system

Abstract

The system according to this embodiment aims to detect technical problems occurring during an event in real time and resolve them quickly. [Solution] The system according to the embodiment comprises a detection unit, an identification unit, a resolution unit, a collection unit, an analysis unit, and a proposal unit. The detection unit detects technical problems in real time. The identification unit identifies the cause of the technical problem detected by the detection unit. The resolution unit implements a solution based on the cause identified by the identification unit. The collection unit collects feedback from participants after the solution has been implemented by the resolution unit. The analysis unit analyzes the feedback collected by the collection unit. The proposal unit proposes plans and improvements for the next event based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to detect and quickly solve technical troubles occurring during an event in real time.

[0005] The system according to the embodiment aims to detect and quickly solve technical troubles occurring during an event in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, an identification unit, a resolution unit, a collection unit, an analysis unit, and a proposal unit. The detection unit detects technical problems in real time. The identification unit identifies the cause of the technical problem detected by the detection unit. The resolution unit implements a solution based on the cause identified by the identification unit. The collection unit collects feedback from participants after the solution has been implemented by the resolution unit. The analysis unit analyzes the feedback collected by the collection unit. The proposal unit proposes plans and improvements for the next event based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect technical problems that occur during an event in real time and resolve them quickly. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that detects technical problems occurring during an event in real time, identifies the cause, and resolves them. This AI agent system includes a technical problem detection unit, a cause identification unit, a resolution unit, an instruction unit, a feedback collection unit, an analysis unit, and a proposal unit. The technical problem detection unit detects technical problems occurring during an event in real time. For example, it uses sensors and log data to detect malfunctions in sound equipment or network connection failures. The cause identification unit identifies the cause of the detected technical problem. For example, it identifies that the cause of a malfunction in sound equipment is a microphone failure. The resolution unit implements a solution based on the identified cause. For example, it executes a procedure to repair the microphone failure. If human intervention is required to implement the solution, the instruction unit provides instructions summarizing the necessary actions. For example, it instructs technical staff to replace the microphone. The feedback collection unit automatically collects feedback from participants. For example, it collects feedback using questionnaires or evaluation forms. The analysis unit analyzes the collected feedback. For example, it analyzes participant evaluations and comments to identify areas for improvement in the event. The proposal team autonomously proposes and implements plans and improvements for the next event based on the analysis results. For example, it might propose changing the placement of sound equipment. The AI ​​agent also has the ability to learn from past trouble data and feedback to provide predictions and preventative measures in advance. For example, based on past trouble data, it can predict troubles that are likely to occur at the next event and propose preventative measures. This allows the event to continue smoothly and improves the user experience. As a result, the AI ​​agent system can detect technical troubles that occur during the event in real time, identify the cause, and resolve it.

[0029] The AI ​​agent system according to this embodiment comprises a detection unit, an identification unit, a resolution unit, a collection unit, an analysis unit, and a proposal unit. The detection unit detects technical problems in real time. The detection unit detects technical problems using, for example, sensors or log data. For example, the detection unit detects malfunctions in sound equipment or network connection failures. The identification unit identifies the cause of the detected technical problem. For example, the identification unit identifies that the cause of the malfunction in the sound equipment is a microphone failure. The resolution unit implements a solution based on the identified cause. For example, the resolution unit executes a procedure to repair the microphone failure. The resolution unit can also, for example, instruct the microphone to be replaced. The collection unit collects feedback from participants. For example, the collection unit collects feedback using questionnaires or evaluation forms. The analysis unit analyzes the collected feedback. For example, the analysis unit analyzes participant evaluations and comments to identify areas for improvement in the event. The proposal unit proposes plans and improvements for the next event based on the analysis results. For example, the proposal unit suggests changing the arrangement of the sound equipment. As a result, the AI ​​agent system according to this embodiment can perform technical trouble detection, cause identification, resolution, feedback collection, analysis, and proposal in a series of steps.

[0030] The detection unit detects technical problems in real time. For example, it uses sensors and log data to detect technical problems. Specifically, it utilizes acoustic sensors and network monitoring tools to detect issues such as malfunctions in sound equipment and network connection failures. Acoustic sensors detect abnormal sound, issuing alerts, for example, if sound from speakers cuts out or noise occurs. Network monitoring tools monitor network traffic and connection status, detecting increases in abnormal packets or connection interruptions. Data obtained from these sensors and tools is transmitted in real time to a central monitoring system, aiding in the early detection of technical problems. Furthermore, the detection unit implements an anomaly detection algorithm using AI to automatically identify data that deviates from normal operating patterns. For example, it analyzes data from acoustic sensors to detect abnormal sounds that fall outside the normal volume level or frequency band. Network monitoring tools also learn abnormal traffic patterns based on past traffic data and detect anomalies in real time. This allows the detection unit to quickly and accurately detect technical problems, improving the overall reliability of the system.

[0031] The identification unit identifies the cause of detected technical problems. For example, it might determine that a malfunction in audio equipment is due to a microphone failure. Specifically, the identification unit analyzes data transmitted from the detection unit and performs a detailed diagnosis to identify the source and cause of the problem. For example, if a malfunction in audio equipment is detected, the identification unit checks the operating status of each component, such as microphones, speakers, and amplifiers, to identify which part is problematic. The identification unit implements an anomaly detection algorithm using AI to automatically identify data that deviates from normal operating patterns. For example, it analyzes data from acoustic sensors to detect abnormal sounds that are outside the normal volume level or frequency band. In addition, network monitoring tools learn abnormal traffic patterns based on past traffic data and detect anomalies in real time. This allows the identification unit to quickly and accurately detect technical problems and improve the overall reliability of the system.

[0032] The resolution unit implements solutions based on the identified cause. For example, the resolution unit executes procedures to repair a faulty microphone. Specifically, the resolution unit implements appropriate repair procedures and countermeasures based on the cause information provided by the identification unit. For example, if a microphone fault is identified, the resolution unit will instruct the replacement or repair of the microphone and procure the necessary parts and tools. The resolution unit can also use AI to suggest the optimal solution. For example, it can suggest the most effective repair procedure based on past repair history and trouble data, improving the efficiency of repair work. Furthermore, the resolution unit can also support remote troubleshooting. For example, a technician in a remote location can remotely access the system, identify the cause of the problem, and implement a solution. This allows the resolution unit to resolve technical problems quickly and effectively, minimizing system downtime.

[0033] The data collection department collects feedback from participants. This includes using methods such as questionnaires and evaluation forms. Specifically, the department distributes questionnaires and evaluation forms to participants after events or system use to collect their opinions and impressions. Questionnaires are distributed via online forms or mobile apps, making them easy for participants to answer. The data collection department can also utilize AI-powered natural language processing technology to automate feedback collection. For example, it can automatically analyze comments entered by participants in free-form text to extract important keywords and evaluation points. Furthermore, the data collection department can also collect feedback from social media and online review sites. This allows the data collection department to gather a wide range of feedback from diverse channels, which can then be used to improve systems and events.

