system

Abstract

The system according to this embodiment aims to efficiently collect and analyze audience reactions at large-scale event facilities and provide appropriate feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a supply unit, a reaction analysis unit, and an evaluation unit. The collection unit collects information obtained from cameras inside the dome. The analysis unit analyzes the information collected by the collection unit. The supply unit provides information to the audience based on the information analyzed by the analysis unit. The reaction analysis unit analyzes the audience's reactions. The evaluation unit evaluates the results obtained by the reaction analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there was a problem that it was difficult to efficiently collect and analyze the reactions of visitors in large-scale event facilities and provide appropriate feedback.

[0005] The system according to the embodiment aims to efficiently collect and analyze the reactions of visitors in large-scale event facilities and provide appropriate feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a supply unit, a reaction analysis unit, and an evaluation unit. The collection unit collects information obtained from cameras inside the dome. The analysis unit analyzes the information collected by the collection unit. The supply unit provides the information to the audience based on the information analyzed by the analysis unit. The reaction analysis unit analyzes the audience's reactions. The evaluation unit evaluates the results obtained by the reaction analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze audience reactions at large-scale event facilities and provide appropriate feedback. [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 applicable 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 receiving 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 receiving 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 event analysis system according to an embodiment of the present invention is a system that utilizes large-scale event facilities owned by the SB Group and provides new services using generative AI. This event analysis system inputs information obtained from cameras inside the dome into the generative AI, and spectators can listen to AI commentary tailored to their needs (level of baseball knowledge, favorite team, favorite player, etc.) via their smartphone app. For example, it provides basic rules and player introductions for beginners, and tactics and detailed player data for advanced users. The event analysis system also acquires spectator reactions to advertisements on large screens and on-site sampling using cameras inside the dome, and analyzes "crowd reactions" with the generative AI. This allows for measuring the effectiveness of awareness advertising and conducting product testing. For example, it can display advertisements for new products and analyze spectator expressions and behavior to evaluate the effectiveness of the advertisements. Furthermore, the event analysis system packages these services and provides them to other companies that own large-scale facilities. This allows other companies to introduce similar services and enhance the value of their facilities. For example, it is possible to provide similar services in other sports stadiums and concert halls. In this way, by utilizing the assets of the SB Group and using generative AI, new services can be provided and the value of facilities can be enhanced. This allows the event analysis system to provide personalized information to audiences and evaluate the effectiveness of advertising.

[0029] The event analysis system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a reaction analysis unit, and an evaluation unit. The collection unit collects information obtained from cameras inside the dome. For example, the collection unit collects the movements and facial expressions of spectators in real time using cameras inside the dome. The collection unit can also collect audio data. For example, the collection unit can collect the sounds of cheers and support from spectators and use them for analysis. Furthermore, the collection unit can also collect text data. For example, the collection unit can collect comments and feedback entered by spectators into a smartphone application. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected video data to recognize the facial expressions and movements of spectators. Furthermore, the analysis unit can analyze audio data to estimate the emotions and reactions of spectators. For example, the analysis unit can estimate the level of excitement and satisfaction of spectators from the audio data. Furthermore, the analysis unit can analyze text data to extract spectators' opinions and impressions. For example, the analysis unit can extract spectators' evaluations and requests from the text data and use them to improve services. The service provider provides information to the audience based on the analysis performed by the analysis provider. For example, the service provider can provide AI commentary to the audience's smartphone app. The service provider can also display advertisements and information on large screens. For example, the service provider can display advertisements tailored to the audience's needs, enabling effective marketing. Furthermore, the service provider can provide real-time feedback to the audience. For example, the service provider can provide appropriate information and advice based on the audience's reactions. The reaction analysis provider analyzes the audience's reactions to advertisements on large screens and on-site sampling. For example, the reaction analysis provider can analyze the audience's facial expressions and movements to evaluate the effectiveness of the advertisements. The reaction analysis provider can also analyze audience behavior data to evaluate the results of product tests. For example, the reaction analysis provider can analyze audience behavior patterns to evaluate the acceptance of new products. Furthermore, the reaction analysis provider can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. For example, the reaction analysis provider can analyze the audience's cheers and support to evaluate the effectiveness of the advertisements.The evaluation unit evaluates the results obtained by the response analysis unit. For example, the evaluation unit evaluates the effectiveness of awareness advertising based on the analyzed response data. The evaluation unit can also evaluate the results of product tests and identify areas for improvement in the product. For example, the evaluation unit can identify areas for improvement in the product based on audience response data and reflect them in the next test. Furthermore, the evaluation unit can also identify areas for improvement in the service based on audience feedback. For example, the evaluation unit can identify areas for improvement in the service based on audience feedback and reflect them in the next event. As a result, the event analysis system according to this embodiment can provide personalized information to the audience and evaluate the effectiveness of advertising.

[0030] The data collection unit gathers information obtained from cameras inside the dome. Specifically, it uses multiple high-resolution cameras installed inside the dome to collect real-time data on the movements and expressions of spectators. These cameras are positioned to cover a wide area, allowing for detailed capture of subtle changes in spectators' expressions and movements. The data collection unit can also collect audio data. For example, it can use high-sensitivity microphones installed inside the dome to collect the sounds of cheers and support from spectators, and this audio data can be used for analysis. Furthermore, the data collection unit can also collect text data. For example, it can collect comments and feedback entered by spectators into a smartphone app in real time, and this text data can be used for analysis. The data collection unit has the infrastructure to centrally manage this data and provide it quickly to the analysis and provision units. Data collection is performed in real time and immediately transmitted to a central database. This allows the data collection unit to grasp spectator movements and reactions in real time and respond quickly. In addition, the data collection unit can adjust the frequency and accuracy of data collection to respond flexibly to specific situations and conditions. For example, by increasing the collection frequency for specific events or actions, more detailed data can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes the collected video data and uses image recognition technology to recognize the facial expressions and movements of the audience. For example, it applies a face recognition algorithm using deep learning to estimate emotions from the audience's facial expressions. It can also use a motion recognition algorithm to analyze the audience's movements and gestures and evaluate their level of excitement and interest. Furthermore, the analysis unit can analyze audio data to estimate the audience's emotions and reactions. For example, it uses speech recognition technology to analyze the cheers and shouts of the audience and estimate their level of excitement and satisfaction from changes in volume and tone. The analysis of audio data also includes using natural language processing technology to analyze the content of what the audience says and extract emotions and opinions. Furthermore, the analysis unit can analyze text data to extract the audience's opinions and impressions. For example, it uses natural language processing technology to analyze comments and feedback entered by the audience into a smartphone app and classify them into positive and negative opinions. This allows for the extraction of audience evaluations and requests, which can be used to improve services. The analysis unit can comprehensively analyze this data to understand the overall audience reaction and emotions. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The service provider provides information to the audience based on the analysis performed by the analytics department. Specifically, it provides AI commentary to audience members via their smartphone apps. The AI ​​commentary is customized to the audience's interests and is updated in real time. For example, if an audience member shows interest in a particular player or play, detailed information and commentary on that player or play can be provided. The service provider can also display advertisements and information on large screens. For example, it can display advertisements tailored to the audience's needs, enabling effective marketing. The advertisements are customized to the audience's interests and are updated in real time. Furthermore, the service provider can provide real-time feedback to the audience. For example, it can provide appropriate information and advice based on audience reactions. The service provider has the infrastructure to deliver this information quickly and effectively, and can distribute information in real time to audience members' smartphone apps and large screens. This allows the service provider to provide personalized information to the audience and evaluate the effectiveness of advertisements. Furthermore, the service provider can collect audience feedback and continuously improve the accuracy and effectiveness of the information provided. For example, based on audience feedback, it can review the content of the AI ​​commentary and the way advertisements are displayed to provide more effective information. This allows the event organizers to provide information to the audience quickly and reliably, thereby improving event satisfaction.

[0033] The reaction analysis unit analyzes audience reactions to advertisements on large screens and on-site sampling. Specifically, it uses image recognition technology to analyze audience facial expressions and movements to evaluate the effectiveness of advertisements. For example, it applies a facial recognition algorithm using deep learning to estimate emotions towards advertisements from audience facial expressions. It can also use motion recognition algorithms to analyze audience movements and gestures to evaluate their level of interest and reaction to advertisements. Furthermore, the reaction analysis unit can analyze audience behavior data to evaluate the results of product tests. For example, it can analyze audience behavior patterns to evaluate the acceptance of new products. Audience behavior data includes travel routes, dwell time, and purchase history, and by comprehensively analyzing this data, it is possible to identify the acceptance of products and areas for improvement. In addition, the reaction analysis unit can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. For example, it can analyze audience cheers and cheers to evaluate their level of excitement and satisfaction with advertisements. Audio data analysis also includes using natural language processing technology to analyze the content of audience statements and extract emotions and opinions. This allows the reaction analysis unit to analyze audience reactions from multiple angles and comprehensively evaluate the effectiveness of advertisements and product tests.

[0034] The evaluation department evaluates the results obtained by the response analysis department. Specifically, it evaluates the effectiveness of awareness advertisements based on the analyzed response data. For example, by analyzing audience facial expressions, movements, and audio data, it is possible to quantitatively evaluate the effectiveness of advertisements by evaluating emotions and reactions to advertisements. The evaluation department can also evaluate the results of product tests and identify areas for improvement. For example, based on audience behavior data, it can identify the acceptance of new products and areas for improvement, and reflect these in the next test. Furthermore, the evaluation department can identify areas for improvement of services based on audience feedback. For example, based on audience feedback, it can identify areas for improvement of services and reflect these in the next event. The evaluation department has the infrastructure to comprehensively evaluate these data and evaluate the effectiveness of advertisements and products. Based on the evaluation results, the evaluation department can identify areas for improvement of advertisements and products and reflect them in the next event or product test. This allows the evaluation department to enhance the effectiveness of advertisements and products and improve audience satisfaction. Furthermore, based on the evaluation results, the evaluation department can analyze long-term trends and patterns and formulate future countermeasures. This allows the evaluation department to continuously improve the effectiveness of advertisements and products, thereby enhancing the overall reliability and effectiveness of the system.

