example, the generation AI can analyze the user's social media posts and reflect related events in the schedule. The generation AI can also analyze the interests of the user's followers and include related events in the schedule. Furthermore, the generation AI can create an optimal event schedule based on the user's social media activity history. As a result, the schedule creation unit can create a more appropriate schedule by analyzing the user's social media activity and reflecting the related schedule.

[0081] The role assignment unit can estimate the user's emotions and adjust the way in which roles are expressed based on the estimated user emotions. The role assignment unit, for example, estimates the user's emotions and adjusts the way in which roles are expressed based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The role assignment unit can estimate the user's emotions using a generation AI and adjust the way in which roles are expressed based on the estimated user emotions. For example, if the user is nervous, the generation AI can create a simple and highly visible role assignment. Alternatively, if the user is relaxed, the generation AI can create a role assignment that includes detailed information. Furthermore, if the user is excited, the generation AI can create a role assignment that incorporates a visually stimulating design. This allows the role assignment unit to create a more appropriate role assignment by adjusting the way in which roles are expressed based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] The role allocation unit can automatically generate role allocation by referring to past event data when allocating roles. For example, the role allocation unit automatically generates role allocation by referring to past event data when allocating roles. Past event data includes, but is not limited to, past role allocation data, participant data, etc. The role allocation unit can automatically generate role allocation by using a generation AI by referring to past event data. For example, the generation AI can analyze role allocations of successful past events and automatically generate similar role allocations. The generation AI can also analyze role allocations of unsuccessful past events and automatically generate role allocations that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate role allocations that reflect the latest trends. As a result, the role allocation unit can automatically generate optimal role allocations by referring to past event data, thereby improving the accuracy of role allocation.

[0083] The role allocation unit can apply different role allocation algorithms depending on the type of event when allocating roles. For example, the role allocation unit applies different role allocation algorithms depending on the type of event when allocating roles. Types of events include, but are not limited to, conferences, seminars, and workshops. The role allocation unit can use the generation AI to apply different role allocation algorithms depending on the type of event. For example, the generation AI can apply a role allocation algorithm for business events to create professional role allocations. The generation AI can also apply a role allocation algorithm for casual events to create friendly role allocations. Furthermore, the generation AI can apply a role allocation algorithm for academic events to create detailed and specialized role allocations. This allows the role allocation unit to create more appropriate role allocations by applying different role allocation algorithms depending on the type of event.

[0084] The role assignment unit can estimate the user's emotions and determine the priority of role assignment based on the estimated user emotions. The role assignment unit can, for example, estimate the user's emotions and determine the priority of role assignment based on the estimated user emotions. Role assignment priorities include, but are not limited to, importance and urgency. The role assignment unit can estimate the user's emotions using a generation AI and determine the priority of role assignment based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize important roles. Also, if the user is relaxed, the generation AI can prioritize detailed roles. Furthermore, if the user is excited, the generation AI can prioritize visually stimulating roles. In this way, the role assignment unit can create more appropriate role assignments by determining the priority of role assignment based on the user's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] The role allocation unit can prioritize highly relevant roles in consideration of the user's geographical location information when allocating roles. For example, the role allocation unit prioritizes highly relevant roles in consideration of the user's geographical location information when allocating roles. Highly relevant roles include, but are not limited to, geographical relevance, skill relevance, and the like. The role allocation unit can prioritize highly relevant roles in consideration of the user's geographical location information using a generation AI. For example, the generation AI prioritizes allocating roles related to the user's current location. The generation AI can also prioritize allocating region-specific roles based on the user's geographical location. Furthermore, the generation AI can prioritize allocating optimal roles based on the user's geographical location. In this way, the role allocation unit can create a more appropriate role allocation by prioritizing highly relevant roles in consideration of the user's geographical location information.