[0034] The analysis department analyzes the collected feedback. For example, it analyzes participant ratings and comments to identify areas for improvement in the event. Specifically, the analysis department analyzes the feedback data provided by the collection department in detail to understand participants' satisfaction levels and areas of dissatisfaction. The analysis utilizes AI-based natural language processing technology and machine learning algorithms to extract important information from text data. For example, it analyzes participant comments to identify frequently occurring keywords and evaluation points. It also statistically analyzes numerical data from evaluation forms to quantitatively evaluate participant satisfaction and areas for improvement. Furthermore, by comparing current feedback data with past feedback data, the analysis department can identify trends and patterns and find areas for long-term improvement. This allows the analysis department to effectively analyze the collected feedback and use it to improve the system and events.

[0035] The proposal department proposes plans and improvements for the next event based on the analysis results. For example, the proposal department might suggest changing the placement of sound equipment. Specifically, based on the analysis results provided by the analysis department, the proposal department makes concrete suggestions for improvements to the next event and system. For example, if there is a lot of feedback regarding the placement of sound equipment, the proposal department will suggest changing the placement of speakers to improve sound quality. Also, if there is a lot of feedback regarding network connection failures, the proposal department will suggest strengthening the network infrastructure or introducing a backup system. Furthermore, the proposal department can also use AI to simulate the optimal improvement measures and predict their effects. For example, it can simulate multiple improvement measures and select the most effective one. This allows the proposal department to make concrete and effective improvement suggestions based on the analysis results, thereby improving the quality of the next event and system.

[0036] The resolution unit includes an instruction unit that provides consolidated instructions for actions requiring human intervention. For example, the resolution unit can instruct technical staff to replace a microphone. The resolution unit can also instruct technical staff to reconnect the network. The resolution unit can also instruct technical staff to adjust the sound equipment. This allows for a quick response even when human intervention is required, by providing consolidated instructions for actions. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without AI.

[0037] The data collection unit collects feedback from participants using questionnaires and evaluation forms. For example, the data collection unit distributes questionnaires after the event to collect feedback from participants. The data collection unit can also collect feedback from participants using online evaluation forms. The data collection unit can also collect feedback in real time during the event. This allows for efficient collection of participant feedback using questionnaires and evaluation forms. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0038] The analysis unit analyzes the collected feedback and identifies areas for improvement in the event. For example, the analysis unit analyzes participant ratings and comments to identify areas for improvement. The analysis unit can also analyze the text data of the feedback and extract important keywords. The analysis unit can also analyze the evaluation scores of the feedback and calculate an overall evaluation of the event. In this way, areas for improvement in the event can be identified by analyzing the feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0039] The proposal department makes suggestions to change the arrangement of the sound equipment based on the analysis results. For example, the proposal department may suggest changing the placement of speakers. The proposal department may also suggest changing the placement of microphones. The proposal department may also suggest changing the type of sound equipment. By making suggestions to change the arrangement of sound equipment based on the analysis results, the sound environment for the next event can be improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0040] The proposal unit learns from past trouble data, predicts potential troubles in the next event, and proposes preventative measures. For example, the proposal unit predicts potential troubles in the next event based on past trouble data. The proposal unit can also, for example, refer to past trouble data and propose preventative measures. The proposal unit can also, for example, analyze past trouble data and propose measures to prevent troubles in the next event. In this way, by learning from past trouble data, it is possible to predict potential troubles in the next event and propose preventative measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0041] The detection unit performs early detection of similar problems by referring to past problem data. For example, the detection unit detects similar problem patterns based on past problem data. The detection unit can also perform early detection by referring to past problem data and considering the frequency of problem occurrences. The detection unit can also perform early detection by analyzing past problem data and predicting the time of problem occurrences. This makes it possible to detect similar problems early by referring to past problem data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0042] The detection unit dynamically changes the detection algorithm according to the progress of the event. For example, at the start of an event, the detection unit sets the detection algorithm to high sensitivity. For example, in the middle of an event, the detection unit may set the detection algorithm to medium sensitivity. For example, at the end of an event, the detection unit may set the detection algorithm to low sensitivity. By dynamically changing the detection algorithm according to the progress of the event, more appropriate trouble detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0043] The detection unit adjusts its detection range according to the scale of the event and the number of participants. For example, in large-scale events, the detection unit performs detection over a wide area. In small-scale events, the detection unit can also perform detection over a limited area. For example, if there are many participants, the detection unit can expand its detection range. By adjusting the detection range according to the scale of the event and the number of participants, more appropriate trouble detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0044] The detection unit improves detection accuracy by changing the type and arrangement of sensors. The detection unit can, for example, add acoustic sensors to detect acoustic problems. The detection unit can also, for example, add network sensors to detect connection failures. The detection unit can also, for example, optimize the arrangement of sensors to improve detection accuracy. In this way, detection accuracy can be improved by changing the type and arrangement of sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0045] The identification unit improves identification accuracy by referring to past trouble cause data. The identification unit identifies similar causes based on past trouble cause data, for example. The identification unit can also improve identification accuracy by referring to past trouble cause data, for example. The identification unit can also optimize the identification algorithm by analyzing past trouble cause data, for example. This allows for improved identification accuracy by referring to past trouble cause data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0046] The identification unit optimizes the identification algorithm based on the time and location of the trouble. The identification unit may, for example, adjust the identification algorithm based on the time of the trouble. The identification unit may also optimize the identification algorithm based on the location of the trouble. The identification unit may also optimize the identification algorithm considering the time and location of the trouble. By optimizing the identification algorithm based on the time and location of the trouble, the identification accuracy can be improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0047] The identification unit applies different identification methods depending on the type of trouble. For example, in the case of an acoustic trouble, the identification unit uses an acoustic sensor for identification. For example, in the case of a network trouble, the identification unit can also use a network sensor for identification. For example, in the case of a power supply trouble, the identification unit can also use a power supply sensor for identification. By applying different identification methods depending on the type of trouble, the accuracy of identification can be improved. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or without using AI.

[0048] The identification unit adjusts specific priorities considering the scope of the trouble's impact. For example, the identification unit prioritizes identifying troubles with a wide scope of impact. The identification unit may also postpone identifying troubles with a narrow scope of impact. The identification unit can also adjust specific priorities considering the scope of impact. By adjusting specific priorities considering the scope of the trouble's impact, it becomes possible to identify the cause more appropriately. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0049] The solution unit selects the optimal solution by referring to past solution data. The solution unit selects the optimal solution based on past solution data, for example. The solution unit can also improve the accuracy of the solution by referring to past solution data, for example. The solution unit can also analyze past solution data and select the optimal solution, for example. This makes it possible to select the optimal solution by referring to past solution data. Some or all of the above processing in the solution unit may be performed using AI, for example, or without using AI.

[0050] The resolution unit customizes the resolution method according to the type and impact of the problem. For example, in the case of an audio problem, the resolution unit customizes the repair method for the audio equipment. For example, in the case of a network problem, the resolution unit can also customize the repair method for the network equipment. For example, in the case of a power supply problem, the resolution unit can also customize the repair method for the power supply equipment. By customizing the resolution method according to the type and impact of the problem, the accuracy of the resolution can be improved. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without using AI.

