System

Abstract

An object of a system according to an embodiment is to efficiently perform arrangement, troubleshooting, and risk management in planning and operation of an event.SOLUTION: A system according to an embodiment includes an event plan generation unit, a setup list generation unit, a troubleshooting method detection unit, a change reflection unit, and a risk suggestion unit. The event proposal generation unit generates an event proposal by inputting what the user wants to do and an image of the event. The preparation list generation unit creates a preparation list based on the event proposal generated by the event proposal generation unit. The trouble handling method searching unit searches for an assumed trouble and a handling method thereof based on the event proposal generated by the event proposal generation unit. A change reflection part automatically reflects the change in all the products when inputting the change generated during the preparation of the event. The risk suggestion unit identifies a risk from various viewpoints and suggests an effective solution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, planning and running an event involves complicated procedures for planning, dealing with problems, and managing risks, making it difficult to carry out these tasks efficiently.

[0005] The system according to the embodiment aims to efficiently carry out arrangements, troubleshoot, and manage risks in the planning and management of events. [Means for solving the problem]

[0006] The system according to the embodiment includes an event plan creation unit, a setup list creation unit, a troubleshooting solution identification unit, a change reflection unit, and a risk proposal unit. The event plan creation unit creates an event plan by inputting what the user wants to do and an image of the event. The setup list creation unit creates a setup list based on the event plan created by the event plan creation unit. The troubleshooting solution identification unit identifies anticipated problems and solutions based on the event plan created by the event plan creation unit. The change reflection unit automatically reflects changes made during event preparations in all created materials when the changes are input. The risk proposal unit identifies risks from multiple perspectives and proposes effective solutions. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out arrangements, troubleshoot, and manage risks when planning and running an event. [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 support system according to an embodiment of the present invention is a system that automatically creates an event plan and a schedule leading up to the day of the event by inputting what the user wants to do and an image of the event, and also identifies anticipated problems and how to deal with them. This allows the event management support system to efficiently and effectively solve problems in event management.

[0029] An event management support system according to an embodiment includes an event plan generation unit, a schedule generation unit, a troubleshooting unit, a change reflection unit, and a risk proposal unit. The event plan generation unit generates an event plan by inputting what a user wants to do and an image of the event. For example, if a user inputs "I want to hold a music festival," the generation AI generates a detailed plan including the stage layout, performer schedule, and necessary equipment. The schedule generation unit creates a schedule list based on the event plan generated by the event plan generation unit. For example, the generation AI lists tasks to be performed during the preparation period, their order, and the person in charge. The troubleshooting unit identifies anticipated problems and their solutions based on the event plan generated by the event plan generation unit. For example, the generation AI considers various risks, such as the impact of weather changes, equipment failure, and ensuring the safety of participants, and proposes solutions for each. The change reflection unit automatically reflects changes made during event preparation in all created documents. For example, if there are changes in performers or schedules, the generation AI automatically updates the plan, schedule, and risk countermeasures simply by inputting that information. The risk proposal unit identifies risks from multiple perspectives and proposes effective solutions. For example, the generation AI considers the scale and location of the event, the characteristics of the participants, and other factors to propose optimal risk management measures. This allows the event management support system according to the embodiment to efficiently and effectively solve problems in event management. For example, when planning a music festival, the system automatically creates the stage layout and performer schedule and lists tasks for the preparation phase. It also identifies risks such as weather changes and equipment failure in advance and proposes countermeasures. Furthermore, all created documents are automatically updated simply by inputting changes that occur along the way, allowing for flexible response.

[0030] The event plan generation unit can analyze the user's past event history and generate a plan that incorporates successful elements. For example, the event plan generation unit uses a generation AI to analyze the user's past event history and extract successful elements. For example, the stage layout and program structure that received high praise from past events can be incorporated into a new plan. The event plan generation unit also generates a plan that combines successful elements based on the user's past event history. For example, popular activities and performances from past events can be reflected in the new plan. The event plan generation unit also uses a generation AI to analyze past event data and identify success factors. For example, successful elements can be incorporated into the new plan based on participant feedback and survey results. In this way, more effective event plans can be generated by incorporating past successful elements.