[0035] The data collection unit can collect information obtained from cameras inside the dome. For example, the data collection unit can use cameras inside the dome to collect the movements and facial expressions of spectators in real time. The data collection unit can capture the movements and facial expressions of spectators with a high-resolution camera and save them as video data. The data collection unit can also collect audio data. The data collection unit can collect the cheers and cheers of spectators with a high-sensitivity microphone and save them as audio data. Furthermore, the data collection unit can also collect text data. The data collection unit can collect and save comments and feedback entered by spectators into a smartphone app as text data. In this way, the data collection unit can obtain real-time data by collecting information obtained from cameras inside the dome. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired by cameras into a generating AI and have the generating AI perform an analysis of the movements and facial expressions of spectators from the video data.

[0036] The analysis unit can analyze collected information and generate AI commentary tailored to the audience's needs. For example, the analysis unit can analyze collected video data to recognize the audience's facial expressions and movements. By analyzing the audience's facial expressions and movements, the analysis unit can estimate their interests and concerns. The analysis unit can also analyze audio data to estimate the audience's emotions and reactions. The analysis unit can analyze the audience's cheers and cheers to estimate their level of excitement and satisfaction. Furthermore, the analysis unit can analyze text data to extract the audience's opinions and impressions. The analysis unit can analyze comments and feedback entered by the audience into a smartphone app to extract their evaluations and requests. As a result, the analysis unit can generate AI commentary tailored to the audience's needs, enabling personalized information provision. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input collected video data into a generating AI and have the generating AI perform the analysis of the audience's facial expressions and movements.

[0037] The service provider can provide AI commentary to the audience's smartphone app. For example, the service provider can deliver AI commentary to the audience's smartphone app in real time. The service provider can generate AI commentary tailored to the audience's needs and deliver it to the smartphone app. The service provider can also display advertisements and information on large screens. The service provider can generate advertisements tailored to the audience's needs and display them on large screens. Furthermore, the service provider can provide real-time feedback to the audience. The service provider can provide appropriate information and advice based on the audience's reactions. In this way, the service provider can provide personalized information in real time by providing AI commentary to the audience's smartphone app. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can use generative AI to generate AI commentary tailored to the audience's needs and deliver it to the smartphone app.

[0038] The reaction analysis unit can analyze audience reactions to advertisements on large screens and on-site sampling. For example, the reaction analysis unit can analyze audience facial expressions and movements to evaluate the effectiveness of the advertisement. The reaction analysis unit can quantitatively evaluate the effectiveness of the advertisement by analyzing audience facial expressions and movements. The reaction analysis unit can also analyze audience behavior data to evaluate the results of product tests. The reaction analysis unit can analyze audience behavior patterns to evaluate the acceptance of new products. Furthermore, the reaction analysis unit can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. The reaction analysis unit can analyze audience cheers and cheers to evaluate the effectiveness of the advertisement. In this way, the reaction analysis unit can evaluate the effectiveness of advertisements and sampling by analyzing audience reactions. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audience facial expression and movement data into a generative AI and have the generative AI perform an analysis to evaluate the effectiveness of the advertisement.

[0039] The evaluation unit can evaluate the effectiveness of awareness advertising based on the analyzed response data. For example, the evaluation unit can quantitatively evaluate the effectiveness of advertising based on the analyzed response data. The evaluation unit can quantify and evaluate the effectiveness of advertising based on audience response data. The evaluation unit can also evaluate the results of product tests and identify areas for product improvement. The evaluation unit can identify areas for product improvement based on audience response data and reflect them in the next test. Furthermore, the evaluation unit can identify areas for service improvement based on audience feedback. The evaluation unit can identify areas for service improvement based on audience feedback and reflect them in the next event. In this way, the evaluation unit can improve advertising strategies by evaluating the effectiveness of awareness advertising. Some or all of the above processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input the analyzed response data into generative AI and have the generative AI perform an analysis to evaluate the effectiveness of advertising.

[0040] The package unit can integrate the functions of each unit and provide them to other large-scale facility-owning companies. For example, the package unit integrates and packages the functions of the collection unit, analysis unit, provision unit, reaction analysis unit, and evaluation unit. The package unit can provide these functions as a single system. Furthermore, the package unit can provide packages to other large-scale facility-owning companies. The package unit can provide other large-scale facility-owning companies with packages that include the functions of the collection unit, analysis unit, provision unit, reaction analysis unit, and evaluation unit. This allows the package unit to enable other large-scale facility-owning companies to introduce similar services and enhance the value of their facilities. Some or all of the processing described above in the package unit may be performed using, for example, generative AI, or not using generative AI. For example, the package unit can use generative AI to optimize the integration of the functions of each unit and provide it to other large-scale facility-owning companies.

[0041] The data collection unit can simultaneously collect information from different areas within the dome and analyze the characteristics of each area. For example, the data collection unit can simultaneously collect information from different areas using multiple cameras within the dome. The data collection unit can collect the movements and facial expressions of spectators in each area in real time and analyze the differences in reactions between areas. The data collection unit can also collect the behavioral patterns of spectators in each area and clarify the characteristics of each area. The data collection unit can collect behavioral data of spectators in each area and analyze the characteristics of each area. Furthermore, the data collection unit can collect audio data from each area and analyze the differences in cheers and support between areas. The data collection unit can collect audio data from each area and analyze the differences in reactions between areas. In this way, the data collection unit can clarify the differences in reactions between areas by analyzing the characteristics of each area. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input data on the movements and facial expressions of spectators in each area into a generative AI and analyze the characteristics of each area.

[0042] The data collection unit can track the movements and behavioral patterns of spectators in real time during collection and collect reactions to specific events. For example, the data collection unit can track the movements and behavioral patterns of spectators in real time. The data collection unit can track the movements and behavioral patterns of spectators in real time and collect reactions to specific events. The data collection unit can also track how spectators react to specific players in real time and store this data. Furthermore, the data collection unit can collect how spectators react to specific plays in real time. The data collection unit can collect how spectators react to specific plays in real time and store this data. This allows the data collection unit to collect detailed reactions to specific events by tracking the movements and behavioral patterns of spectators in real time. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input data on spectator movements and behavioral patterns into generative AI and analyze reactions to specific events.

[0043] The data collection unit can also collect information from surrounding areas outside the dome and analyze the behavior of spectators before and after the event. For example, the data collection unit can collect traffic conditions outside the dome and analyze the movement patterns of spectators. The data collection unit can collect traffic conditions outside the dome in real time and analyze the movement patterns of spectators. The data collection unit can also collect information on the usage of restaurants outside the dome and analyze the behavior of spectators. The data collection unit can collect information on the usage of restaurants outside the dome and analyze the behavior patterns of spectators. Furthermore, the data collection unit can collect information on the usage of parking lots outside the dome and analyze the behavior of spectators. The data collection unit can collect information on the usage of parking lots outside the dome and analyze the behavior patterns of spectators. In this way, by collecting information from outside the dome, the data collection unit can analyze the behavior of spectators before and after the event in detail. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on traffic conditions and restaurant usage outside the dome into a generative AI and analyze the behavior patterns of spectators.

[0044] The data collection unit can analyze audience social media activity and collect relevant information during the collection process. For example, the data collection unit can collect content posted by audience members on social media and analyze their reactions to the event. The data collection unit can also collect information shared by audience members on social media and analyze the impact of the event. Furthermore, the data collection unit can collect hashtags used by audience members on social media and analyze event trends. This allows the data collection unit to collect audience reactions more broadly by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input audience social media posting data into generative AI and analyze reactions to the event.

[0045] The analysis unit can improve the accuracy of its analysis by referring to past audience behavior data during the analysis. For example, the analysis unit can refer to past audience reaction data to analyze the current reaction. The analysis unit can also refer to past audience reaction data to analyze the current reaction. Furthermore, the analysis unit can refer to past audience behavior patterns to analyze the current behavior. The analysis unit can refer to past audience behavior patterns to analyze the current behavior. In addition, the analysis unit can refer to past audience emotion data to analyze the current emotion. The analysis unit can refer to past audience emotion data to analyze the current emotion. As a result, the analysis unit improves the accuracy of its current analysis by referring to past behavior data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past audience behavior data into a generative AI and analyze the current reaction.

[0046] The analysis unit can integrate different data sources (audio, video, text) during analysis. For example, the analysis unit can integrate audio data and video data to analyze audience reactions. The analysis unit can integrate audio data and video data to analyze audience reactions. Furthermore, the analysis unit can integrate video data and text data to analyze audience reactions. The analysis unit can integrate video data and text data to analyze audience reactions. In addition, the analysis unit can integrate audio data and text data to analyze audience reactions. The analysis unit can integrate audio data and text data to analyze audience reactions. As a result, the analysis unit can improve the accuracy of its analysis by integrating different data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data, video data, and text data into a generative AI to analyze audience reactions.

[0047] The analysis unit can perform analysis while considering the geographical location information of the audience. For example, the analysis unit can analyze reactions in each area while considering the seating positions of the audience. The analysis unit can analyze reactions in each area while considering the seating positions of the audience. Furthermore, the analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. The analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. In addition, the analysis unit can analyze the overall reaction while considering the geographical location information of the audience. The analysis unit can analyze the overall reaction while considering the geographical location information of the audience. As a result, the analysis unit can perform detailed analysis of reactions in each area by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical location information of the audience into a generative AI and analyze reactions in each area.