[0051] The resolution unit selects a solution method based on the location where the problem occurred. For example, the resolution unit selects the optimal solution method based on the location where the problem occurred. The resolution unit can also customize the solution method, for example, by considering the location where the problem occurred. The resolution unit can also select a solution method according to the location where the problem occurred. This improves the accuracy of the solution by selecting a solution method based on the location where the problem occurred. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without using AI.

[0052] The resolution unit determines the priority of solutions by considering the scope of impact of the trouble. For example, the resolution unit may prioritize resolving troubles with a wide scope of impact. For example, the resolution unit may postpone resolving troubles with a narrow scope of impact. For example, the resolution unit may also determine the priority of solutions by considering the scope of impact. This allows for a more appropriate solution by determining the priority of solutions by considering the scope of impact of the trouble. Some or all of the above processing in the resolution unit may be performed using AI, for example, or without using AI.

[0053] The data collection unit optimizes the data collection method by referring to past feedback data. The data collection unit selects the optimal data collection method based on past feedback data, for example. The data collection unit can also optimize the data collection method by referring to past feedback data, for example. The data collection unit can also analyze past feedback data and select the optimal data collection method, for example. This allows the data collection method to be optimized by referring to past feedback data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0054] The data collection unit changes its collection method depending on the type and scale of the event. For example, for large-scale events, the data collection unit may use questionnaires to collect feedback. For example, for small-scale events, the data collection unit may also use interviews to collect feedback. The data collection unit may also select the most appropriate collection method depending on the type of event. By changing the collection method according to the type and scale of the event, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0055] The data collection unit adjusts the feedback collection method considering the participants' attribute information. For example, the data collection unit selects the optimal feedback collection method according to the age group of the participants. The data collection unit can also customize the feedback collection method according to the occupation of the participants. The data collection unit can also adjust the feedback collection method based on the interests of the participants. By adjusting the feedback collection method considering the participants' attribute information, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0056] The data collection unit applies different collection methods depending on the type of feedback. For example, in the case of quantitative feedback, the data collection unit uses questionnaires. For example, in the case of qualitative feedback, the data collection unit may also use interviews. The data collection unit may also select the most appropriate collection method depending on the type of feedback. By applying different collection methods depending on the type of feedback, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0057] The analysis unit improves analysis accuracy by referring to past feedback data. The analysis unit improves analysis accuracy based on past feedback data, for example. The analysis unit can also optimize the analysis algorithm by referring to past feedback data, for example. The analysis unit can also improve analysis accuracy by analyzing past feedback data, for example. In this way, analysis accuracy can be improved by referring to past feedback data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0058] The analysis unit changes its analysis method depending on the type and content of the feedback. For example, in the case of quantitative feedback, the analysis unit uses statistical analysis methods. For example, in the case of qualitative feedback, the analysis unit may also use text mining methods. The analysis unit may also select the most suitable analysis method depending on the type and content of the feedback. By changing the analysis method according to the type and content of the feedback, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0059] The analysis unit optimizes the analysis method based on the timing of feedback submission. The analysis unit selects the optimal analysis method based on the timing of feedback submission, for example. The analysis unit can also optimize the analysis method by considering the timing of feedback submission, for example. The analysis unit can also select an analysis method according to the timing of feedback submission, for example. By optimizing the analysis method based on the timing of feedback submission, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0060] The analysis unit adjusts the order of analysis results based on the relevance of the feedback. For example, the analysis unit prioritizes analyzing highly relevant feedback. The analysis unit may also postpone analyzing less relevant feedback. The analysis unit may also adjust the order of analysis results considering the relevance of the feedback. This allows for the display of more important analysis results by adjusting the order of analysis results based on the relevance of the feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0061] The proposal unit improves proposal accuracy by referring to past proposal data. The proposal unit improves proposal accuracy based on past proposal data, for example. The proposal unit can also optimize the proposal algorithm by referring to past proposal data, for example. The proposal unit can also improve proposal accuracy by analyzing past proposal data, for example. In this way, proposal accuracy can be improved by referring to past proposal data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0062] The proposal department modifies its proposal methodology according to the type and scale of the event. For example, for large-scale events, the proposal department provides detailed proposals. For small-scale events, the proposal department may provide concise proposals. The proposal department may also select the most appropriate proposal methodology depending on the type of event. By changing the proposal methodology according to the type and scale of the event, more appropriate proposals can be made. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI.

[0063] The proposal unit proposes preventive measures by referring to past trouble data. The proposal unit proposes preventive measures based on past trouble data, for example. The proposal unit can also improve the accuracy of preventive measures by referring to past trouble data, for example. The proposal unit can also analyze past trouble data and propose the optimal preventive measures, for example. This allows for an improvement in the accuracy of preventive measures by referring to past trouble data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0064] The proposal team adjusts the content of their proposals based on the progress of the event. For example, at the start of the event, the proposal team prioritizes important proposals. For example, in the middle of the event, the proposal team may make detailed proposals. For example, at the end of the event, the proposal team may make concise proposals. By adjusting the content of proposals based on the progress of the event, more appropriate proposals can be made. Some or all of the above processes in the proposal team may be performed using AI, for example, or not using AI.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The AI ​​agent system can also be equipped with a prediction unit. This unit predicts potential technical problems based on the progress of the event and past data. For example, it can analyze trouble data from past events to predict problems likely to occur at the next event. It can also predict problems likely to occur during the event based on data collected in real time. Furthermore, the prediction unit can suggest preventative measures against predicted problems. Therefore, by incorporating a prediction unit, problems can be predicted in advance and preventative measures can be taken, enabling the smooth running of the event.

[0067] The AI ​​agent system can also be equipped with a notification unit. This unit can quickly notify relevant parties when technical problems occur. For example, it can notify technical staff of a problem in real time. It can also notify event organizers and participants of the problem. Furthermore, it can provide real-time updates on the problem's resolution. By incorporating this notification unit, relevant parties can respond quickly, minimizing the impact of the problem.

[0068] AI agent systems can also be equipped with a learning unit. This learning unit improves the system's accuracy based on past trouble data and feedback data. For example, the learning unit can analyze past trouble data and learn trouble patterns. It can also identify areas for system improvement based on feedback data and incorporate them into future events. Furthermore, the learning unit can continuously improve the system's accuracy based on data collected in real time. As a result, incorporating a learning unit improves the system's accuracy, enabling more effective trouble detection and resolution.

[0069] The AI ​​agent system can also be equipped with a simulation unit. This unit simulates the occurrence of technical problems during the event preparation phase. For example, it can simulate potential problems that may occur during the event based on past trouble data. Furthermore, the simulation unit can propose preventative measures based on the simulation results. It can also notify relevant parties of the simulation results, enabling them to take countermeasures in advance. Therefore, by incorporating a simulation unit, problems can be predicted and preventative measures taken beforehand, ensuring the smooth running of the event.