[0031] The event plan generation unit can analyze market trends in real time and propose plan proposals that incorporate the latest trends. For example, the event plan generation unit uses a generation AI to analyze market trends in real time and propose plan proposals that incorporate the latest trends. For example, currently popular music genres and artists can be incorporated into the event. The event plan generation unit also analyzes market trends and generates plan proposals that reflect the latest trends. For example, an event plan that incorporates the latest technology and interactive elements can be proposed. The event plan generation unit also analyzes market trends in real time and generates plan proposals that incorporate trends. For example, themes and concepts that are trending on social media can be reflected in the event. This makes it possible to propose plan proposals that reflect the latest market trends.

[0032] The setup list generation unit can analyze setup lists from past events and propose the optimal task order. For example, the generation AI in the setup list generation unit analyzes setup lists from past events and proposes the optimal task order. For example, it efficiently arranges tasks that should be done during the preparation period. The setup list generation unit also analyzes setup lists from past events and proposes the optimal task order. For example, it prioritizes important tasks to create an efficient schedule. The generation AI in the setup list generation unit analyzes past event data and proposes the optimal task order. For example, it proposes an effective task order based on past success stories. This makes it possible to analyze past setup lists and propose the optimal task order.

[0033] The setup list generation unit can predict the required time for each task and create an efficient schedule. In the setup list generation unit, for example, a generation AI predicts the required time for each task and creates an efficient schedule. For example, it predicts the required time for tasks that should be performed during the preparation period and reflects this in the schedule. The setup list generation unit also predicts the required time for each task and creates an efficient schedule. For example, it creates an efficient schedule based on the required time for each task. In the setup list generation unit, a generation AI predicts the required time for each task and creates an efficient schedule. For example, it predicts the required time for a task based on past data and reflects this in the schedule. This makes it possible to predict the required time for each task and create an efficient schedule.

[0034] The trouble-shooting method identification unit can analyze past event data and learn about frequently occurring troubles and how to deal with them. In the trouble-shooting method identification unit, for example, the generation AI analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, troubles that occurred in past events and how to deal with them are registered in a database. The trouble-shooting method identification unit also analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, it proposes effective ways to deal with them based on past trouble cases. In the trouble-shooting method identification unit, the generation AI analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, it proposes preventive measures based on past trouble data. In this way, it is possible to analyze past event data and learn about frequently occurring troubles and how to deal with them.

[0035] The troubleshooting method identification unit can acquire weather data in real time and propose measures in response to weather changes. For example, the generation AI in the troubleshooting method identification unit acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest moving indoors or setting up a tent when it rains. The troubleshooting method identification unit also acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest safety measures in case of strong winds or how to secure equipment. The troubleshooting method identification unit also acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest what clothing to wear or how to prepare drinks in response to changes in temperature. This makes it possible to acquire weather data in real time and propose measures in response to weather changes.

[0036] The change reflection unit can analyze the impact of changes and optimize the overall schedule and resource allocation. In the change reflection unit, for example, the generation AI analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically adjusts the schedule when there is a change in performers. The change reflection unit also analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically adjusts resource allocation when there is a change in equipment. The change reflection unit also analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically reallocates the schedule and resources when there is a change in venue. This makes it possible to analyze the impact of changes and optimize the overall schedule and resource allocation.

[0037] The change reflection unit can identify new risks based on the changes and propose countermeasures. In the change reflection unit, for example, the generation AI can identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in performers and propose countermeasures. The change reflection unit can also identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in equipment and propose countermeasures. The change reflection unit can also identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in venue and propose countermeasures. In this way, new risks can be identified based on the changes and proposed countermeasures.