[0086] The role assignment unit can analyze the user's social media activity and reflect related roles when assigning roles. For example, the role assignment unit can analyze the user's social media activity and reflect related roles when assigning roles. Related roles include, but are not limited to, associations based on social media activity. The role assignment unit can analyze the user's social media activity using a generation AI and reflect related roles. For example, the generation AI can analyze the user's social media posts and assign related roles. The generation AI can also analyze the interests of the user's followers and assign related roles. Furthermore, the generation AI can assign optimal roles based on the user's social media activity history. In this way, the role assignment unit can create more appropriate role assignments by analyzing the user's social media activity and reflecting related roles.

[0087] The checklist creation unit can estimate the user's emotions and adjust the checklist presentation style based on the estimated user emotions. The checklist creation unit, for example, estimates the user's emotions and adjusts the checklist presentation style based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. The checklist creation unit can estimate the user's emotions using a generation AI and adjust the checklist presentation style based on the estimated user emotions. For example, if the user is nervous, the generation AI can create a simple, highly visible checklist. Alternatively, if the user is relaxed, the generation AI can create a checklist with detailed information. Furthermore, if the user is excited, the generation AI can create a checklist with a visually stimulating design. This allows the checklist creation unit to create a more appropriate checklist by adjusting the checklist presentation style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The checklist creation unit can automatically generate a checklist by referencing past event data when creating the checklist. For example, the checklist creation unit automatically generates a checklist by referencing past event data when creating the checklist. Past event data includes, but is not limited to, past checklist data, participant data, etc. The checklist creation unit can automatically generate a checklist by using a generation AI to refer to past event data. For example, the generation AI can analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends. As a result, the checklist creation unit can automatically generate optimal checklists by referencing past event data, thereby improving the accuracy of checklist creation.

[0089] The checklist creation unit can apply different checklist algorithms depending on the type of event when creating a checklist. For example, the checklist creation unit applies different checklist algorithms depending on the type of event when creating a checklist. Types of events include, but are not limited to, conferences, seminars, and workshops. The checklist creation unit can use the generation AI to apply different checklist algorithms depending on the type of event. For example, the generation AI can apply a checklist algorithm for business events to create a professional checklist. The generation AI can also apply a checklist algorithm for casual events to create a friendly checklist. Furthermore, the generation AI can apply a checklist algorithm for academic events to create a detailed and specialized checklist. This allows the checklist creation unit to create more appropriate checklists by applying different checklist algorithms depending on the type of event.

[0090] The checklist creation unit can estimate the user's emotions and determine the priority of the checklist based on the estimated user emotions. The checklist creation unit can, for example, estimate the user's emotions and determine the priority of the checklist based on the estimated user emotions. Checklist priorities include, but are not limited to, importance and urgency. The checklist creation unit can estimate the user's emotions using a generation AI and determine the priority of the checklist based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize important items in the checklist. Also, if the user is relaxed, the generation AI can prioritize detailed items in the checklist. Furthermore, if the user is excited, the generation AI can prioritize visually stimulating items in the checklist. In this way, the checklist creation unit can create a more appropriate checklist by determining the priority of the checklist based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When creating a checklist, the checklist creation unit can prioritize creating a highly relevant checklist by taking into account the user's geographical location information. For example, when creating a checklist, the checklist creation unit prioritizes creating a highly relevant checklist by taking into account the user's geographical location information. Highly relevant checklists include, but are not limited to, geographical relevance and temporal relevance, for example. The checklist creation unit can prioritize creating a highly relevant checklist by using a generation AI. For example, the generation AI can include local information related to the user's current location in the checklist. The generation AI can also reflect region-specific items in the checklist based on the user's geographical location. Furthermore, the generation AI can create an optimal checklist based on the user's geographical location. As a result, the checklist creation unit can create a more appropriate checklist by prioritizing highly relevant checklists by taking into account the user's geographical location information.