[0048] The analysis unit can improve the accuracy of its analysis by referring to the audience's social media activity during the analysis. For example, the analysis unit can refer to the content that the audience has posted on social media and analyze the reactions. The analysis unit can also refer to the information that the audience has shared on social media and analyze the reactions. Furthermore, the analysis unit can refer to the hashtags that the audience has used on social media and analyze the reactions. In this way, the analysis unit can improve the accuracy of its analysis by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the audience's social media posting data into a generative AI and analyze the reactions.

[0049] The service provider can provide the most relevant information by referring to the audience's past viewing history at the time of delivery. For example, the service provider can provide relevant information based on what the audience has watched in the past. The service provider can provide relevant information based on what the audience has watched in the past. The service provider can also provide information that might be of interest to the audience based on their past viewing history. The service provider can also analyze the audience's past viewing history and provide the most suitable information. The service provider can analyze the audience's past viewing history and provide the most suitable information. In this way, the service provider can provide the most relevant information to the audience by referring to their past viewing history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the audience's past viewing history into a generative AI and provide the most relevant information.

[0050] The information provider can adjust the information format according to the audience's device characteristics at the time of delivery. For example, if the audience is using a smartphone, the information provider can provide information that matches the screen size. The information provider can also provide information optimized for larger screens if the audience is using a tablet. Furthermore, if the audience is using a smartwatch, the information provider can provide concise and highly visible information. This enables the information provider to provide information according to device characteristics. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the audience's device characteristics into a generative AI and adjust the information format.

[0051] The information provider can provide optimal information by considering the geographical location information of the audience at the time of provision. For example, the information provider can provide area-specific information by considering the seating location of the audience. The information provider can provide area-specific information by considering the seating location of the audience. Furthermore, the information provider can provide information for specific areas by considering the movement patterns of the audience. The information provider can provide information for specific areas by considering the geographical location information of the audience. In addition, the information provider can provide overall information by considering the geographical location information of the audience. The information provider can provide overall information by considering the geographical location information of the audience. This enables the information provider to provide area-specific information by considering geographical location information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the geographical location information of the audience into a generative AI and provide optimal information.

[0052] The service provider can analyze the audience's social media activity and provide relevant information at the time of service provision. For example, the service provider can analyze the content posted by the audience on social media and provide relevant information. The service provider can analyze the content posted by the audience on social media and provide relevant information. Furthermore, the service provider can analyze the information shared by the audience on social media and provide relevant information. In addition, the service provider can analyze the hashtags used by the audience on social media and provide relevant information. The service provider can analyze the hashtags used by the audience on social media and provide relevant information. In this way, the service provider can provide relevant information to the audience by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the audience's social media posting data into a generative AI and provide relevant information.

[0053] The reaction analysis unit can improve the accuracy of its analysis by referring to the audience's past reaction data during reaction analysis. For example, the reaction analysis unit can refer to the audience's past reaction data to analyze the current reaction. The reaction analysis unit can also refer to the audience's past behavior patterns to analyze the current behavior. The reaction analysis unit can also refer to the audience's past behavior patterns to analyze the current behavior. Furthermore, the reaction analysis unit can refer to the audience's past emotional data to analyze the current emotion. The reaction analysis unit can refer to the audience's past emotional data to analyze the current emotion. As a result, the reaction analysis unit improves the accuracy of its current analysis by referring to past reaction data. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reaction analysis unit can input the audience's past reaction data into a generative AI to analyze the current reaction.

[0054] The reaction analysis unit can integrate different data sources (audio, video, text) during reaction analysis. For example, the reaction analysis unit can integrate audio data and video data to analyze audience reactions. The reaction analysis unit can integrate audio data and video data to analyze audience reactions. Furthermore, the reaction analysis unit can integrate video data and text data to analyze audience reactions. The reaction analysis unit can integrate audio data and text data to analyze audience reactions. In this way, the reaction analysis unit can improve the accuracy of its analysis by integrating different data sources. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audio data, video data, and text data into a generative AI to analyze audience reactions.

[0055] The reaction analysis unit can perform analysis while considering the geographical location information of the audience. For example, the reaction analysis unit can analyze reactions in each area while considering the seating positions of the audience. The reaction analysis unit can analyze reactions in each area while considering the seating positions of the audience. Furthermore, the reaction analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. The reaction analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. In addition, the reaction analysis unit can analyze the overall reaction while considering the geographical location information of the audience. The reaction analysis unit can analyze the overall reaction while considering the geographical location information of the audience. As a result, the reaction analysis unit can perform detailed analysis of reactions in each area by considering geographical location information. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reaction analysis unit can input the geographical location information of the audience into a generative AI and analyze reactions in each area.

[0056] The reaction analysis unit can improve the accuracy of its analysis by referring to the audience's social media activity during reaction analysis. For example, the reaction analysis unit can refer to content posted by the audience on social media and analyze the reactions. The reaction analysis unit can also refer to information shared by the audience on social media and analyze the reactions. Furthermore, the reaction analysis unit can refer to hashtags used by the audience on social media and analyze the reactions. In this way, the reaction analysis unit can improve the accuracy of its analysis by referring to social media activity. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input the audience's social media posting data into a generative AI and analyze the reactions.

[0057] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit can optimize the current evaluation by referring to past evaluation data. The evaluation unit can optimize the current evaluation by referring to past evaluation data. The evaluation unit can also optimize its evaluation algorithm by analyzing past evaluation data. Furthermore, the evaluation unit can optimize its evaluation criteria based on past evaluation data. As a result, the evaluation unit improves the accuracy of its current evaluation by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input past evaluation data into a generative AI to optimize the current evaluation.

[0058] The evaluation unit can integrate different data sources (audio, video, text) during evaluation. For example, the evaluation unit can integrate audio data and video data and perform an evaluation. The evaluation unit can integrate audio data and video data and perform an evaluation. Furthermore, the evaluation unit can integrate video data and text data and perform an evaluation. The evaluation unit can integrate audio data and text data and perform an evaluation. In this way, the evaluation unit can improve the accuracy of its evaluation by integrating different data sources. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input audio data, video data, and text data into a generative AI and perform an evaluation.

[0059] The evaluation unit can perform evaluations while considering the geographical location information of the audience. For example, the evaluation unit can perform area-by-area evaluations while considering the seating positions of the audience. The evaluation unit can perform area-by-area evaluations while considering the seating positions of the audience. Furthermore, the evaluation unit can perform evaluations in specific areas while considering the movement patterns of the audience. The evaluation unit can perform overall evaluations while considering the geographical location information of the audience. The evaluation unit can perform overall evaluations while considering the geographical location information of the audience. This enables the evaluation unit to perform area-by-area evaluations by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the geographical location information of the audience into a generative AI and perform area-by-area evaluations.

[0060] The evaluation unit can improve the accuracy of its evaluation by referring to the audience's social media activity during the evaluation process. For example, the evaluation unit can refer to the content that the audience has posted on social media and perform an evaluation. The evaluation unit can also refer to the information that the audience has shared on social media and perform an evaluation. Furthermore, the evaluation unit can refer to the hashtags that the audience has used on social media and perform an evaluation. The evaluation unit can refer to the hashtags that the audience has used on social media and perform an evaluation. As a result, the evaluation unit can improve the accuracy of its evaluation by referring to social media activity. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the audience's social media posting data into a generative AI and perform an evaluation.

[0061] The packaging unit can generate an optimal package by referring to past data of each part during the packaging process. For example, the packaging unit can generate an optimal package by referring to past data of each part. Furthermore, the packaging unit can optimize the package content by analyzing past data of each part. In addition, the packaging unit can optimize the package delivery method based on past data of each part. This allows the packaging unit to generate an optimal package by referring to past data. Some or all of the above processing in the packaging unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the packaging unit can input past data of each part into a generation AI to generate an optimal package.

[0062] The package unit integrates the functions of each unit and can adjust the method of providing packages considering the geographical location information of the audience. For example, the package unit can provide packages for each area, taking into account the seating positions of the audience. The package unit can provide packages for each area, taking into account the seating positions of the audience. Furthermore, the package unit can provide packages for specific areas, taking into account the movement patterns of the audience. The package unit can provide packages for specific areas, taking into account the geographical location information of the audience. The package unit can provide packages for the entirety, taking into account the geographical location information of the audience. This enables the package unit to provide packages for each area by considering geographical location information. Some or all of the above processing in the package unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the package unit can input the geographical location information of the audience into a generative AI and provide the optimal package.

[0063] The package unit can integrate the functions of each unit and generate the optimal package by referring to the audience's social media activity. For example, the package unit can refer to the content posted by the audience on social media and generate the optimal package. The package unit can also refer to the information shared by the audience on social media and optimize the package content. Furthermore, the package unit can refer to the hashtags used by the audience on social media and optimize the way the package is delivered. The package unit can refer to the hashtags used by the audience on social media and optimize the way the package is delivered. In this way, the package unit can generate the optimal package by referring to social media activity. Some or all of the above processing in the package unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the package unit can input the audience's social media posting data into a generation AI and generate the optimal package.

[0064] The service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies at the time of delivery. For example, the service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies. The service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies. The service provider can also optimize the service delivery method by analyzing past data of other large-scale facility-owning companies. The service provider can optimize the service delivery method by analyzing past data of other large-scale facility-owning companies. Furthermore, the service provider can optimize the service delivery method based on past data of other large-scale facility-owning companies. The service provider can optimize the service delivery method based on past data of other large-scale facility-owning companies. In this way, the service provider can select the optimal service delivery method for other large-scale facility-owning companies by referring to past data. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input past data of other large-scale facility-owning companies into a generation AI and select the optimal service delivery method.