[0038] The risk proposal unit can analyze risk management data from different industries and apply it to event management. For example, the generation AI in the risk proposal unit analyzes risk management data from different industries and applies it to event management. For example, it proposes safety measures based on risk management data from the aviation industry. The risk proposal unit also analyzes risk management data from different industries and applies it to event management. For example, it proposes budget management measures based on risk management data from the financial industry. The risk proposal unit also analyzes risk management data from different industries and applies it to event management. For example, it proposes health management measures based on risk management data from the medical industry. This allows risk management data from different industries to be analyzed and applied to event management.

[0039] The risk proposal unit can obtain market data in real time and propose solutions that take economic risks into consideration. For example, the generation AI obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it proposes cost-cutting measures to reduce the risk of going over budget. The risk proposal unit also obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it proposes sponsorship and advertising strategies to improve profitability. The risk proposal unit also obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it analyzes ticket sales trends and proposes pricing and promotion strategies. This makes it possible to obtain market data in real time and propose solutions that take economic risks into consideration.

[0040] The risk proposal unit can incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, the generating AI can incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can incorporate risk management methods that have been successful at overseas events. The risk proposal unit can also incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can apply risk management methods used in intercultural exchange events. The risk proposal unit can also incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can refer to risk management methods used in international events. This allows it to incorporate risk management methods from different regions and cultures and propose solutions from an international perspective.

[0041] The risk proposal unit can perform simulations, verify various risk scenarios, and propose optimal solutions. For example, the generation AI can perform simulations in the risk proposal unit, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for natural disasters and emergency situations. The risk proposal unit can also perform simulations, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for sudden illness or injury of a participant. The generation AI can also perform simulations in the risk proposal unit, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for traffic congestion and access issues. This makes it possible to simulate various risk scenarios and propose optimal solutions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The event management support system can also be equipped with a feedback collection unit that collects participant feedback in real time and suggests improvements as the event progresses. For example, participants can provide feedback in real time via their smartphones, and the data can be analyzed to suggest improvements as the event progresses. The feedback collection unit can also monitor participant satisfaction in real time and adjust the content of the event as necessary. Furthermore, the feedback collection unit can compile participant feedback after the event ends and reflect it in planning the next event. This allows participants' opinions to be reflected in real time, making it possible to manage events with a higher level of satisfaction.

[0044] The event management support system can also include an environmental assessment unit that evaluates the environmental impact of the event and proposes sustainable management methods. For example, it evaluates the resource and energy consumption of the event and proposes environmentally friendly alternatives. The environmental assessment unit can also propose measures to reduce waste and promote recycling. Furthermore, the environmental assessment unit can calculate the carbon footprint of the event and propose carbon offset methods. This enables environmentally friendly and sustainable event management.

[0045] The event management support system can also be equipped with a health management unit that monitors the health status of participants and provides medical support as needed. For example, participants provide health data via a smartwatch, and the data is analyzed to notify medical staff if any abnormalities are detected. The health management unit can also provide health advice and support during the event. Furthermore, the health management unit can compile participants' health data after the event and reflect it in planning the next event. This makes it possible to run events while protecting the health of participants.

[0046] The event management support system can also be equipped with a networking support unit to support networking among participants. For example, it can match participants based on their profiles and interests and provide networking opportunities. The networking support unit can also suggest spaces and activities where participants can interact with each other during the event. Furthermore, the networking support unit can help participants exchange contact information after the event and support the building of long-term relationships. This can promote interaction between participants and increase the value of the event.

[0047] The event management support system can also be equipped with a marketing support section to support event marketing. For example, it can propose strategies for effectively announcing and promoting the event. The marketing support section can also support promotional activities using social media and advertising media. Furthermore, the marketing support section can analyze the effectiveness of promotional activities after the event and reflect the results in planning the next event. This can increase the event's ability to attract customers through effective marketing activities.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The event plan generator generates an event plan based on the user's input of what they want to do and the image of the event. For example, if a user inputs that they want to hold a music festival, the generation AI generates a detailed plan including the stage layout, performer schedule, necessary equipment, etc. Step 2: The setup list generator creates a setup list based on the event plan generated by the event plan generator. For example, the generation AI creates a list of tasks to be performed during the preparation period, the order in which they should be performed, and the people in charge. Step 3: The troubleshooting section identifies potential problems and how to deal with them based on the event plan generated by the event plan generation section. For example, the AI ​​generator considers various risks, such as the impact of weather changes, equipment failure, and ensuring the safety of participants, and proposes solutions for each. Step 4: The Change Reflection Unit automatically reflects any changes that occur during event preparation. For example, if there are changes to the performers or schedule, the generation AI simply inputs that information and the plan, schedule, and risk countermeasures are automatically updated. Step 5: The Risk Proposal Department identifies risks from multiple perspectives and proposes effective solutions. For example, the Generative AI takes into account the scale and location of the event, the characteristics of the participants, and other factors to propose optimal risk management measures.