[0092] The checklist creation unit can analyze the user's social media activity and reflect relevant checklists when creating a checklist. For example, the checklist creation unit can analyze the user's social media activity and reflect relevant checklists when creating a checklist. Relevant checklists include, but are not limited to, relevance based on social media activity. The checklist creation unit can analyze the user's social media activity using a generation AI and reflect relevant checklists. For example, the generation AI can analyze the user's social media posts and include relevant items in a checklist. The generation AI can also analyze the interests of the user's followers and include relevant items in a checklist. Furthermore, the generation AI can create an optimal checklist based on the user's social media activity history. This allows the checklist creation unit to create a more appropriate checklist by analyzing the user's social media activity and reflecting relevant checklists.

[0093] The rehearsal unit can estimate the user's emotions and adjust the rehearsal expression method based on the estimated user emotions. The rehearsal unit, for example, estimates the user's emotions and adjusts the rehearsal expression method based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The rehearsal unit can estimate the user's emotions using a generation AI and adjust the rehearsal expression method based on the estimated user emotions. For example, if the user is nervous, the generation AI can create a simple, highly visible rehearsal scenario. Alternatively, if the user is relaxed, the generation AI can create a rehearsal scenario that includes detailed information. Furthermore, if the user is excited, the generation AI can create a rehearsal scenario that incorporates a visually stimulating design. This allows the rehearsal unit to adjust the rehearsal expression method based on the user's emotions, thereby enabling a more appropriate rehearsal. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The rehearsal unit can automatically generate a rehearsal scenario by referring to past event data during a rehearsal. For example, the rehearsal unit automatically generates a rehearsal scenario by referring to past event data during a rehearsal. Past event data includes, but is not limited to, past rehearsal data, participant data, and the like. The rehearsal unit can automatically generate a rehearsal scenario by using a generation AI by referring to past event data. For example, the generation AI can analyze rehearsal scenarios of past successful events and automatically generate a similar rehearsal scenario. The generation AI can also analyze rehearsal scenarios of past unsuccessful events and automatically generate a rehearsal scenario that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a rehearsal scenario that reflects the latest trends. In this way, the rehearsal unit can automatically generate an optimal rehearsal scenario by referring to past event data, thereby improving the accuracy of the rehearsal.

[0095] The rehearsal unit can apply different rehearsal algorithms depending on the type of event during rehearsal. For example, the rehearsal unit applies different rehearsal algorithms depending on the type of event during rehearsal. Types of events include, but are not limited to, conferences, seminars, and workshops. The rehearsal unit can use the generation AI to apply different rehearsal algorithms depending on the type of event. For example, the generation AI can apply a rehearsal algorithm for a business event to create a professional rehearsal scenario. The generation AI can also apply a rehearsal algorithm for a casual event to create a friendly rehearsal scenario. Furthermore, the generation AI can apply a rehearsal algorithm for an academic event to create a detailed and professional rehearsal scenario. This allows the rehearsal unit to perform more appropriate rehearsals by applying different rehearsal algorithms depending on the type of event.

[0096] The rehearsal unit can estimate the user's emotions and determine the priority of rehearsals based on the estimated user emotions. The rehearsal unit, for example, estimates the user's emotions and determines the priority of rehearsals based on the estimated user emotions. Examples of rehearsal priorities include, but are not limited to, importance and urgency. The rehearsal unit can estimate the user's emotions using a generation AI and determine the priority of rehearsals based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize rehearsing important scenarios. Also, if the user is relaxed, the generation AI can prioritize rehearsing detailed scenarios. Furthermore, if the user is excited, the generation AI can prioritize rehearsing visually stimulating scenarios. This allows the rehearsal unit to prioritize rehearsals based on the user's emotions, thereby enabling more appropriate rehearsals. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The rehearsal unit can prioritize highly relevant rehearsals during rehearsals, taking into account the user's geographical location information. For example, the rehearsal unit can prioritize highly relevant rehearsals during rehearsals, taking into account the user's geographical location information. Highly relevant rehearsals include, but are not limited to, geographical relevance and temporal relevance, for example. The rehearsal unit can prioritize highly relevant rehearsals using the generation AI, taking into account the user's geographical location information. For example, the generation AI can include local information related to the user's current location in the rehearsal. The generation AI can also reflect region-specific scenarios in the rehearsal based on the user's geographical location. Furthermore, the generation AI can create an optimal rehearsal scenario based on the user's geographical location. This allows the rehearsal unit to prioritize highly relevant rehearsals by taking into account the user's geographical location information, thereby enabling more appropriate rehearsals.