[0065] The service provider can select the optimal service delivery method at the time of delivery, taking into account the geographical location information of other large-scale facility-owning companies. For example, the service provider can select a service delivery method for each area, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select a service delivery method for each area, taking into account the geographical location information of other large-scale facility-owning companies. Furthermore, the service provider can also select a service delivery method for a specific area, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select a service delivery method for a specific area, taking into account the geographical location information of other large-scale facility-owning companies. In addition, the service provider can select an overall service delivery method, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select an overall service delivery method, taking into account the geographical location information of other large-scale facility-owning companies. This allows the service provider to select the optimal service delivery method for other large-scale facility-owning companies by taking into account geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the geographical location information of other large-scale facility-owning companies into a generation AI and select the optimal service delivery method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Event analysis systems can refer to past audience behavior data and provide content tailored to their preferences. For example, audience members who have watched a particular player's games frequently in the past can be shown the player's latest plays and interviews. Similarly, audience members who have watched a particular match frequently can be shown highlights and related data from that match. Furthermore, audience members who have attended a particular event in the past can be shown content related to that event and information about upcoming events. This allows for more personalized content delivery by leveraging past audience behavior data.

[0068] Event analysis systems can provide content tailored to regional characteristics by considering the geographical location of the audience. For example, they can provide audiences in a specific region with information about players and teams associated with that region. They can also provide information about events and matches held in that region. Furthermore, they can provide audiences in a specific region with information about sponsors and advertisers in that region. This enables the provision of content tailored to regional characteristics, thereby attracting the interest of the audience.

[0069] Event analysis systems can analyze audience social media activity and provide content based on their interests. For example, if an audience member frequently posts about a particular player or team on social media, the system can provide content related to that player or team. Similarly, if an audience member frequently shares information about a specific event or match on social media, the system can provide content related to that event or match. Furthermore, it can analyze the hashtags used by audience members on social media and provide content related to those hashtags. This allows for more personalized content delivery by leveraging audience social media activity.

[0070] Event analysis systems can refer to audiences' past viewing history and display advertisements tailored to their interests. For example, audiences who have shown interest in a particular brand or product in the past can be shown advertisements for the latest information and campaigns of that brand or product. Similarly, audiences who have frequently purchased products in a specific category can be shown advertisements related to products in that category. Furthermore, audiences who have attended a particular event in the past can be shown advertisements from sponsors associated with that event. This allows for more targeted advertising by leveraging audiences' past viewing history.

[0071] The event analysis system can adjust the ad format according to the audience's device characteristics. For example, if the audience is using a smartphone, the ad can be displayed to match the screen size. If the audience is using a tablet, an ad optimized for the larger screen can be displayed. Furthermore, if the audience is using a smartwatch, a concise and highly visible ad can be displayed. This enables ad display tailored to device characteristics, thereby increasing the effectiveness of the ads.

[0072] Event analysis systems can display advertisements tailored to regional characteristics by considering the geographical location of the audience. For example, audiences in a specific region can see advertisements from sponsors and advertisers related to that region. Furthermore, audiences in a specific region can see advertisements for events and campaigns held in that region. In addition, advertisements tailored to the culture and customs of that region can be displayed. This enables the display of advertisements that are specific to each region, thereby increasing the effectiveness of advertising.

[0073] Event analysis systems can analyze audience social media activity and display advertisements based on their interests. For example, if an audience frequently posts about a particular brand or product on social media, advertisements for that brand or product can be displayed. Similarly, if an audience frequently shares products from a specific category on social media, advertisements related to products in that category can be displayed. Furthermore, the system can analyze hashtags used by the audience on social media and display advertisements related to those hashtags. This allows for more targeted advertising by leveraging audience social media activity.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The collection unit gathers information obtained from cameras inside the dome. For example, the collection unit can collect the movements and facial expressions of the audience in real time, and can also collect audio and text data. Specifically, it collects the cheers and shouts of the audience, as well as comments and feedback entered into smartphone apps. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected video data to recognize the audience's facial expressions and movements, and estimates the audience's emotions and reactions from the audio data. It also analyzes the text data to extract the audience's opinions and impressions, which are used to improve the service. Step 3: The service provider provides information to the audience based on the analysis performed by the analysis unit. For example, they might provide AI commentary on the audience's smartphone app or display advertisements and information on a large screen. They can also provide real-time feedback based on the audience's reactions. Step 4: The reaction analysis unit analyzes audience reactions to advertisements on large screens and on-site sampling. For example, it analyzes audience facial expressions and movements to evaluate the effectiveness of advertisements, and analyzes audience behavior data to evaluate the results of product tests. It also analyzes audience audio data to evaluate the effectiveness of advertisements and sampling. Step 5: The evaluation unit evaluates the results obtained by the response analysis unit. For example, it evaluates the effectiveness of awareness advertising based on the analyzed response data and evaluates the results of product tests to identify areas for product improvement. It also identifies areas for service improvement based on audience feedback and incorporates them into the next event.

[0076] (Example of form 2) The event analysis system according to an embodiment of the present invention is a system that utilizes large-scale event facilities owned by the SB Group and provides new services using generative AI. This event analysis system inputs information obtained from cameras inside the dome into the generative AI, and spectators can listen to AI commentary tailored to their needs (level of baseball knowledge, favorite team, favorite player, etc.) via their smartphone app. For example, it provides basic rules and player introductions for beginners, and tactics and detailed player data for advanced users. The event analysis system also acquires spectator reactions to advertisements on large screens and on-site sampling using cameras inside the dome, and analyzes "crowd reactions" with the generative AI. This allows for measuring the effectiveness of awareness advertising and conducting product testing. For example, it can display advertisements for new products and analyze spectator expressions and behavior to evaluate the effectiveness of the advertisements. Furthermore, the event analysis system packages these services and provides them to other companies that own large-scale facilities. This allows other companies to introduce similar services and enhance the value of their facilities. For example, it is possible to provide similar services in other sports stadiums and concert halls. In this way, by utilizing the assets of the SB Group and using generative AI, new services can be provided and the value of facilities can be enhanced. This allows the event analysis system to provide personalized information to audiences and evaluate the effectiveness of advertising.

[0077] The event analysis system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a reaction analysis unit, and an evaluation unit. The collection unit collects information obtained from cameras inside the dome. For example, the collection unit collects the movements and facial expressions of spectators in real time using cameras inside the dome. The collection unit can also collect audio data. For example, the collection unit can collect the sounds of cheers and support from spectators and use them for analysis. Furthermore, the collection unit can also collect text data. For example, the collection unit can collect comments and feedback entered by spectators into a smartphone application. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected video data to recognize the facial expressions and movements of spectators. Furthermore, the analysis unit can analyze audio data to estimate the emotions and reactions of spectators. For example, the analysis unit can estimate the level of excitement and satisfaction of spectators from the audio data. Furthermore, the analysis unit can analyze text data to extract spectators' opinions and impressions. For example, the analysis unit can extract spectators' evaluations and requests from the text data and use them to improve services. The service provider provides information to the audience based on the analysis performed by the analysis provider. For example, the service provider can provide AI commentary to the audience's smartphone app. The service provider can also display advertisements and information on large screens. For example, the service provider can display advertisements tailored to the audience's needs, enabling effective marketing. Furthermore, the service provider can provide real-time feedback to the audience. For example, the service provider can provide appropriate information and advice based on the audience's reactions. The reaction analysis provider analyzes the audience's reactions to advertisements on large screens and on-site sampling. For example, the reaction analysis provider can analyze the audience's facial expressions and movements to evaluate the effectiveness of the advertisements. The reaction analysis provider can also analyze audience behavior data to evaluate the results of product tests. For example, the reaction analysis provider can analyze audience behavior patterns to evaluate the acceptance of new products. Furthermore, the reaction analysis provider can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. For example, the reaction analysis provider can analyze the audience's cheers and support to evaluate the effectiveness of the advertisements.The evaluation unit evaluates the results obtained by the response analysis unit. For example, the evaluation unit evaluates the effectiveness of awareness advertising based on the analyzed response data. The evaluation unit can also evaluate the results of product tests and identify areas for improvement in the product. For example, the evaluation unit can identify areas for improvement in the product based on audience response data and reflect them in the next test. Furthermore, the evaluation unit can also identify areas for improvement in the service based on audience feedback. For example, the evaluation unit can identify areas for improvement in the service based on audience feedback and reflect them in the next event. As a result, the event analysis system according to this embodiment can provide personalized information to the audience and evaluate the effectiveness of advertising.