[0050] (Example 2) The event management support system according to an embodiment of the present invention is a system that automatically creates an event plan and a schedule leading up to the day of the event by inputting what the user wants to do and an image of the event, and also identifies anticipated problems and how to deal with them. This allows the event management support system to efficiently and effectively solve problems in event management.

[0051] An event management support system according to an embodiment includes an event plan generation unit, a schedule generation unit, a troubleshooting unit, a change reflection unit, and a risk proposal unit. The event plan generation unit generates an event plan by inputting what a user wants to do and an image of the event. For example, if a user inputs "I want to hold a music festival," the generation AI generates a detailed plan including the stage layout, performer schedule, and necessary equipment. The schedule generation unit creates a schedule list based on the event plan generated by the event plan generation unit. For example, the generation AI lists tasks to be performed during the preparation period, their order, and the person in charge. The troubleshooting unit identifies anticipated problems and their solutions based on the event plan generated by the event plan generation unit. For example, the generation AI considers various risks, such as the impact of weather changes, equipment failure, and ensuring the safety of participants, and proposes solutions for each. The change reflection unit automatically reflects changes made during event preparation in all created documents. For example, if there are changes in performers or schedules, the generation AI automatically updates the plan, schedule, and risk countermeasures simply by inputting that information. The risk proposal unit identifies risks from multiple perspectives and proposes effective solutions. For example, the generation AI considers the scale and location of the event, the characteristics of the participants, and other factors to propose optimal risk management measures. This allows the event management support system according to the embodiment to efficiently and effectively solve problems in event management. For example, when planning a music festival, the system automatically creates the stage layout and performer schedule and lists tasks for the preparation phase. It also identifies risks such as weather changes and equipment failure in advance and proposes countermeasures. Furthermore, all created documents are automatically updated simply by inputting changes that occur along the way, allowing for flexible response.

[0052] The event plan generation unit can analyze the user's past event history and generate a plan that incorporates successful elements. For example, the event plan generation unit uses a generation AI to analyze the user's past event history and extract successful elements. For example, the stage layout and program structure that received high praise from past events can be incorporated into a new plan. The event plan generation unit also generates a plan that combines successful elements based on the user's past event history. For example, popular activities and performances from past events can be reflected in the new plan. The event plan generation unit also uses a generation AI to analyze past event data and identify success factors. For example, successful elements can be incorporated into the new plan based on participant feedback and survey results. In this way, more effective event plans can be generated by incorporating past successful elements.

[0053] The event plan generation unit can analyze market trends in real time and propose plan proposals that incorporate the latest trends. For example, the event plan generation unit uses a generation AI to analyze market trends in real time and propose plan proposals that incorporate the latest trends. For example, currently popular music genres and artists can be incorporated into the event. The event plan generation unit also analyzes market trends and generates plan proposals that reflect the latest trends. For example, an event plan that incorporates the latest technology and interactive elements can be proposed. The event plan generation unit also analyzes market trends in real time and generates plan proposals that incorporate trends. For example, themes and concepts that are trending on social media can be reflected in the event. This makes it possible to propose plan proposals that reflect the latest market trends.