[0070] The AI ​​agent system can also be equipped with an evaluation unit. This unit evaluates the handling of technical issues after the event. For example, it can assess the time from the occurrence of an issue to its resolution. It can also evaluate the effectiveness of the response based on the scope of the issue's impact and participant feedback. Furthermore, based on the evaluation results, the evaluation unit can suggest improvements for future events. By incorporating an evaluation unit, the effectiveness of troubleshooting can be evaluated, and areas for improvement for future events can be identified, thereby improving the overall quality of the event.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The detection unit detects technical problems in real time. For example, it uses sensors and log data to detect malfunctions in sound equipment or network connection failures. Step 2: The identification unit identifies the cause of the detected technical problem. For example, it identifies that the cause of the sound equipment malfunction is a microphone failure. Step 3: The resolution team implements solutions based on the identified cause. For example, they might take steps to repair a faulty microphone or instruct the microphone to be replaced. Step 4: The collection team gathers feedback from participants. For example, they collect feedback using questionnaires or evaluation forms. Step 5: The analysis unit analyzes the collected feedback. For example, it analyzes participant ratings and comments to identify areas for improvement in the event. Step 6: The proposal team proposes plans and improvements for the next event based on the analysis results. For example, they might propose changing the layout of the sound equipment.

[0073] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that detects technical problems occurring during an event in real time, identifies the cause, and resolves them. This AI agent system includes a technical problem detection unit, a cause identification unit, a resolution unit, an instruction unit, a feedback collection unit, an analysis unit, and a proposal unit. The technical problem detection unit detects technical problems occurring during an event in real time. For example, it uses sensors and log data to detect malfunctions in sound equipment or network connection failures. The cause identification unit identifies the cause of the detected technical problem. For example, it identifies that the cause of a malfunction in sound equipment is a microphone failure. The resolution unit implements a solution based on the identified cause. For example, it executes a procedure to repair the microphone failure. If human intervention is required to implement the solution, the instruction unit provides instructions summarizing the necessary actions. For example, it instructs technical staff to replace the microphone. The feedback collection unit automatically collects feedback from participants. For example, it collects feedback using questionnaires or evaluation forms. The analysis unit analyzes the collected feedback. For example, it analyzes participant evaluations and comments to identify areas for improvement in the event. The proposal team autonomously proposes and implements plans and improvements for the next event based on the analysis results. For example, it might propose changing the placement of sound equipment. The AI ​​agent also has the ability to learn from past trouble data and feedback to provide predictions and preventative measures in advance. For example, based on past trouble data, it can predict troubles that are likely to occur at the next event and propose preventative measures. This allows the event to continue smoothly and improves the user experience. As a result, the AI ​​agent system can detect technical troubles that occur during the event in real time, identify the cause, and resolve it.

[0074] The AI ​​agent system according to this embodiment comprises a detection unit, an identification unit, a resolution unit, a collection unit, an analysis unit, and a proposal unit. The detection unit detects technical problems in real time. The detection unit detects technical problems using, for example, sensors or log data. For example, the detection unit detects malfunctions in sound equipment or network connection failures. The identification unit identifies the cause of the detected technical problem. For example, the identification unit identifies that the cause of the malfunction in the sound equipment is a microphone failure. The resolution unit implements a solution based on the identified cause. For example, the resolution unit executes a procedure to repair the microphone failure. The resolution unit can also, for example, instruct the microphone to be replaced. The collection unit collects feedback from participants. For example, the collection unit collects feedback using questionnaires or evaluation forms. The analysis unit analyzes the collected feedback. For example, the analysis unit analyzes participant evaluations and comments to identify areas for improvement in the event. The proposal unit proposes plans and improvements for the next event based on the analysis results. For example, the proposal unit suggests changing the arrangement of the sound equipment. As a result, the AI ​​agent system according to this embodiment can perform technical trouble detection, cause identification, resolution, feedback collection, analysis, and proposal in a series of steps.

[0075] The detection unit detects technical problems in real time. For example, it uses sensors and log data to detect technical problems. Specifically, it utilizes acoustic sensors and network monitoring tools to detect issues such as malfunctions in sound equipment and network connection failures. Acoustic sensors detect abnormal sound, issuing alerts, for example, if sound from speakers cuts out or noise occurs. Network monitoring tools monitor network traffic and connection status, detecting increases in abnormal packets or connection interruptions. Data obtained from these sensors and tools is transmitted in real time to a central monitoring system, aiding in the early detection of technical problems. Furthermore, the detection unit implements an anomaly detection algorithm using AI to automatically identify data that deviates from normal operating patterns. For example, it analyzes data from acoustic sensors to detect abnormal sounds that fall outside the normal volume level or frequency band. Network monitoring tools also learn abnormal traffic patterns based on past traffic data and detect anomalies in real time. This allows the detection unit to quickly and accurately detect technical problems, improving the overall reliability of the system.

[0076] The identification unit identifies the cause of detected technical problems. For example, it might determine that a malfunction in audio equipment is due to a microphone failure. Specifically, the identification unit analyzes data transmitted from the detection unit and performs a detailed diagnosis to identify the source and cause of the problem. For example, if a malfunction in audio equipment is detected, the identification unit checks the operating status of each component, such as microphones, speakers, and amplifiers, to identify which part is problematic. The identification unit implements an anomaly detection algorithm using AI to automatically identify data that deviates from normal operating patterns. For example, it analyzes data from acoustic sensors to detect abnormal sounds that are outside the normal volume level or frequency band. In addition, network monitoring tools learn abnormal traffic patterns based on past traffic data and detect anomalies in real time. This allows the identification unit to quickly and accurately detect technical problems and improve the overall reliability of the system.

[0077] The resolution unit implements solutions based on the identified cause. For example, the resolution unit executes procedures to repair a faulty microphone. Specifically, the resolution unit implements appropriate repair procedures and countermeasures based on the cause information provided by the identification unit. For example, if a microphone fault is identified, the resolution unit will instruct the replacement or repair of the microphone and procure the necessary parts and tools. The resolution unit can also use AI to suggest the optimal solution. For example, it can suggest the most effective repair procedure based on past repair history and trouble data, improving the efficiency of repair work. Furthermore, the resolution unit can also support remote troubleshooting. For example, a technician in a remote location can remotely access the system, identify the cause of the problem, and implement a solution. This allows the resolution unit to resolve technical problems quickly and effectively, minimizing system downtime.

[0078] The data collection department collects feedback from participants. This includes using methods such as questionnaires and evaluation forms. Specifically, the department distributes questionnaires and evaluation forms to participants after events or system use to collect their opinions and impressions. Questionnaires are distributed via online forms or mobile apps, making them easy for participants to answer. The data collection department can also utilize AI-powered natural language processing technology to automate feedback collection. For example, it can automatically analyze comments entered by participants in free-form text to extract important keywords and evaluation points. Furthermore, the data collection department can also collect feedback from social media and online review sites. This allows the data collection department to gather a wide range of feedback from diverse channels, which can then be used to improve systems and events.