[0098] The rehearsal unit can analyze the user's social media activity and reflect a relevant rehearsal scenario during a rehearsal. For example, the rehearsal unit can analyze the user's social media activity and reflect a relevant rehearsal scenario during a rehearsal. The relevant rehearsal scenario includes, but is not limited to, relevance based on social media activity. The rehearsal unit can analyze the user's social media activity using a generation AI and reflect a relevant rehearsal scenario. For example, the generation AI can analyze the user's social media posts and include a relevant scenario in the rehearsal. The generation AI can also analyze the interests of the user's followers and include a relevant scenario in the rehearsal. Furthermore, the generation AI can create an optimal rehearsal scenario based on the user's social media activity history. This allows the rehearsal unit to perform a more appropriate rehearsal by analyzing the user's social media activity and reflecting a relevant rehearsal scenario. === Hard Collateral 1-1 === Each of the multiple elements, including the material creation unit, schedule creation unit, role assignment unit, checklist creation unit, and rehearsal unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the material creation unit is realized by the control unit 46A of the smart device 14, and a generation AI automatically generates optimal content by referring to past event materials. The schedule creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal schedule based on past event data. The role assignment unit is realized, for example, by the control unit 46A of the smart device 14, and a generation AI automatically generates optimal role assignments by referring to past event data. The checklist creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates an optimal checklist based on past event data. The rehearsal unit is realized, for example, by the control unit 46A of the smart device 14, and simulates problems that may occur during rehearsals and proposes countermeasures. === Hard Collateral 1-2 === Each of the multiple elements, including the material creation unit, schedule creation unit, role assignment unit, checklist creation unit, and rehearsal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the material creation unit is realized by the control unit 46A of the smart glasses 214, and a generation AI automatically generates optimal content by referring to past event materials. The schedule creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal schedule based on past event data. The role assignment unit is realized, for example, by the control unit 46A of the smart glasses 214, and a generation AI automatically generates optimal role assignments by referring to past event data. The checklist creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates an optimal checklist based on past event data. The rehearsal unit is realized, for example, by the control unit 46A of the smart glasses 214, and simulates problems that may occur during a rehearsal and proposes countermeasures. === Hard Collateral 1-3 === Each of the multiple elements, including the material creation unit, schedule creation unit, role assignment unit, checklist creation unit, and rehearsal unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the material creation unit is realized by the control unit 46A of the headset-type terminal 314, and a generation AI automatically generates optimal content by referencing past event materials. The schedule creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal schedule based on past event data. The role assignment unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and a generation AI automatically generates optimal role assignments by referencing past event data. The checklist creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates an optimal checklist based on past event data. The rehearsal unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and simulates problems that may occur during rehearsals and proposes countermeasures. === Hard Collateral 1-4 === Each of the multiple elements, including the material creation unit, schedule creation unit, role assignment unit, checklist creation unit, and rehearsal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the material creation unit is realized by the control unit 46A of the robot 414, and a generation AI automatically generates optimal content by referring to past event materials. The schedule creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal schedule based on past event data. The role assignment unit is realized, for example, by the control unit 46A of the robot 414, and a generation AI automatically generates optimal role assignments by referring to past event data. The checklist creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates an optimal checklist based on past event data. The rehearsal unit is realized, for example, by the control unit 46A of the robot 414, and simulates problems that may occur during rehearsals and proposes countermeasures.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The event management efficiency improvement system may further include a feedback collection unit. The feedback collection unit automatically collects and analyzes feedback from participants after the event ends. For example, the feedback collection unit may automatically generate and send a questionnaire in which participants evaluate each session of the event. The feedback collection unit may also suggest improvements for the next event based on the collected feedback. Furthermore, the feedback collection unit may estimate the emotions of participants and classify the content of the feedback based on the emotions. This allows the event organizer to obtain detailed feedback based on the emotions of participants and improve the quality of the next event.