[0054] The event plan generation unit can use the emotion estimation function to analyze the emotional state of the user and generate a plan that elicits positive emotions. The event plan generation unit, for example, uses the emotion estimation function to analyze the emotional state of the user and generate a plan that elicits positive emotions. For example, it proposes activities that allow the user to relax. The event plan generation unit also analyzes the emotional state of the user and generates a plan that elicits positive emotions. For example, it incorporates entertainment elements that the user can enjoy. The event plan generation unit also uses the emotion estimation function to analyze the emotional state of the user and generate a plan that elicits positive emotions. For example, it proposes productions and programs that will move the user. In this way, a plan that elicits positive emotions can be generated based on the emotional state of the user.

[0055] The setup list generation unit can analyze setup lists from past events and propose the optimal task order. For example, the generation AI in the setup list generation unit analyzes setup lists from past events and proposes the optimal task order. For example, it efficiently arranges tasks that should be done during the preparation period. The setup list generation unit also analyzes setup lists from past events and proposes the optimal task order. For example, it prioritizes important tasks to create an efficient schedule. The generation AI in the setup list generation unit analyzes past event data and proposes the optimal task order. For example, it proposes an effective task order based on past success stories. This makes it possible to analyze past setup lists and propose the optimal task order.

[0056] The setup list generation unit can predict the required time for each task and create an efficient schedule. In the setup list generation unit, for example, a generation AI predicts the required time for each task and creates an efficient schedule. For example, it predicts the required time for tasks that should be performed during the preparation period and reflects this in the schedule. The setup list generation unit also predicts the required time for each task and creates an efficient schedule. For example, it creates an efficient schedule based on the required time for each task. In the setup list generation unit, a generation AI predicts the required time for each task and creates an efficient schedule. For example, it predicts the required time for a task based on past data and reflects this in the schedule. This makes it possible to predict the required time for each task and create an efficient schedule.

[0057] The setup list generation unit can use the emotion estimation function to analyze the user's stress level and create a setup list that reduces the user's burden. The setup list generation unit, for example, uses the emotion estimation function to analyze the user's stress level and create a setup list that reduces the user's burden. For example, it distributes high-stress tasks. The setup list generation unit also analyzes the user's stress level and creates a setup list that reduces the user's burden. For example, it inserts relaxing tasks in between tasks. The setup list generation unit also uses the emotion estimation function to analyze the user's stress level and create a setup list that reduces the user's burden. For example, it prioritizes low-stress tasks. In this way, it is possible to analyze the user's stress level and create a setup list that reduces the user's burden.

[0058] The trouble-shooting method identification unit can analyze past event data and learn about frequently occurring troubles and how to deal with them. In the trouble-shooting method identification unit, for example, the generation AI analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, troubles that occurred in past events and how to deal with them are registered in a database. The trouble-shooting method identification unit also analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, it proposes effective ways to deal with them based on past trouble cases. In the trouble-shooting method identification unit, the generation AI analyzes past event data and learns about frequently occurring troubles and how to deal with them. For example, it proposes preventive measures based on past trouble data. In this way, it is possible to analyze past event data and learn about frequently occurring troubles and how to deal with them.

[0059] The troubleshooting method identification unit can acquire weather data in real time and propose measures in response to weather changes. For example, the generation AI in the troubleshooting method identification unit acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest moving indoors or setting up a tent when it rains. The troubleshooting method identification unit also acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest safety measures in case of strong winds or how to secure equipment. The troubleshooting method identification unit also acquires weather data in real time and proposes measures in response to weather changes. For example, it may suggest what clothing to wear or how to prepare drinks in response to changes in temperature. This makes it possible to acquire weather data in real time and propose measures in response to weather changes.

[0060] The trouble-handling method identification unit can use the emotion estimation function to predict participants' anxiety and propose ways to deal with it. The trouble-handling method identification unit can, for example, use the emotion estimation function to predict participants' anxiety and propose ways to deal with it. For example, it can set up a relaxation area to reduce anxiety. The trouble-handling method identification unit can also predict participants' anxiety and propose ways to deal with it. For example, it can provide guidance and support that gives participants who are prone to anxiety a sense of security. The trouble-handling method identification unit can also use the emotion estimation function to predict participants' anxiety and propose ways to deal with it. For example, it can provide information or create guidelines to reduce anxiety. This makes it possible to predict participants' anxiety and propose ways to deal with it.