[0078] The data collection unit gathers information obtained from cameras inside the dome. Specifically, it uses multiple high-resolution cameras installed inside the dome to collect real-time data on the movements and expressions of spectators. These cameras are positioned to cover a wide area, allowing for detailed capture of subtle changes in spectators' expressions and movements. The data collection unit can also collect audio data. For example, it can use high-sensitivity microphones installed inside the dome to collect the sounds of cheers and support from spectators, and this audio data can be used for analysis. Furthermore, the data collection unit can also collect text data. For example, it can collect comments and feedback entered by spectators into a smartphone app in real time, and this text data can be used for analysis. The data collection unit has the infrastructure to centrally manage this data and provide it quickly to the analysis and provision units. Data collection is performed in real time and immediately transmitted to a central database. This allows the data collection unit to grasp spectator movements and reactions in real time and respond quickly. In addition, the data collection unit can adjust the frequency and accuracy of data collection to respond flexibly to specific situations and conditions. For example, by increasing the collection frequency for specific events or actions, more detailed data can be obtained. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0079] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes the collected video data and uses image recognition technology to recognize the facial expressions and movements of the audience. For example, it applies a face recognition algorithm using deep learning to estimate emotions from the audience's facial expressions. It can also use a motion recognition algorithm to analyze the audience's movements and gestures and evaluate their level of excitement and interest. Furthermore, the analysis unit can analyze audio data to estimate the audience's emotions and reactions. For example, it uses speech recognition technology to analyze the cheers and shouts of the audience and estimate their level of excitement and satisfaction from changes in volume and tone. The analysis of audio data also includes using natural language processing technology to analyze the content of what the audience says and extract emotions and opinions. Furthermore, the analysis unit can analyze text data to extract the audience's opinions and impressions. For example, it uses natural language processing technology to analyze comments and feedback entered by the audience into a smartphone app and classify them into positive and negative opinions. This allows for the extraction of audience evaluations and requests, which can be used to improve services. The analysis unit can comprehensively analyze this data to understand the overall audience reaction and emotions. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0080] The service provider provides information to the audience based on the analysis performed by the analytics department. Specifically, it provides AI commentary to audience members via their smartphone apps. The AI ​​commentary is customized to the audience's interests and is updated in real time. For example, if an audience member shows interest in a particular player or play, detailed information and commentary on that player or play can be provided. The service provider can also display advertisements and information on large screens. For example, it can display advertisements tailored to the audience's needs, enabling effective marketing. The advertisements are customized to the audience's interests and are updated in real time. Furthermore, the service provider can provide real-time feedback to the audience. For example, it can provide appropriate information and advice based on audience reactions. The service provider has the infrastructure to deliver this information quickly and effectively, and can distribute information in real time to audience members' smartphone apps and large screens. This allows the service provider to provide personalized information to the audience and evaluate the effectiveness of advertisements. Furthermore, the service provider can collect audience feedback and continuously improve the accuracy and effectiveness of the information provided. For example, based on audience feedback, it can review the content of the AI ​​commentary and the way advertisements are displayed to provide more effective information. This allows the event organizers to provide information to the audience quickly and reliably, thereby improving event satisfaction.

[0081] The reaction analysis unit analyzes audience reactions to advertisements on large screens and on-site sampling. Specifically, it uses image recognition technology to analyze audience facial expressions and movements to evaluate the effectiveness of advertisements. For example, it applies a facial recognition algorithm using deep learning to estimate emotions towards advertisements from audience facial expressions. It can also use motion recognition algorithms to analyze audience movements and gestures to evaluate their level of interest and reaction to advertisements. Furthermore, the reaction analysis unit can analyze audience behavior data to evaluate the results of product tests. For example, it can analyze audience behavior patterns to evaluate the acceptance of new products. Audience behavior data includes travel routes, dwell time, and purchase history, and by comprehensively analyzing this data, it is possible to identify the acceptance of products and areas for improvement. In addition, the reaction analysis unit can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. For example, it can analyze audience cheers and cheers to evaluate their level of excitement and satisfaction with advertisements. Audio data analysis also includes using natural language processing technology to analyze the content of audience statements and extract emotions and opinions. This allows the reaction analysis unit to analyze audience reactions from multiple angles and comprehensively evaluate the effectiveness of advertisements and product tests.

[0082] The evaluation department evaluates the results obtained by the response analysis department. Specifically, it evaluates the effectiveness of awareness advertisements based on the analyzed response data. For example, by analyzing audience facial expressions, movements, and audio data, it is possible to quantitatively evaluate the effectiveness of advertisements by evaluating emotions and reactions to advertisements. The evaluation department can also evaluate the results of product tests and identify areas for improvement. For example, based on audience behavior data, it can identify the acceptance of new products and areas for improvement, and reflect these in the next test. Furthermore, the evaluation department can identify areas for improvement of services based on audience feedback. For example, based on audience feedback, it can identify areas for improvement of services and reflect these in the next event. The evaluation department has the infrastructure to comprehensively evaluate these data and evaluate the effectiveness of advertisements and products. Based on the evaluation results, the evaluation department can identify areas for improvement of advertisements and products and reflect them in the next event or product test. This allows the evaluation department to enhance the effectiveness of advertisements and products and improve audience satisfaction. Furthermore, based on the evaluation results, the evaluation department can analyze long-term trends and patterns and formulate future countermeasures. This allows the evaluation department to continuously improve the effectiveness of advertisements and products, thereby enhancing the overall reliability and effectiveness of the system.

[0083] The data collection unit can collect information obtained from cameras inside the dome. For example, the data collection unit can use cameras inside the dome to collect the movements and facial expressions of spectators in real time. The data collection unit can capture the movements and facial expressions of spectators with a high-resolution camera and save them as video data. The data collection unit can also collect audio data. The data collection unit can collect the cheers and cheers of spectators with a high-sensitivity microphone and save them as audio data. Furthermore, the data collection unit can also collect text data. The data collection unit can collect and save comments and feedback entered by spectators into a smartphone app as text data. In this way, the data collection unit can obtain real-time data by collecting information obtained from cameras inside the dome. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired by cameras into a generating AI and have the generating AI perform an analysis of the movements and facial expressions of spectators from the video data.

[0084] The analysis unit can analyze collected information and generate AI commentary tailored to the audience's needs. For example, the analysis unit can analyze collected video data to recognize the audience's facial expressions and movements. By analyzing the audience's facial expressions and movements, the analysis unit can estimate their interests and concerns. The analysis unit can also analyze audio data to estimate the audience's emotions and reactions. The analysis unit can analyze the audience's cheers and cheers to estimate their level of excitement and satisfaction. Furthermore, the analysis unit can analyze text data to extract the audience's opinions and impressions. The analysis unit can analyze comments and feedback entered by the audience into a smartphone app to extract their evaluations and requests. As a result, the analysis unit can generate AI commentary tailored to the audience's needs, enabling personalized information provision. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input collected video data into a generating AI and have the generating AI perform the analysis of the audience's facial expressions and movements.

[0085] The service provider can provide AI commentary to the audience's smartphone app. For example, the service provider can deliver AI commentary to the audience's smartphone app in real time. The service provider can generate AI commentary tailored to the audience's needs and deliver it to the smartphone app. The service provider can also display advertisements and information on large screens. The service provider can generate advertisements tailored to the audience's needs and display them on large screens. Furthermore, the service provider can provide real-time feedback to the audience. The service provider can provide appropriate information and advice based on the audience's reactions. In this way, the service provider can provide personalized information in real time by providing AI commentary to the audience's smartphone app. Some or all of the above processing in the service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can use generative AI to generate AI commentary tailored to the audience's needs and deliver it to the smartphone app.

[0086] The reaction analysis unit can analyze audience reactions to advertisements on large screens and on-site sampling. For example, the reaction analysis unit can analyze audience facial expressions and movements to evaluate the effectiveness of the advertisement. The reaction analysis unit can quantitatively evaluate the effectiveness of the advertisement by analyzing audience facial expressions and movements. The reaction analysis unit can also analyze audience behavior data to evaluate the results of product tests. The reaction analysis unit can analyze audience behavior patterns to evaluate the acceptance of new products. Furthermore, the reaction analysis unit can analyze audience audio data to evaluate the effectiveness of advertisements and sampling. The reaction analysis unit can analyze audience cheers and cheers to evaluate the effectiveness of the advertisement. In this way, the reaction analysis unit can evaluate the effectiveness of advertisements and sampling by analyzing audience reactions. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audience facial expression and movement data into a generative AI and have the generative AI perform an analysis to evaluate the effectiveness of the advertisement.

[0087] The evaluation unit can evaluate the effectiveness of awareness advertising based on the analyzed response data. For example, the evaluation unit can quantitatively evaluate the effectiveness of advertising based on the analyzed response data. The evaluation unit can quantify and evaluate the effectiveness of advertising based on audience response data. The evaluation unit can also evaluate the results of product tests and identify areas for product improvement. The evaluation unit can identify areas for product improvement based on audience response data and reflect them in the next test. Furthermore, the evaluation unit can identify areas for service improvement based on audience feedback. The evaluation unit can identify areas for service improvement based on audience feedback and reflect them in the next event. In this way, the evaluation unit can improve advertising strategies by evaluating the effectiveness of awareness advertising. Some or all of the above processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input the analyzed response data into generative AI and have the generative AI perform an analysis to evaluate the effectiveness of advertising.

[0088] The package unit can integrate the functions of each unit and provide them to other large-scale facility-owning companies. For example, the package unit integrates and packages the functions of the collection unit, analysis unit, provision unit, reaction analysis unit, and evaluation unit. The package unit can provide these functions as a single system. Furthermore, the package unit can provide packages to other large-scale facility-owning companies. The package unit can provide other large-scale facility-owning companies with packages that include the functions of the collection unit, analysis unit, provision unit, reaction analysis unit, and evaluation unit. This allows the package unit to enable other large-scale facility-owning companies to introduce similar services and enhance the value of their facilities. Some or all of the processing described above in the package unit may be performed using, for example, generative AI, or not using generative AI. For example, the package unit can use generative AI to optimize the integration of the functions of each unit and provide it to other large-scale facility-owning companies.

[0089] The data collection unit can estimate the audience's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can analyze the audience's facial expressions and estimate their emotions. The data collection unit can analyze the audience's facial expression data and estimate their emotions. The data collection unit can also analyze the audience's voice data and estimate their emotions. The data collection unit can analyze the audience's voice data and estimate their emotions. Furthermore, the data collection unit can analyze the audience's biometric data and estimate their emotions. The data collection unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the data collection unit to effectively collect data by adjusting the timing of information collection based on the audience's emotions. 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 above-described processing in the data collection unit may be performed using a generative AI, for example, or without a generative AI. For example, the data collection unit can input audience facial expression data into a generating AI, which can then perform emotion estimation.