[0079] The analysis department analyzes the collected feedback. For example, it analyzes participant ratings and comments to identify areas for improvement in the event. Specifically, the analysis department analyzes the feedback data provided by the collection department in detail to understand participants' satisfaction levels and areas of dissatisfaction. The analysis utilizes AI-based natural language processing technology and machine learning algorithms to extract important information from text data. For example, it analyzes participant comments to identify frequently occurring keywords and evaluation points. It also statistically analyzes numerical data from evaluation forms to quantitatively evaluate participant satisfaction and areas for improvement. Furthermore, by comparing current feedback data with past feedback data, the analysis department can identify trends and patterns and find areas for long-term improvement. This allows the analysis department to effectively analyze the collected feedback and use it to improve the system and events.

[0080] The proposal department proposes plans and improvements for the next event based on the analysis results. For example, the proposal department might suggest changing the placement of sound equipment. Specifically, based on the analysis results provided by the analysis department, the proposal department makes concrete suggestions for improvements to the next event and system. For example, if there is a lot of feedback regarding the placement of sound equipment, the proposal department will suggest changing the placement of speakers to improve sound quality. Also, if there is a lot of feedback regarding network connection failures, the proposal department will suggest strengthening the network infrastructure or introducing a backup system. Furthermore, the proposal department can also use AI to simulate the optimal improvement measures and predict their effects. For example, it can simulate multiple improvement measures and select the most effective one. This allows the proposal department to make concrete and effective improvement suggestions based on the analysis results, thereby improving the quality of the next event and system.

[0081] The resolution unit includes an instruction unit that provides consolidated instructions for actions requiring human intervention. For example, the resolution unit can instruct technical staff to replace a microphone. The resolution unit can also instruct technical staff to reconnect the network. The resolution unit can also instruct technical staff to adjust the sound equipment. This allows for a quick response even when human intervention is required, by providing consolidated instructions for actions. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without AI.

[0082] The data collection unit collects feedback from participants using questionnaires and evaluation forms. For example, the data collection unit distributes questionnaires after the event to collect feedback from participants. The data collection unit can also collect feedback from participants using online evaluation forms. The data collection unit can also collect feedback in real time during the event. This allows for efficient collection of participant feedback using questionnaires and evaluation forms. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI.

[0083] The analysis unit analyzes the collected feedback and identifies areas for improvement in the event. For example, the analysis unit analyzes participant ratings and comments to identify areas for improvement. The analysis unit can also analyze the text data of the feedback and extract important keywords. The analysis unit can also analyze the evaluation scores of the feedback and calculate an overall evaluation of the event. In this way, areas for improvement in the event can be identified by analyzing the feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0084] The proposal department makes suggestions to change the arrangement of the sound equipment based on the analysis results. For example, the proposal department may suggest changing the placement of speakers. The proposal department may also suggest changing the placement of microphones. The proposal department may also suggest changing the type of sound equipment. By making suggestions to change the arrangement of sound equipment based on the analysis results, the sound environment for the next event can be improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or without using AI.

[0085] The proposal unit learns from past trouble data, predicts potential troubles in the next event, and proposes preventative measures. For example, the proposal unit predicts potential troubles in the next event based on past trouble data. The proposal unit can also, for example, refer to past trouble data and propose preventative measures. The proposal unit can also, for example, analyze past trouble data and propose measures to prevent troubles in the next event. In this way, by learning from past trouble data, it is possible to predict potential troubles in the next event and propose preventative measures. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0086] The detection unit estimates the user's emotions and adjusts the detection sensitivity of technical problems based on the estimated user emotions. For example, if the user is stressed, the detection unit increases the detection sensitivity to quickly detect problems. For example, if the user is relaxed, the detection unit can also decrease the detection sensitivity to reduce unnecessary alerts. For example, if the user is tense, the detection unit can set the detection sensitivity to a moderate level to provide appropriate alerts. This allows for more appropriate problem detection by adjusting the detection sensitivity based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI.

[0087] The detection unit performs early detection of similar problems by referring to past problem data. For example, the detection unit detects similar problem patterns based on past problem data. The detection unit can also perform early detection by referring to past problem data and considering the frequency of problem occurrences. The detection unit can also perform early detection by analyzing past problem data and predicting the time of problem occurrences. This makes it possible to detect similar problems early by referring to past problem data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0088] The detection unit dynamically changes the detection algorithm according to the progress of the event. For example, at the start of an event, the detection unit sets the detection algorithm to high sensitivity. For example, in the middle of an event, the detection unit may set the detection algorithm to medium sensitivity. For example, at the end of an event, the detection unit may set the detection algorithm to low sensitivity. By dynamically changing the detection algorithm according to the progress of the event, more appropriate trouble detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0089] The detection unit estimates the user's emotions and adjusts the display method of the detection results based on the estimated user emotions. For example, if the user is stressed, the detection unit provides a simple display method. For example, if the user is relaxed, the detection unit may also provide a detailed display method. For example, if the user is tense, the detection unit may also provide a highly visible display method. By adjusting the display method of the detection results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0090] The detection unit adjusts its detection range according to the scale of the event and the number of participants. For example, in large-scale events, the detection unit performs detection over a wide area. In small-scale events, the detection unit can also perform detection over a limited area. For example, if there are many participants, the detection unit can expand its detection range. By adjusting the detection range according to the scale of the event and the number of participants, more appropriate trouble detection becomes possible. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0091] The detection unit improves detection accuracy by changing the type and arrangement of sensors. The detection unit can, for example, add acoustic sensors to detect acoustic problems. The detection unit can also, for example, add network sensors to detect connection failures. The detection unit can also, for example, optimize the arrangement of sensors to improve detection accuracy. In this way, detection accuracy can be improved by changing the type and arrangement of sensors. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI.

[0092] The identification unit estimates the user's emotions and determines the priority of cause identification based on the estimated user emotions. For example, if the user is stressed, the identification unit quickly identifies the cause. For example, if the user is relaxed, the identification unit can also perform a more detailed cause identification. For example, if the user is tense, the identification unit can prioritize identifying important causes. This allows for more appropriate cause identification by determining the priority of cause identification based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI.

[0093] The identification unit improves identification accuracy by referring to past trouble cause data. The identification unit identifies similar causes based on past trouble cause data, for example. The identification unit can also improve identification accuracy by referring to past trouble cause data, for example. The identification unit can also optimize the identification algorithm by analyzing past trouble cause data, for example. This allows for improved identification accuracy by referring to past trouble cause data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0094] The identification unit optimizes the identification algorithm based on the time and location of the trouble. The identification unit may, for example, adjust the identification algorithm based on the time of the trouble. The identification unit may also optimize the identification algorithm based on the location of the trouble. The identification unit may also optimize the identification algorithm considering the time and location of the trouble. By optimizing the identification algorithm based on the time and location of the trouble, the identification accuracy can be improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0095] The identification unit estimates the user's emotions and adjusts the display method of the identification result based on the estimated user emotions. For example, if the user is stressed, the identification unit provides a simple display method. For example, if the user is relaxed, the identification unit may also provide a detailed display method. For example, if the user is tense, the identification unit may also provide a highly visible display method. By adjusting the display method of the identification result based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI, for example, or without using AI.