[0061] The change reflection unit can analyze the impact of changes and optimize the overall schedule and resource allocation. In the change reflection unit, for example, the generation AI analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically adjusts the schedule when there is a change in performers. The change reflection unit also analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically adjusts resource allocation when there is a change in equipment. The change reflection unit also analyzes the impact of changes and optimizes the overall schedule and resource allocation. For example, it automatically reallocates the schedule and resources when there is a change in venue. This makes it possible to analyze the impact of changes and optimize the overall schedule and resource allocation.

[0062] The change reflection unit can identify new risks based on the changes and propose countermeasures. In the change reflection unit, for example, the generation AI can identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in performers and propose countermeasures. The change reflection unit can also identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in equipment and propose countermeasures. The change reflection unit can also identify new risks based on the changes and propose countermeasures. For example, it can identify new risks associated with a change in venue and propose countermeasures. In this way, new risks can be identified based on the changes and proposed countermeasures.

[0063] The change reflection unit can use the emotion estimation function to make suggestions to reduce the stress that the changes cause to the user. The change reflection unit, for example, uses the emotion estimation function to make suggestions to reduce the stress that the changes cause to the user. For example, it provides detailed explanations and support for the changes. The change reflection unit also makes suggestions to reduce the stress that the changes cause to the user. For example, it suggests relaxation methods to reduce anxiety associated with the changes. The change reflection unit also uses the emotion estimation function to make suggestions to reduce the stress that the changes cause to the user. For example, it emphasizes the positive aspects of the changes. This makes it possible to make suggestions to reduce the stress that the changes cause to the user.

[0064] The risk proposal unit can analyze risk management data from different industries and apply it to event management. For example, the generation AI in the risk proposal unit analyzes risk management data from different industries and applies it to event management. For example, it proposes safety measures based on risk management data from the aviation industry. The risk proposal unit also analyzes risk management data from different industries and applies it to event management. For example, it proposes budget management measures based on risk management data from the financial industry. The risk proposal unit also analyzes risk management data from different industries and applies it to event management. For example, it proposes health management measures based on risk management data from the medical industry. This allows risk management data from different industries to be analyzed and applied to event management.

[0065] The risk proposal unit can obtain market data in real time and propose solutions that take economic risks into consideration. For example, the generation AI obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it proposes cost-cutting measures to reduce the risk of going over budget. The risk proposal unit also obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it proposes sponsorship and advertising strategies to improve profitability. The risk proposal unit also obtains market data in real time and proposes solutions that take economic risks into consideration. For example, it analyzes ticket sales trends and proposes pricing and promotion strategies. This makes it possible to obtain market data in real time and propose solutions that take economic risks into consideration.

[0066] The risk proposal unit can use the emotion estimation function to predict the emotions of participants and propose solutions to reduce emotional risks. For example, the risk proposal unit can use the emotion estimation function to predict the emotions of participants and propose solutions to reduce emotional risks. For example, the risk proposal unit can set up a relaxation area to reduce participants' anxiety. The risk proposal unit can also predict the emotions of participants and propose solutions to reduce emotional risks. For example, the risk proposal unit can provide entertainment or activities to reduce participants' stress. The risk proposal unit can also predict the emotions of participants and propose solutions to reduce emotional risks. For example, the risk proposal unit can provide support or guidance according to the emotional state of the participants. This makes it possible to predict the emotions of participants and propose solutions to reduce emotional risks.

[0067] The risk proposal unit can incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, the generating AI can incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can incorporate risk management methods that have been successful at overseas events. The risk proposal unit can also incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can apply risk management methods used in intercultural exchange events. The risk proposal unit can also incorporate risk management methods from different regions and cultures and propose solutions from an international perspective. For example, it can refer to risk management methods used in international events. This allows it to incorporate risk management methods from different regions and cultures and propose solutions from an international perspective.