[0101] The event management efficiency system can further include a budget management unit. The budget management unit automatically manages the event budget and proposes optimal budget allocation. For example, the budget management unit can optimize budget allocation based on past event data and reduce wasteful spending. The budget management unit can also monitor budget usage in real time and warn of the risk of budget overruns. Furthermore, the budget management unit can propose a budget plan for the next event based on budget usage. This allows event organizers to improve budget management efficiency and reduce costs.

[0102] The event management efficiency improvement system can further include a participant management unit. The participant management unit automatically manages event participant information and creates an optimal participant list. For example, the participant management unit can analyze participant interests based on past event data and propose an optimal participant list. The participant management unit can also estimate participant emotions and group participants based on their emotions. Furthermore, the participant management unit can improve the participant list for the next event based on participant feedback. This allows event organizers to improve participant management efficiency and create more effective events.

[0103] The event management efficiency improvement system can further include a marketing support unit. The marketing support unit automatically supports marketing activities for the event and proposes optimal marketing strategies. For example, the marketing support unit can optimize marketing campaigns based on past event data and implement effective promotions. The marketing support unit can also estimate the emotions of participants and adjust marketing messages based on their emotions. Furthermore, the marketing support unit can monitor the effectiveness of marketing activities in real time and modify strategies as necessary. This allows event organizers to streamline their marketing activities and attract the attention of participants.

[0104] The event management efficiency improvement system may further include an environmental monitoring unit. The environmental monitoring unit monitors the environment of the event venue in real time and maintains optimal environmental conditions. For example, the environmental monitoring unit may collect environmental data such as temperature, humidity, and lighting, and suggest optimal environmental conditions. The environmental monitoring unit may also estimate the emotions of participants and adjust the environmental conditions based on the emotions. Furthermore, the environmental monitoring unit may improve the environmental conditions for the next event based on the environmental data. This allows the event organizer to improve the comfort of participants and increase the success rate of the event.

[0105] The event management efficiency system can further include a security management unit. The security management unit automatically manages event security and proposes optimal security measures. For example, the security management unit can analyze security risks based on past event data and propose optimal security measures. The security management unit can also monitor security situations in real time and issue warnings if an abnormality is detected. Furthermore, the security management unit can improve security plans for the next event based on security data. This allows event organizers to improve security management efficiency and ensure the safety of participants.

[0106] The event management efficiency improvement system can further include a traffic management unit. The traffic management unit automatically manages traffic to the event venue and suggests the optimal transportation method. For example, the traffic management unit can analyze traffic conditions based on past event data and suggest the optimal transportation method. The traffic management unit can also monitor traffic conditions in real time and suggest alternative routes in the event of congestion or delays. Furthermore, the traffic management unit can improve the traffic plan for the next event based on the traffic data. This allows event organizers to manage traffic more efficiently and ensure smooth movement of participants.

[0107] The event management efficiency system can further include a health management unit. The health management unit monitors the health status of event participants in real time and proposes optimal health management. For example, the health management unit can collect participants' health data and issue an alert if an abnormality is detected. The health management unit can also estimate participants' emotions and provide health management advice based on the emotions. Furthermore, the health management unit can improve the health management plan for the next event based on the health data. This allows event organizers to ensure the health of participants and increase the success rate of the event.