[0096] The identification unit applies different identification methods depending on the type of trouble. For example, in the case of an acoustic trouble, the identification unit uses an acoustic sensor for identification. For example, in the case of a network trouble, the identification unit can also use a network sensor for identification. For example, in the case of a power supply trouble, the identification unit can also use a power supply sensor for identification. By applying different identification methods depending on the type of trouble, the accuracy of identification can be improved. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or without using AI.

[0097] The identification unit adjusts specific priorities considering the scope of the trouble's impact. For example, the identification unit prioritizes identifying troubles with a wide scope of impact. The identification unit may also postpone identifying troubles with a narrow scope of impact. The identification unit can also adjust specific priorities considering the scope of impact. By adjusting specific priorities considering the scope of the trouble's impact, it becomes possible to identify the cause more appropriately. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI.

[0098] The solution unit estimates the user's emotions and adjusts the execution order of solutions based on the estimated emotions. For example, if the user is stressed, the solution unit will execute solutions quickly. For example, if the user is relaxed, the solution unit may also execute detailed solutions. For example, if the user is tense, the solution unit may prioritize executing important solutions. This allows for more appropriate solutions by adjusting the execution order of solutions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using AI or not using AI.

[0099] The solution unit selects the optimal solution by referring to past solution data. The solution unit selects the optimal solution based on past solution data, for example. The solution unit can also improve the accuracy of the solution by referring to past solution data, for example. The solution unit can also analyze past solution data and select the optimal solution, for example. This makes it possible to select the optimal solution by referring to past solution data. Some or all of the above processing in the solution unit may be performed using AI, for example, or without using AI.

[0100] The resolution unit customizes the resolution method according to the type and impact of the problem. For example, in the case of an audio problem, the resolution unit customizes the repair method for the audio equipment. For example, in the case of a network problem, the resolution unit can also customize the repair method for the network equipment. For example, in the case of a power supply problem, the resolution unit can also customize the repair method for the power supply equipment. By customizing the resolution method according to the type and impact of the problem, the accuracy of the resolution can be improved. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without using AI.

[0101] The solution unit estimates the user's emotions and adjusts how the solution is displayed based on the estimated emotions. For example, if the user is stressed, the solution unit provides a simple display method. For example, if the user is relaxed, the solution unit may also provide a detailed display method. For example, if the user is tense, the solution unit may also provide a highly visible display method. By adjusting how the solution is displayed based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI.

[0102] The resolution unit selects a solution method based on the location where the problem occurred. For example, the resolution unit selects the optimal solution method based on the location where the problem occurred. The resolution unit can also customize the solution method, for example, by considering the location where the problem occurred. The resolution unit can also select a solution method according to the location where the problem occurred. This improves the accuracy of the solution by selecting a solution method based on the location where the problem occurred. Some or all of the above-described processes in the resolution unit may be performed using AI, for example, or without using AI.

[0103] The resolution unit determines the priority of solutions by considering the scope of impact of the trouble. For example, the resolution unit may prioritize resolving troubles with a wide scope of impact. For example, the resolution unit may postpone resolving troubles with a narrow scope of impact. For example, the resolution unit may also determine the priority of solutions by considering the scope of impact. This allows for a more appropriate solution by determining the priority of solutions by considering the scope of impact of the trouble. Some or all of the above processing in the resolution unit may be performed using AI, for example, or without using AI.

[0104] The data collection unit estimates the user's emotions and adjusts the timing of feedback collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit collects feedback after the event ends. For example, if the user is stressed, the data collection unit may collect feedback during the event. For example, if the user is tense, the data collection unit may collect feedback immediately after the event ends. By adjusting the timing of feedback collection based on the user's emotions, feedback can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI.

[0105] The data collection unit optimizes the data collection method by referring to past feedback data. The data collection unit selects the optimal data collection method based on past feedback data, for example. The data collection unit can also optimize the data collection method by referring to past feedback data, for example. The data collection unit can also analyze past feedback data and select the optimal data collection method, for example. This allows the data collection method to be optimized by referring to past feedback data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0106] The data collection unit changes its collection method depending on the type and scale of the event. For example, for large-scale events, the data collection unit may use questionnaires to collect feedback. For example, for small-scale events, the data collection unit may also use interviews to collect feedback. The data collection unit may also select the most appropriate collection method depending on the type of event. By changing the collection method according to the type and scale of the event, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0107] The data collection unit estimates the user's emotions and determines the priority of feedback to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting important feedback. For example, if the user is relaxed, the data collection unit may also collect detailed feedback. For example, if the user is tense, the data collection unit may also prioritize collecting concise feedback. This allows for the collection of more important feedback by prioritizing the feedback collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI.

[0108] The data collection unit adjusts the feedback collection method considering the participants' attribute information. For example, the data collection unit selects the optimal feedback collection method according to the age group of the participants. The data collection unit can also customize the feedback collection method according to the occupation of the participants. The data collection unit can also adjust the feedback collection method based on the interests of the participants. By adjusting the feedback collection method considering the participants' attribute information, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0109] The data collection unit applies different collection methods depending on the type of feedback. For example, in the case of quantitative feedback, the data collection unit uses questionnaires. For example, in the case of qualitative feedback, the data collection unit may also use interviews. The data collection unit may also select the most appropriate collection method depending on the type of feedback. By applying different collection methods depending on the type of feedback, it becomes possible to collect more appropriate feedback. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0110] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple display method. For example, if the user is relaxed, the analysis unit may also provide a detailed display method. For example, if the user is tense, the analysis unit may also provide a highly visible display method. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0111] The analysis unit improves analysis accuracy by referring to past feedback data. The analysis unit improves analysis accuracy based on past feedback data, for example. The analysis unit can also optimize the analysis algorithm by referring to past feedback data, for example. The analysis unit can also improve analysis accuracy by analyzing past feedback data, for example. In this way, analysis accuracy can be improved by referring to past feedback data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0112] The analysis unit changes its analysis method depending on the type and content of the feedback. For example, in the case of quantitative feedback, the analysis unit uses statistical analysis methods. For example, in the case of qualitative feedback, the analysis unit may also use text mining methods. The analysis unit may also select the most suitable analysis method depending on the type and content of the feedback. By changing the analysis method according to the type and content of the feedback, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0113] The analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit may also display detailed analysis results. For example, if the user is tense, the analysis unit may also prioritize displaying concise analysis results. This allows for the prioritization of more important analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI.

[0114] The analysis unit optimizes the analysis method based on the timing of feedback submission. The analysis unit selects the optimal analysis method based on the timing of feedback submission, for example. The analysis unit can also optimize the analysis method by considering the timing of feedback submission, for example. The analysis unit can also select an analysis method according to the timing of feedback submission, for example. By optimizing the analysis method based on the timing of feedback submission, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI.