[0090] The data collection unit can simultaneously collect information from different areas within the dome and analyze the characteristics of each area. For example, the data collection unit can simultaneously collect information from different areas using multiple cameras within the dome. The data collection unit can collect the movements and facial expressions of spectators in each area in real time and analyze the differences in reactions between areas. The data collection unit can also collect the behavioral patterns of spectators in each area and clarify the characteristics of each area. The data collection unit can collect behavioral data of spectators in each area and analyze the characteristics of each area. Furthermore, the data collection unit can collect audio data from each area and analyze the differences in cheers and support between areas. The data collection unit can collect audio data from each area and analyze the differences in reactions between areas. In this way, the data collection unit can clarify the differences in reactions between areas by analyzing the characteristics of each area. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the data collection unit can input data on the movements and facial expressions of spectators in each area into a generative AI and analyze the characteristics of each area.

[0091] The data collection unit can track the movements and behavioral patterns of spectators in real time during collection and collect reactions to specific events. For example, the data collection unit can track the movements and behavioral patterns of spectators in real time. The data collection unit can track the movements and behavioral patterns of spectators in real time and collect reactions to specific events. The data collection unit can also track how spectators react to specific players in real time and store this data. Furthermore, the data collection unit can collect how spectators react to specific plays in real time. The data collection unit can collect how spectators react to specific plays in real time and store this data. This allows the data collection unit to collect detailed reactions to specific events by tracking the movements and behavioral patterns of spectators in real time. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input data on spectator movements and behavioral patterns into generative AI and analyze reactions to specific events.

[0092] The data collection unit can estimate the emotions of the audience and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can analyze the audience's facial expressions and estimate their emotions. The data collection unit can analyze the audience's facial expression data and estimate their emotions. The data collection unit can also analyze the audience's voice data and estimate their emotions. The data collection unit can analyze the audience's voice data and estimate their emotions. Furthermore, the data collection unit can analyze the audience's biometric data and estimate their emotions. The data collection unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the data collection unit to prioritize the collection of important information by determining the priority of information to collect based on the audience'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-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input audience facial expression data into a generating AI, which can then perform emotion estimation.

[0093] The data collection unit can also collect information from surrounding areas outside the dome and analyze the behavior of spectators before and after the event. For example, the data collection unit can collect traffic conditions outside the dome and analyze the movement patterns of spectators. The data collection unit can collect traffic conditions outside the dome in real time and analyze the movement patterns of spectators. The data collection unit can also collect information on the usage of restaurants outside the dome and analyze the behavior of spectators. The data collection unit can collect information on the usage of restaurants outside the dome and analyze the behavior patterns of spectators. Furthermore, the data collection unit can collect information on the usage of parking lots outside the dome and analyze the behavior of spectators. The data collection unit can collect information on the usage of parking lots outside the dome and analyze the behavior patterns of spectators. In this way, by collecting information from outside the dome, the data collection unit can analyze the behavior of spectators before and after the event in detail. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input data on traffic conditions and restaurant usage outside the dome into a generative AI and analyze the behavior patterns of spectators.

[0094] The data collection unit can analyze audience social media activity and collect relevant information during the collection process. For example, the data collection unit can collect content posted by audience members on social media and analyze their reactions to the event. The data collection unit can also collect information shared by audience members on social media and analyze the impact of the event. Furthermore, the data collection unit can collect hashtags used by audience members on social media and analyze event trends. This allows the data collection unit to collect audience reactions more broadly by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input audience social media posting data into generative AI and analyze reactions to the event.

[0095] The analysis unit can estimate the emotions of the audience and adjust the analysis algorithm based on the estimated emotions. For example, the analysis unit can analyze the audience's facial expressions and estimate their emotions. The analysis unit can analyze the audience's facial expression data and estimate their emotions. The analysis unit can also analyze the audience's voice data and estimate their emotions. The analysis unit can analyze the audience's voice data and estimate their emotions. Furthermore, the analysis unit can analyze the audience's biometric data and estimate their emotions. The analysis unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the analysis unit to improve the accuracy of the analysis by adjusting the analysis algorithm based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input audience facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0096] The analysis unit can improve the accuracy of its analysis by referring to past audience behavior data during the analysis. For example, the analysis unit can refer to past audience reaction data to analyze the current reaction. The analysis unit can also refer to past audience reaction data to analyze the current reaction. Furthermore, the analysis unit can refer to past audience behavior patterns to analyze the current behavior. The analysis unit can refer to past audience behavior patterns to analyze the current behavior. In addition, the analysis unit can refer to past audience emotion data to analyze the current emotion. The analysis unit can refer to past audience emotion data to analyze the current emotion. As a result, the analysis unit improves the accuracy of its current analysis by referring to past behavior data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past audience behavior data into a generative AI and analyze the current reaction.

[0097] The analysis unit can integrate different data sources (audio, video, text) during analysis. For example, the analysis unit can integrate audio data and video data to analyze audience reactions. The analysis unit can integrate audio data and video data to analyze audience reactions. Furthermore, the analysis unit can integrate video data and text data to analyze audience reactions. The analysis unit can integrate video data and text data to analyze audience reactions. In addition, the analysis unit can integrate audio data and text data to analyze audience reactions. The analysis unit can integrate audio data and text data to analyze audience reactions. As a result, the analysis unit can improve the accuracy of its analysis by integrating different data sources. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data, video data, and text data into a generative AI to analyze audience reactions.

[0098] The analysis unit can estimate the audience's emotions and adjust the display method of the analysis results based on the estimated audience emotions. For example, the analysis unit can analyze the audience's facial expressions and estimate their emotions. The analysis unit can analyze the audience's facial expression data and estimate their emotions. The analysis unit can also analyze the audience's voice data and estimate their emotions. The analysis unit can analyze the audience's voice data and estimate their emotions. Furthermore, the analysis unit can analyze the audience's biometric data and estimate their emotions. The analysis unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the analysis unit to provide more appropriate information by adjusting the display method of the analysis results based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input audience facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0099] The analysis unit can perform analysis while considering the geographical location information of the audience. For example, the analysis unit can analyze reactions in each area while considering the seating positions of the audience. The analysis unit can analyze reactions in each area while considering the seating positions of the audience. Furthermore, the analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. The analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. In addition, the analysis unit can analyze the overall reaction while considering the geographical location information of the audience. The analysis unit can analyze the overall reaction while considering the geographical location information of the audience. As a result, the analysis unit can perform detailed analysis of reactions in each area by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical location information of the audience into a generative AI and analyze reactions in each area.

[0100] The analysis unit can improve the accuracy of its analysis by referring to the audience's social media activity during the analysis. For example, the analysis unit can refer to the content that the audience has posted on social media and analyze the reactions. The analysis unit can also refer to the information that the audience has shared on social media and analyze the reactions. Furthermore, the analysis unit can refer to the hashtags that the audience has used on social media and analyze the reactions. In this way, the analysis unit can improve the accuracy of its analysis by referring to social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the audience's social media posting data into a generative AI and analyze the reactions.

[0101] The information provider can estimate the audience's emotions and adjust the way the information is presented based on the estimated emotions. For example, the information provider can analyze the audience's facial expressions and estimate their emotions. The information provider can analyze the audience's facial expression data and estimate their emotions. The information provider can also analyze the audience's voice data and estimate their emotions. The information provider can analyze the audience's voice data and estimate their emotions. Furthermore, the information provider can analyze the audience's biometric data and estimate their emotions. The information provider can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the information provider to provide more effective information by adjusting the way the information is presented based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input audience facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0102] The service provider can provide the most relevant information by referring to the audience's past viewing history at the time of delivery. For example, the service provider can provide relevant information based on what the audience has watched in the past. The service provider can provide relevant information based on what the audience has watched in the past. The service provider can also provide information that might be of interest to the audience based on their past viewing history. The service provider can also analyze the audience's past viewing history and provide the most suitable information. The service provider can analyze the audience's past viewing history and provide the most suitable information. In this way, the service provider can provide the most relevant information to the audience by referring to their past viewing history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the audience's past viewing history into a generative AI and provide the most relevant information.

[0103] The information provider can adjust the information format according to the audience's device characteristics at the time of delivery. For example, if the audience is using a smartphone, the information provider can provide information that matches the screen size. The information provider can also provide information optimized for larger screens if the audience is using a tablet. Furthermore, if the audience is using a smartwatch, the information provider can provide concise and highly visible information. This enables the information provider to provide information according to device characteristics. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input the audience's device characteristics into a generative AI and adjust the information format.

[0104] The service provider can estimate the audience's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, the service provider can analyze the audience's facial expressions and estimate their emotions. The service provider can analyze the audience's facial expression data and estimate their emotions. The service provider can also analyze the audience's voice data and estimate their emotions. The service provider can analyze the audience's voice data and estimate their emotions. Furthermore, the service provider can analyze the audience's biometric data and estimate their emotions. The service provider can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the service provider to prioritize important information by determining the priority of the information to be provided based on the audience'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-described processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input audience facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0105] The information provider can provide optimal information by considering the geographical location information of the audience at the time of provision. For example, the information provider can provide area-specific information by considering the seating location of the audience. The information provider can provide area-specific information by considering the seating location of the audience. Furthermore, the information provider can provide information for specific areas by considering the movement patterns of the audience. The information provider can provide information for specific areas by considering the geographical location information of the audience. In addition, the information provider can provide overall information by considering the geographical location information of the audience. The information provider can provide overall information by considering the geographical location information of the audience. This enables the information provider to provide area-specific information by considering geographical location information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the information provider can input the geographical location information of the audience into a generative AI and provide optimal information.