[0068] The risk proposal unit can perform simulations, verify various risk scenarios, and propose optimal solutions. For example, the generation AI can perform simulations in the risk proposal unit, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for natural disasters and emergency situations. The risk proposal unit can also perform simulations, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for sudden illness or injury of a participant. The generation AI can also perform simulations in the risk proposal unit, verify various risk scenarios, and propose optimal solutions. For example, it can simulate countermeasures for traffic congestion and access issues. This makes it possible to simulate various risk scenarios and propose optimal solutions.

[0069] The risk suggestion unit can use the emotion estimation function to predict the user's emotion when a risk occurs and propose a quick response. The risk suggestion unit, for example, uses the emotion estimation function to predict the user's emotion when a risk occurs and proposes a quick response. For example, if the user feels anxious, it proposes a response that gives a sense of security. The risk suggestion unit also predicts the user's emotion when a risk occurs and proposes a quick response. For example, if the user feels anger, it proposes a way to respond calmly. The risk suggestion unit also uses the emotion estimation function to predict the user's emotion when a risk occurs and proposes a quick response. For example, it proposes a way to provide clear instructions if the user feels confused. In this way, it is possible to predict the user's emotion when a risk occurs and propose a quick response.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The event management support system can also be equipped with a feedback collection unit that collects participant feedback in real time and suggests improvements as the event progresses. For example, participants can provide feedback in real time via their smartphones, and the data can be analyzed to suggest improvements as the event progresses. The feedback collection unit can also monitor participant satisfaction in real time and adjust the content of the event as necessary. Furthermore, the feedback collection unit can compile participant feedback after the event ends and reflect it in planning the next event. This allows participants' opinions to be reflected in real time, making it possible to manage events with a higher level of satisfaction.

[0072] The event management support system can also include an environmental assessment unit that evaluates the environmental impact of the event and proposes sustainable management methods. For example, it evaluates the resource and energy consumption of the event and proposes environmentally friendly alternatives. The environmental assessment unit can also propose measures to reduce waste and promote recycling. Furthermore, the environmental assessment unit can calculate the carbon footprint of the event and propose carbon offset methods. This enables environmentally friendly and sustainable event management.

[0073] The event management support system can also be equipped with a health management unit that monitors the health status of participants and provides medical support as needed. For example, participants provide health data via a smartwatch, and the data is analyzed to notify medical staff if any abnormalities are detected. The health management unit can also provide health advice and support during the event. Furthermore, the health management unit can compile participants' health data after the event and reflect it in planning the next event. This makes it possible to run events while protecting the health of participants.

[0074] The event management support system can also be equipped with a networking support unit to support networking among participants. For example, it can match participants based on their profiles and interests and provide networking opportunities. The networking support unit can also suggest spaces and activities where participants can interact with each other during the event. Furthermore, the networking support unit can help participants exchange contact information after the event and support the building of long-term relationships. This can promote interaction between participants and increase the value of the event.

[0075] The event management support system can also be equipped with a marketing support section to support event marketing. For example, it can propose strategies for effectively announcing and promoting the event. The marketing support section can also support promotional activities using social media and advertising media. Furthermore, the marketing support section can analyze the effectiveness of promotional activities after the event and reflect the results in planning the next event. This can increase the event's ability to attract customers through effective marketing activities.

[0076] The event management support system can also use its emotion estimation function to analyze participants' emotions and adjust entertainment elements in real time while the event is underway. For example, it can analyze whether participants are enjoying themselves and change the program content as needed. It can also use the emotion estimation function to analyze participants' emotions and suggest performances that will elicit positive emotions. It can also use the emotion estimation function to analyze participants' emotions, compile feedback after the event, and reflect it in planning the next event. This makes it possible to manage events that provide a higher level of satisfaction based on participants' emotions.

[0077] The event management support system can also use the emotion estimation function to analyze the user's emotions and suggest a relaxation area to reduce stress. For example, if the user is feeling stressed, it can suggest a space or activity where they can relax. The emotion estimation function can also be used to analyze the user's emotions and suggest music or scents to reduce stress. Furthermore, the emotion estimation function can be used to analyze the user's emotions, evaluate the effect of stress reduction after the event, and reflect this in planning the next event. This reduces user stress and enables more comfortable event management.