[0108] The event management efficiency improvement system can further include an entertainment management unit. The entertainment management unit automatically manages entertainment elements during the event and proposes an optimal entertainment plan. For example, the entertainment management unit can optimize the entertainment plan based on past event data to attract participants' interest. The entertainment management unit can also estimate participants' emotions and adjust the entertainment content based on the emotions. Furthermore, the entertainment management unit can improve the entertainment plan for the next event based on the entertainment data. This allows event organizers to improve entertainment management efficiency and increase participant satisfaction.

[0109] The event management efficiency improvement system can further include a data analysis unit. The data analysis unit automatically collects and analyzes various data about the event. For example, the data analysis unit can collect participant behavior data, feedback data, security data, etc., and analyze the factors that contributed to the success of the event. The data analysis unit can also suggest improvements for the next event based on the collected data. Furthermore, the data analysis unit can analyze data in real time, immediately identify problems during the event, and propose countermeasures. This allows event managers to make data-based decisions and improve the quality of their events.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The material creation department creates materials. Materials include presentation materials, reports, manuals, etc. The material creation department automatically generates content based on templates. The generation AI can reference past event materials and automatically generate optimal content. For example, the generation AI can analyze materials from past successful events and automatically generate similar content. The generation AI can also analyze materials from past unsuccessful events and automatically generate content that reflects the areas for improvement. Furthermore, the generation AI can extract trends from past event materials and automatically generate content that reflects the latest trends. Step 2: The schedule creation unit creates a schedule based on the materials created by the material creation unit. Schedules include timetables, timelines, Gantt charts, etc. The schedule creation unit proposes an optimal schedule based on past data. The generation AI can also automatically generate an optimal schedule by referencing past event data. For example, the generation AI can analyze schedules of successful past events and automatically generate a similar schedule. The generation AI can also analyze schedules of unsuccessful past events and automatically generate a schedule that reflects improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate a schedule that reflects the latest trends. Step 3: The Role Allocation Department assigns roles based on the schedule created by the Schedule Creation Department. Roles include project manager, team leader, and members. The Role Allocation Department references past event data to automatically generate the optimal role allocation. The Generation AI can also analyze the role allocation of past successful events and automatically generate a similar role allocation. The Generation AI can also analyze the role allocation of past unsuccessful events and automatically generate a role allocation that reflects improvements. Furthermore, the Generation AI can extract trends from past event data and automatically generate a role allocation that reflects the latest trends. Step 4: The checklist creation unit creates a checklist based on the roles assigned by the role allocation unit. Checklists include task lists and confirmation item lists. The checklist creation unit references past event data to automatically generate the optimal checklist. The generation AI can also analyze checklists from past successful events and automatically generate similar checklists. The generation AI can also analyze checklists from past unsuccessful events and automatically generate checklists that reflect improvements. Furthermore, the generation AI can extract trends from past event data and automatically generate checklists that reflect the latest trends.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 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 material creation department that creates materials; a schedule creation unit that creates a schedule based on the materials created by the material creation unit; a role allocation unit that allocates roles based on the schedule created by the schedule creation unit; a checklist creation unit that creates a checklist based on the roles assigned by the role allocation unit; Equipped with A system characterized by:

2. We have a rehearsal department that prepares for various problems through rehearsals.

2. The system of claim 1.

3. The rehearsal section Simulating problems that may occur during rehearsals and proposing countermeasures 3. The system of claim 2.

4. The rehearsal section Propose changes to the event, and if users agree, track and implement those changes across all aspects.

3. The system of claim 2.

5. The material creation unit Auto-generate content based on templates 2. The system of claim 1.

6. The schedule creation unit Propose schedules based on past data 2. The system of claim 1.

7. The material creation unit Estimate the user's emotions and adjust the presentation of materials based on the estimated user emotions 2. The system of claim 1.

8. The material creation unit When creating materials, automatically generate content by referencing past event materials.

2. The system of claim 1.

9. The material creation unit Apply different templates to different types of events when creating materials 2. The system of claim 1.

Images