[0106] The service provider can analyze the audience's social media activity and provide relevant information at the time of service provision. For example, the service provider can analyze the content posted by the audience on social media and provide relevant information. The service provider can analyze the content posted by the audience on social media and provide relevant information. Furthermore, the service provider can analyze the information shared by the audience on social media and provide relevant information. In addition, the service provider can analyze the hashtags used by the audience on social media and provide relevant information. The service provider can analyze the hashtags used by the audience on social media and provide relevant information. In this way, the service provider can provide relevant information to the audience by analyzing their social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the audience's social media posting data into a generative AI and provide relevant information.

[0107] The reaction analysis unit can estimate the audience's emotions and adjust the reaction analysis criteria based on the estimated audience emotions. For example, the reaction analysis unit can analyze the audience's facial expressions and estimate their emotions. The reaction analysis unit can analyze the audience's facial expression data and estimate their emotions. The reaction analysis unit can also analyze the audience's voice data and estimate their emotions. The reaction analysis unit can analyze the audience's voice data and estimate their emotions. Furthermore, the reaction analysis unit can analyze the audience's biometric data and estimate their emotions. The reaction analysis unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. As a result, the reaction analysis unit can improve the accuracy of its analysis by adjusting the reaction analysis criteria based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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-described processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The reaction analysis unit can improve the accuracy of its analysis by referring to the audience's past reaction data during reaction analysis. For example, the reaction analysis unit can refer to the audience's past reaction data to analyze the current reaction. The reaction analysis unit can also refer to the audience's past behavior patterns to analyze the current behavior. The reaction analysis unit can also refer to the audience's past behavior patterns to analyze the current behavior. Furthermore, the reaction analysis unit can refer to the audience's past emotional data to analyze the current emotion. The reaction analysis unit can refer to the audience's past emotional data to analyze the current emotion. As a result, the reaction analysis unit improves the accuracy of its current analysis by referring to past reaction data. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reaction analysis unit can input the audience's past reaction data into a generative AI to analyze the current reaction.

[0109] The reaction analysis unit can integrate different data sources (audio, video, text) during reaction analysis. For example, the reaction analysis unit can integrate audio data and video data to analyze audience reactions. The reaction analysis unit can integrate audio data and video data to analyze audience reactions. Furthermore, the reaction analysis unit can integrate video data and text data to analyze audience reactions. The reaction analysis unit can integrate audio data and text data to analyze audience reactions. In this way, the reaction analysis unit can improve the accuracy of its analysis by integrating different data sources. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audio data, video data, and text data into a generative AI to analyze audience reactions.

[0110] The reaction analysis unit can estimate the audience's emotions and adjust the display method of the reaction analysis results based on the estimated audience emotions. For example, the reaction analysis unit can analyze the audience's facial expressions and estimate their emotions. The reaction analysis unit can analyze the audience's facial expression data and estimate their emotions. The reaction analysis unit can also analyze the audience's voice data and estimate their emotions. The reaction analysis unit can analyze the audience's voice data and estimate their emotions. Furthermore, the reaction analysis unit can analyze the audience's biometric data and estimate their emotions. The reaction analysis unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. As a result, the reaction analysis unit can provide more appropriate information by adjusting the display method of the reaction analysis results based on the audience'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-described processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The reaction analysis unit can perform analysis while considering the geographical location information of the audience. For example, the reaction analysis unit can analyze reactions in each area while considering the seating positions of the audience. The reaction analysis unit can analyze reactions in each area while considering the seating positions of the audience. Furthermore, the reaction analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. The reaction analysis unit can analyze reactions in specific areas while considering the movement patterns of the audience. In addition, the reaction analysis unit can analyze the overall reaction while considering the geographical location information of the audience. The reaction analysis unit can analyze the overall reaction while considering the geographical location information of the audience. As a result, the reaction analysis unit can perform detailed analysis of reactions in each area by considering geographical location information. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the reaction analysis unit can input the geographical location information of the audience into a generative AI and analyze reactions in each area.

[0112] The reaction analysis unit can improve the accuracy of its analysis by referring to the audience's social media activity during reaction analysis. For example, the reaction analysis unit can refer to content posted by the audience on social media and analyze the reactions. The reaction analysis unit can also refer to information shared by the audience on social media and analyze the reactions. Furthermore, the reaction analysis unit can refer to hashtags used by the audience on social media and analyze the reactions. In this way, the reaction analysis unit can improve the accuracy of its analysis by referring to social media activity. Some or all of the above processing in the reaction analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reaction analysis unit can input the audience's social media posting data into a generative AI and analyze the reactions.

[0113] The evaluation unit can estimate the audience's emotions and adjust the evaluation criteria based on the estimated emotions. For example, the evaluation unit can analyze the audience's facial expressions and estimate their emotions. The evaluation unit can analyze the audience's facial expression data and estimate their emotions. The evaluation unit can also analyze the audience's voice data and estimate their emotions. The evaluation unit can analyze the audience's voice data and estimate their emotions. Furthermore, the evaluation unit can analyze the audience's biometric data and estimate their emotions. The evaluation unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. As a result, the evaluation unit can improve the accuracy of the evaluation by adjusting the evaluation criteria based on the audience's emotions. 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 above-described processes in the evaluation unit may be performed using a generative AI, for example, or without a generative AI. For example, the evaluation unit can input audience facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0114] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data during the evaluation process. For example, the evaluation unit can optimize the current evaluation by referring to past evaluation data. The evaluation unit can optimize the current evaluation by referring to past evaluation data. The evaluation unit can also optimize its evaluation algorithm by analyzing past evaluation data. Furthermore, the evaluation unit can optimize its evaluation criteria based on past evaluation data. As a result, the evaluation unit improves the accuracy of its current evaluation by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input past evaluation data into a generative AI to optimize the current evaluation.

[0115] The evaluation unit can integrate different data sources (audio, video, text) during evaluation. For example, the evaluation unit can integrate audio data and video data and perform an evaluation. The evaluation unit can integrate audio data and video data and perform an evaluation. Furthermore, the evaluation unit can integrate video data and text data and perform an evaluation. The evaluation unit can integrate audio data and text data and perform an evaluation. In this way, the evaluation unit can improve the accuracy of its evaluation by integrating different data sources. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input audio data, video data, and text data into a generative AI and perform an evaluation.

[0116] The evaluation unit can estimate the audience's emotions and adjust the display method of the evaluation results based on the estimated audience emotions. For example, the evaluation unit can analyze the audience's facial expressions and estimate their emotions. The evaluation unit can analyze the audience's facial expression data and estimate their emotions. The evaluation unit can also analyze the audience's voice data and estimate their emotions. The evaluation unit can analyze the audience's voice data and estimate their emotions. Furthermore, the evaluation unit can analyze the audience's biometric data and estimate their emotions. The evaluation unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the evaluation unit to provide more appropriate information by adjusting the display method of the evaluation results based on the audience's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 above-described processing in the evaluation unit may be performed using a generative AI, for example, or without a generative AI. For example, the evaluation unit can input audience facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0117] The evaluation unit can perform evaluations while considering the geographical location information of the audience. For example, the evaluation unit can perform area-by-area evaluations while considering the seating positions of the audience. The evaluation unit can perform area-by-area evaluations while considering the seating positions of the audience. Furthermore, the evaluation unit can perform evaluations in specific areas while considering the movement patterns of the audience. The evaluation unit can perform overall evaluations while considering the geographical location information of the audience. The evaluation unit can perform overall evaluations while considering the geographical location information of the audience. This enables the evaluation unit to perform area-by-area evaluations by considering geographical location information. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the geographical location information of the audience into a generative AI and perform area-by-area evaluations.

[0118] The evaluation unit can improve the accuracy of its evaluation by referring to the audience's social media activity during the evaluation process. For example, the evaluation unit can refer to the content that the audience has posted on social media and perform an evaluation. The evaluation unit can also refer to the information that the audience has shared on social media and perform an evaluation. Furthermore, the evaluation unit can refer to the hashtags that the audience has used on social media and perform an evaluation. The evaluation unit can refer to the hashtags that the audience has used on social media and perform an evaluation. As a result, the evaluation unit can improve the accuracy of its evaluation by referring to social media activity. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the audience's social media posting data into a generative AI and perform an evaluation.

[0119] The packaging unit integrates the functions of each unit and can adjust the way the package is delivered based on the audience's emotions. For example, the packaging unit can analyze the audience's facial expressions and estimate their emotions. The packaging unit can analyze the audience's facial expression data and estimate their emotions. The packaging unit can also analyze the audience's voice data and estimate their emotions. Furthermore, the packaging unit can analyze the audience's biometric data and estimate their emotions. The packaging unit can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the packaging unit to provide a more effective service by adjusting the way the package is delivered based on the audience'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-described processing in the packaging unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the packaging unit can input audience facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0120] The packaging unit can generate an optimal package by referring to past data of each part during the packaging process. For example, the packaging unit can generate an optimal package by referring to past data of each part. Furthermore, the packaging unit can optimize the package content by analyzing past data of each part. In addition, the packaging unit can optimize the package delivery method based on past data of each part. This allows the packaging unit to generate an optimal package by referring to past data. Some or all of the above processing in the packaging unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the packaging unit can input past data of each part into a generation AI to generate an optimal package.