[0078] The event management support system can also use the emotion estimation function to analyze participants' emotions and provide real-time support while the event is underway. For example, if a participant is feeling anxious, it can provide support that gives them a sense of security. It can also use the emotion estimation function to analyze participants' emotions and suggest activities to elicit positive emotions. It can also use the emotion estimation function to analyze participants' emotions, compile feedback after the event, and reflect this in planning the next event. This makes it possible to manage events that provide a higher level of satisfaction based on participants' emotions.

[0079] The event management support system can also use the emotion estimation function to analyze the user's emotions and propose ways to deal with problems in real time while the event is in progress. For example, if the user is feeling anxious, it can propose ways to reassure them. It can also use the emotion estimation function to analyze the user's emotions and propose ways to elicit positive emotions. It can also use the emotion estimation function to analyze the user's emotions, compile feedback after the event, and reflect this in planning the next event. This makes it possible to propose more effective ways to deal with problems based on the user's emotions.

[0080] The event management support system can also use the emotion estimation function to analyze participants' emotions and propose risk management measures in real time while the event is underway. For example, if participants are feeling anxious, it can propose risk management measures that will give them a sense of security. It can also use the emotion estimation function to analyze participants' emotions and propose risk management measures that will elicit positive emotions. It can also use the emotion estimation function to analyze participants' emotions, compile feedback after the event, and reflect this in planning the next event. This makes it possible to propose more effective risk management measures based on participants' emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The event plan generator generates an event plan based on the user's input of what they want to do and the image of the event. For example, if a user inputs that they want to hold a music festival, the generation AI generates a detailed plan including the stage layout, performer schedule, necessary equipment, etc. Step 2: The setup list generator creates a setup list based on the event plan generated by the event plan generator. For example, the generation AI creates a list of tasks to be performed during the preparation period, the order in which they should be performed, and the people in charge. Step 3: The troubleshooting section identifies potential problems and how to deal with them based on the event plan generated by the event plan generation section. For example, the AI ​​generator considers various risks, such as the impact of weather changes, equipment failure, and ensuring the safety of participants, and proposes solutions for each. Step 4: The Change Reflection Unit automatically reflects any changes that occur during event preparation. For example, if there are changes to the performers or schedule, the generation AI simply inputs that information and the plan, schedule, and risk countermeasures are automatically updated. Step 5: The Risk Proposal Department identifies risks from multiple perspectives and proposes effective solutions. For example, the Generative AI takes into account the scale and location of the event, the characteristics of the participants, and other factors to propose optimal risk management measures.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[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 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.

[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 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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. an event plan creation unit that creates an event plan by inputting what the user wants to do or an image of the event; a setup list generation unit that generates a setup list based on the event plan generated by the event plan generation unit; a trouble-solving method identification unit that identifies anticipated troubles and their solutions based on the event plan generated by the event plan generation unit; a change reflecting unit that, when changes made during preparation for an event are input, automatically reflects the changes in all created items; A risk proposal department that identifies risks from multiple perspectives and proposes effective solutions. A system characterized by:

2. The event plan creation unit Analyzing the user's past event history and generating a plan incorporating successful elements 2. The system of claim 1.

3. The event plan creation unit Analyze market trends in real time and propose plans that incorporate the latest trends 2. The system of claim 1.

4. The event plan creation unit Analyzing the emotional state of the user and generating a plan that elicits positive emotions 2. The system of claim 1.

5. The setup list generation unit Analyze past schedules for the event and suggest optimal task ordering 2. The system of claim 1.

6. The setup list generation unit Estimate the duration of each of the above tasks and create an efficient schedule 2. The system of claim 1.

7. The setup list generation unit Analyzing the stress level of the user and creating the schedule to reduce the burden 2. The system of claim 1.

8. The trouble-shooting method identification section Analyze past event data to learn about frequently occurring problems and how to deal with them.

2. The system of claim 1.

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