[0115] The analysis unit adjusts the order of analysis results based on the relevance of the feedback. For example, the analysis unit prioritizes analyzing highly relevant feedback. The analysis unit may also postpone analyzing less relevant feedback. The analysis unit may also adjust the order of analysis results considering the relevance of the feedback. This allows for the display of more important analysis results by adjusting the order of analysis results based on the relevance of the feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI.

[0116] The suggestion section estimates the user's emotions and adjusts how the suggestions are displayed based on the estimated emotions. For example, if the user is stressed, the suggestion section provides a simple display method. For example, if the user is relaxed, the suggestion section may provide a detailed display method. For example, if the user is tense, the suggestion section may provide a highly visible display method. By adjusting how the suggestions are displayed based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or without AI.

[0117] The proposal unit improves proposal accuracy by referring to past proposal data. The proposal unit improves proposal accuracy based on past proposal data, for example. The proposal unit can also optimize the proposal algorithm by referring to past proposal data, for example. The proposal unit can also improve proposal accuracy by analyzing past proposal data, for example. In this way, proposal accuracy can be improved by referring to past proposal data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0118] The proposal department modifies its proposal methodology according to the type and scale of the event. For example, for large-scale events, the proposal department provides detailed proposals. For small-scale events, the proposal department may provide concise proposals. The proposal department may also select the most appropriate proposal methodology depending on the type of event. By changing the proposal methodology according to the type and scale of the event, more appropriate proposals can be made. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI.

[0119] The suggestion section estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is stressed, the suggestion section will prioritize displaying important suggestions. For example, if the user is relaxed, the suggestion section may also display detailed suggestions. For example, if the user is tense, the suggestion section may also prioritize displaying concise suggestions. This allows for prioritizing more important suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using AI or not using AI.

[0120] The proposal unit proposes preventive measures by referring to past trouble data. The proposal unit proposes preventive measures based on past trouble data, for example. The proposal unit can also improve the accuracy of preventive measures by referring to past trouble data, for example. The proposal unit can also analyze past trouble data and propose the optimal preventive measures, for example. This allows for an improvement in the accuracy of preventive measures by referring to past trouble data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI.

[0121] The proposal team adjusts the content of their proposals based on the progress of the event. For example, at the start of the event, the proposal team prioritizes important proposals. For example, in the middle of the event, the proposal team may make detailed proposals. For example, at the end of the event, the proposal team may make concise proposals. By adjusting the content of proposals based on the progress of the event, more appropriate proposals can be made. Some or all of the above processes in the proposal team may be performed using AI, for example, or not using AI.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The AI ​​agent system can also be equipped with a prediction unit. This unit predicts potential technical problems based on the progress of the event and past data. For example, it can analyze trouble data from past events to predict problems likely to occur at the next event. It can also predict problems likely to occur during the event based on data collected in real time. Furthermore, the prediction unit can suggest preventative measures against predicted problems. Therefore, by incorporating a prediction unit, problems can be predicted in advance and preventative measures can be taken, enabling the smooth running of the event.

[0124] The AI ​​agent system can also be equipped with a notification unit. This unit can quickly notify relevant parties when technical problems occur. For example, it can notify technical staff of a problem in real time. It can also notify event organizers and participants of the problem. Furthermore, it can provide real-time updates on the problem's resolution. By incorporating this notification unit, relevant parties can respond quickly, minimizing the impact of the problem.

[0125] AI agent systems can also be equipped with a learning unit. This learning unit improves the system's accuracy based on past trouble data and feedback data. For example, the learning unit can analyze past trouble data and learn trouble patterns. It can also identify areas for system improvement based on feedback data and incorporate them into future events. Furthermore, the learning unit can continuously improve the system's accuracy based on data collected in real time. As a result, incorporating a learning unit improves the system's accuracy, enabling more effective trouble detection and resolution.

[0126] The AI ​​agent system can also be equipped with a simulation unit. This unit simulates the occurrence of technical problems during the event preparation phase. For example, it can simulate potential problems that may occur during the event based on past trouble data. Furthermore, the simulation unit can propose preventative measures based on the simulation results. It can also notify relevant parties of the simulation results, enabling them to take countermeasures in advance. Therefore, by incorporating a simulation unit, problems can be predicted and preventative measures taken beforehand, ensuring the smooth running of the event.

[0127] The AI ​​agent system can also be equipped with an evaluation unit. This unit evaluates the handling of technical issues after the event. For example, it can assess the time from the occurrence of an issue to its resolution. It can also evaluate the effectiveness of the response based on the scope of the issue's impact and participant feedback. Furthermore, based on the evaluation results, the evaluation unit can suggest improvements for future events. By incorporating an evaluation unit, the effectiveness of troubleshooting can be evaluated, and areas for improvement for future events can be identified, thereby improving the overall quality of the event.

[0128] The AI ​​agent system can also be equipped with an emotion estimation unit. This unit estimates participants' emotions in real time and reflects this in the event's progress. For example, it can analyze participants' facial expressions and voice data to estimate their emotions. Furthermore, it can adjust the event's progress based on the estimated emotions. It can also evaluate participant satisfaction based on the emotional data. Therefore, by incorporating an emotion estimation unit, the event's progress can be adjusted based on participants' emotions, providing a better user experience.

[0129] The AI ​​agent system can also be equipped with an emotional feedback unit. This unit collects and analyzes feedback based on participants' emotions. For example, it can analyze participants' facial expressions and voice data to estimate their emotions. Furthermore, it can adjust the content of the feedback based on the estimated emotions. Additionally, it can identify areas for improvement in the event based on the emotional data. Thus, by incorporating an emotional feedback unit, feedback can be collected based on participants' emotions, allowing for the identification of more appropriate areas for improvement.

[0130] The AI ​​agent system can also be equipped with an emotion analysis unit. This unit analyzes collected emotion data to identify areas for improvement in the event. For example, it can analyze participants' facial expressions and voice data to estimate their emotions. Furthermore, it can evaluate the event's progress based on the estimated emotions. It can also suggest improvements for future events based on the emotion data. Thus, by incorporating an emotion analysis unit, the event's progress can be evaluated based on participants' emotions, and areas for improvement for future events can be identified.

[0131] The AI ​​agent system can also be equipped with an emotion notification unit. This unit estimates participants' emotions in real time and notifies relevant parties. For example, it can analyze participants' facial expressions and voice data to estimate their emotions. It can also adjust the event's progress based on the estimated emotions. Furthermore, it can evaluate participants' satisfaction based on the emotion data and notify relevant parties. By incorporating an emotion notification unit, the event's progress can be adjusted based on participants' emotions, allowing relevant parties to respond quickly.

[0132] The AI ​​agent system can also be equipped with an emotion suggestion unit. This unit makes suggestions for future events based on participants' emotions. For example, it can analyze participants' facial expressions and voice data to estimate their emotions. Furthermore, based on the estimated emotions, it can propose plans for the next event. It can also identify and suggest improvements to the event based on the emotional data. Therefore, by incorporating an emotion suggestion unit, the system can propose plans for future events based on participants' emotions, providing a better user experience.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The detection unit detects technical problems in real time. For example, it uses sensors and log data to detect malfunctions in sound equipment or network connection failures. Step 2: The identification unit identifies the cause of the detected technical problem. For example, it identifies that the cause of the sound equipment malfunction is a microphone failure. Step 3: The resolution team implements solutions based on the identified cause. For example, they might take steps to repair a faulty microphone or instruct the microphone to be replaced. Step 4: The collection team gathers feedback from participants. For example, they collect feedback using questionnaires or evaluation forms. Step 5: The analysis unit analyzes the collected feedback. For example, it analyzes participant ratings and comments to identify areas for improvement in the event. Step 6: The proposal team proposes plans and improvements for the next event based on the analysis results. For example, they might propose changing the layout of the sound equipment.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0138] Each of the multiple elements described above, including the technical trouble detection unit, cause identification unit, resolution unit, instruction unit, feedback collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the technical trouble detection unit uses sensors and log data of the smart device 14 to detect malfunctions in sound equipment, network connection failures, etc. The cause identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to identify the cause of the detected technical trouble. The resolution unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to execute a solution based on the identified cause. The instruction unit is implemented by, for example, the control unit 46A of the smart device 14 to issue instructions summarizing the necessary actions if human intervention is required to execute the solution. The feedback collection unit is implemented by, for example, the control unit 46A of the smart device 14 to automatically collect feedback from participants. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to analyze the collected feedback. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously proposes and executes plans and improvements for the next event based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the technical trouble detection unit, cause identification unit, resolution unit, instruction unit, feedback collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the technical trouble detection unit uses sensors and log data from the smart glasses 214 to detect malfunctions in sound equipment, network connection failures, etc. The cause identification unit is implemented in the identification processing unit 290 of the data processing unit 12 to identify the cause of the detected technical trouble. The resolution unit is implemented in the identification processing unit 290 of the data processing unit 12 to implement a solution based on the identified cause. The instruction unit is implemented in the control unit 46A of the smart glasses 214 to issue instructions summarizing the necessary actions if human intervention is required to implement the solution. The feedback collection unit is implemented in the control unit 46A of the smart glasses 214 to automatically collect feedback from participants. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 to analyze the collected feedback. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously proposes and executes plans and improvements for the next event based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the technical trouble detection unit, cause identification unit, resolution unit, instruction unit, feedback collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the technical trouble detection unit uses sensors and log data of the headset terminal 314 to detect malfunctions in the sound equipment, network connection failures, etc. The cause identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to identify the cause of the detected technical trouble. The resolution unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to execute a solution based on the identified cause. The instruction unit is implemented by, for example, the control unit 46A of the headset terminal 314 to issue instructions summarizing the necessary actions if human intervention is required to execute the solution. The feedback collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 to automatically collect feedback from participants. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to analyze the collected feedback. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously proposes and executes plans and improvements for the next event based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the technical trouble detection unit, cause identification unit, resolution unit, instruction unit, feedback collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the technical trouble detection unit uses the robot 414's sensors and log data to detect malfunctions in the sound equipment, network connection failures, etc. The cause identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to identify the cause of the detected technical trouble. The resolution unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to execute a solution based on the identified cause. The instruction unit is implemented by, for example, the control unit 46A of the robot 414 to issue instructions summarizing the necessary actions if human intervention is required to execute the solution. The feedback collection unit is implemented by, for example, the control unit 46A of the robot 414 to automatically collect feedback from participants. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 to analyze the collected feedback. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously proposes and executes plans and improvements for the next event based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] 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.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) A detection unit that detects technical problems in real time, A unit for identifying the cause of the technical trouble detected by the detection unit, A solution unit that executes a solution based on the cause identified by the aforementioned identification unit, After the solution is implemented by the aforementioned solution unit, a collection unit collects feedback from participants, An analysis unit analyzes the feedback collected by the aforementioned collection unit, The system includes a proposal unit that proposes plans and improvements for the next event based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned solution unit is When human intervention is required, the system includes a control unit that consolidates the necessary actions and issues instructions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect feedback from participants using questionnaires and evaluation forms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the collected feedback to identify areas for improvement in the event. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we will propose changing the layout of the acoustic equipment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, By learning from past trouble data, we predict potential problems that might occur at future events and propose preventative measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is The system estimates the user's emotions and adjusts the sensitivity of technical trouble detection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is By referring to past trouble data, similar problems can be detected early. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is Dynamically change the detection algorithm according to the progress of the event. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is The detection range is adjusted according to the scale of the event and the number of participants. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is Improve detection accuracy by changing the type and placement of sensors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, It estimates the user's emotions and determines the priority of cause identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, Improve the accuracy of identifying past trouble causes by referring to past trouble cause data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, Optimize specific algorithms based on the time and location of the trouble. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, Apply different specific methods depending on the type of problem. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, Adjusting specific priorities while considering the scope of the problem's impact. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned solution unit is It estimates the user's emotions and adjusts the order in which solutions are executed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned solution unit is Select the optimal solution by referring to past solution data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned solution unit is Customize the solution method according to the type and impact of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned solution unit is It estimates the user's emotions and adjusts how solutions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned solution unit is Select a solution method based on where the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned solution unit is Prioritize solutions by considering the scope of the problem's impact. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is Optimize the data collection method by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is The data collection method is changed depending on the type and scale of the event. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is It estimates the user's emotions and determines the priority of feedback to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned collection unit is We will adjust the feedback collection method considering the participant's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is Apply different collection methods depending on the type of feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, Improve analysis accuracy by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, The analysis method is changed depending on the type and content of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, Optimize the analysis method based on the timing of feedback submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, Adjust the order of analysis results based on the relevance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, It estimates the user's emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, Improve proposal accuracy by referring to past proposal data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, The proposed methodology will be changed depending on the type and scale of the event. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, We propose preventative measures based on past trouble data. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned proposal section is, We will adjust the proposal based on the progress of the event. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that detects technical problems in real time, A unit for identifying the cause of the technical trouble detected by the detection unit, A solution unit that executes a solution based on the cause identified by the aforementioned identification unit, After the solution is implemented by the aforementioned solution unit, a collection unit collects feedback from participants, An analysis unit analyzes the feedback collected by the aforementioned collection unit, The system includes a proposal unit that proposes plans and improvements for the next event based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned solution unit is When human intervention is required, the system includes a control unit that consolidates the necessary actions and issues instructions. The system according to feature 1.

3. The aforementioned collection unit is We collect feedback from participants using questionnaires and evaluation forms. The system according to feature 1.

4. The aforementioned analysis unit, Analyze the collected feedback to identify areas for improvement in the event. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we will propose changing the layout of the acoustic equipment. The system according to feature 1.

6. The aforementioned proposal section is, By learning from past trouble data, we predict potential problems that might occur at future events and propose preventative measures. The system according to feature 1.

7. The detection unit is The system estimates the user's emotions and adjusts the sensitivity of technical trouble detection based on those estimated emotions. The system according to feature 1.

8. The detection unit is By referring to past trouble data, similar problems can be detected early. The system according to feature 1.

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