[0121] The package unit integrates the functions of each unit and can adjust the method of providing packages considering the geographical location information of the audience. For example, the package unit can provide packages for each area, taking into account the seating positions of the audience. The package unit can provide packages for each area, taking into account the seating positions of the audience. Furthermore, the package unit can provide packages for specific areas, taking into account the movement patterns of the audience. The package unit can provide packages for specific areas, taking into account the geographical location information of the audience. The package unit can provide packages for the entirety, taking into account the geographical location information of the audience. This enables the package unit to provide packages for each area by considering geographical location information. Some or all of the above processing in the package unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the package unit can input the geographical location information of the audience into a generative AI and provide the optimal package.

[0122] The package unit can integrate the functions of each unit and generate the optimal package by referring to the audience's social media activity. For example, the package unit can refer to the content posted by the audience on social media and generate the optimal package. The package unit can also refer to the information shared by the audience on social media and optimize the package content. Furthermore, the package unit can refer to the hashtags used by the audience on social media and optimize the way the package is delivered. The package unit can refer to the hashtags used by the audience on social media and optimize the way the package is delivered. In this way, the package unit can generate the optimal package by referring to social media activity. Some or all of the above processing in the package unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the package unit can input the audience's social media posting data into a generation AI and generate the optimal package.

[0123] The service provider can estimate the emotions of the audience and adjust the service delivery method to other large-scale facility owners based on the estimated emotions. For example, the service provider can analyze the audience's facial expressions and estimate their emotions. The service provider can analyze the audience's facial expression data and estimate their emotions. The service provider can also analyze the audience's voice data and estimate their emotions. The service provider can analyze the audience's voice data and estimate their emotions. Furthermore, the service provider can analyze the audience's biometric data and estimate their emotions. The service provider can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the service provider to provide the most suitable service to other large-scale facility owners by adjusting the service delivery method based on the audience's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, 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 service provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provision unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0124] The service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies at the time of delivery. For example, the service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies. The service provider can select the optimal service delivery method by referring to past data of other large-scale facility-owning companies. The service provider can also optimize the service delivery method by analyzing past data of other large-scale facility-owning companies. The service provider can optimize the service delivery method by analyzing past data of other large-scale facility-owning companies. Furthermore, the service provider can optimize the service delivery method based on past data of other large-scale facility-owning companies. The service provider can optimize the service delivery method based on past data of other large-scale facility-owning companies. In this way, the service provider can select the optimal service delivery method for other large-scale facility-owning companies by referring to past data. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input past data of other large-scale facility-owning companies into a generation AI and select the optimal service delivery method.

[0125] The service provider can estimate the emotions of the audience and, based on the estimated emotions, determine the priority of services to other large-scale facility-owning companies. For example, the service provider can analyze the audience's facial expressions and estimate their emotions. The service provider can analyze the audience's facial expression data and estimate their emotions. The service provider can also analyze the audience's voice data and estimate their emotions. The service provider can analyze the audience's voice data and estimate their emotions. Furthermore, the service provider can analyze the audience's biometric data and estimate their emotions. The service provider can analyze the audience's heart rate and skin electrical activity and estimate their emotions. This allows the service provider to prioritize providing important information by determining the priority of services based on the audience's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, 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 service provision unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provision unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0126] The service provider can select the optimal service delivery method at the time of delivery, taking into account the geographical location information of other large-scale facility-owning companies. For example, the service provider can select a service delivery method for each area, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select a service delivery method for each area, taking into account the geographical location information of other large-scale facility-owning companies. Furthermore, the service provider can also select a service delivery method for a specific area, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select a service delivery method for a specific area, taking into account the geographical location information of other large-scale facility-owning companies. In addition, the service provider can select an overall service delivery method, taking into account the geographical location information of other large-scale facility-owning companies. The service provider can select an overall service delivery method, taking into account the geographical location information of other large-scale facility-owning companies. This allows the service provider to select the optimal service delivery method for other large-scale facility-owning companies by taking into account geographical location information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the geographical location information of other large-scale facility-owning companies into a generation AI and select the optimal service delivery method.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] An event analysis system can estimate audience emotions and provide content that will engage them based on those emotions. For example, if the audience is excited, the system can provide highlight reels or replays of exciting plays to further heighten their excitement. If the audience is bored, the system can provide content such as past memorable moments or player interviews to capture their attention. Furthermore, if the audience is moved, the system can provide touching stories or messages of gratitude from players to share that emotion. This enables the provision of content tailored to the audience's emotions, thereby increasing audience satisfaction.

[0129] Event analysis systems can refer to past audience behavior data and provide content tailored to their preferences. For example, audience members who have watched a particular player's games frequently in the past can be shown the player's latest plays and interviews. Similarly, audience members who have watched a particular match frequently can be shown highlights and related data from that match. Furthermore, audience members who have attended a particular event in the past can be shown content related to that event and information about upcoming events. This allows for more personalized content delivery by leveraging past audience behavior data.

[0130] Event analysis systems can provide content tailored to regional characteristics by considering the geographical location of the audience. For example, they can provide audiences in a specific region with information about players and teams associated with that region. They can also provide information about events and matches held in that region. Furthermore, they can provide audiences in a specific region with information about sponsors and advertisers in that region. This enables the provision of content tailored to regional characteristics, thereby attracting the interest of the audience.

[0131] Event analysis systems can analyze audience social media activity and provide content based on their interests. For example, if an audience member frequently posts about a particular player or team on social media, the system can provide content related to that player or team. Similarly, if an audience member frequently shares information about a specific event or match on social media, the system can provide content related to that event or match. Furthermore, it can analyze the hashtags used by audience members on social media and provide content related to those hashtags. This allows for more personalized content delivery by leveraging audience social media activity.

[0132] Event analysis systems can estimate audience emotions and adjust how ads are displayed based on those estimates. For example, if the audience is excited, the system can display exciting ads that further enhance that excitement. If the audience is bored, the system can display humorous or interactive ads to capture their interest. Furthermore, if the audience is moved, the system can display ads with emotionally resonant stories to share that emotion. This enables ad display tailored to the audience's emotions, thereby increasing the effectiveness of advertising.

[0133] Event analysis systems can refer to audiences' past viewing history and display advertisements tailored to their interests. For example, audiences who have shown interest in a particular brand or product in the past can be shown advertisements for the latest information and campaigns of that brand or product. Similarly, audiences who have frequently purchased products in a specific category can be shown advertisements related to products in that category. Furthermore, audiences who have attended a particular event in the past can be shown advertisements from sponsors associated with that event. This allows for more targeted advertising by leveraging audiences' past viewing history.

[0134] The event analysis system can adjust the ad format according to the audience's device characteristics. For example, if the audience is using a smartphone, the ad can be displayed to match the screen size. If the audience is using a tablet, an ad optimized for the larger screen can be displayed. Furthermore, if the audience is using a smartwatch, a concise and highly visible ad can be displayed. This enables ad display tailored to device characteristics, thereby increasing the effectiveness of the ads.

[0135] An event analysis system can estimate the emotions of the audience and prioritize the information it provides based on those emotions. For example, if the audience is excited, the system can prioritize providing information that will further enhance their excitement. If the audience is bored, the system can prioritize providing interesting information to capture their attention. Furthermore, if the audience is moved, the system can prioritize providing emotionally moving information to share that emotion. This allows for information provision tailored to the audience's emotions, thereby increasing audience satisfaction.

[0136] Event analysis systems can display advertisements tailored to regional characteristics by considering the geographical location of the audience. For example, audiences in a specific region can see advertisements from sponsors and advertisers related to that region. Furthermore, audiences in a specific region can see advertisements for events and campaigns held in that region. In addition, advertisements tailored to the culture and customs of that region can be displayed. This enables the display of advertisements that are specific to each region, thereby increasing the effectiveness of advertising.

[0137] Event analysis systems can analyze audience social media activity and display advertisements based on their interests. For example, if an audience frequently posts about a particular brand or product on social media, advertisements for that brand or product can be displayed. Similarly, if an audience frequently shares products from a specific category on social media, advertisements related to products in that category can be displayed. Furthermore, the system can analyze hashtags used by the audience on social media and display advertisements related to those hashtags. This allows for more targeted advertising by leveraging audience social media activity.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The collection unit gathers information obtained from cameras inside the dome. For example, the collection unit can collect the movements and facial expressions of the audience in real time, and can also collect audio and text data. Specifically, it collects the cheers and shouts of the audience, as well as comments and feedback entered into smartphone apps. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected video data to recognize the audience's facial expressions and movements, and estimates the audience's emotions and reactions from the audio data. It also analyzes the text data to extract the audience's opinions and impressions, which are used to improve the service. Step 3: The service provider provides information to the audience based on the analysis performed by the analysis unit. For example, they might provide AI commentary on the audience's smartphone app or display advertisements and information on a large screen. They can also provide real-time feedback based on the audience's reactions. Step 4: The reaction analysis unit analyzes audience reactions to advertisements on large screens and on-site sampling. For example, it analyzes audience facial expressions and movements to evaluate the effectiveness of advertisements, and analyzes audience behavior data to evaluate the results of product tests. It also analyzes audience audio data to evaluate the effectiveness of advertisements and sampling. Step 5: The evaluation unit evaluates the results obtained by the response analysis unit. For example, it evaluates the effectiveness of awareness advertising based on the analyzed response data and evaluates the results of product tests to identify areas for product improvement. It also identifies areas for service improvement based on audience feedback and incorporates them into the next event.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, reaction analysis unit, evaluation unit, and packaging unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the movements, facial expressions, and sounds of the audience. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to estimate the emotions and reactions of the audience. The provision unit uses the control unit 46A of the smart device 14 to provide the audience with AI commentary and advertisements. The reaction analysis unit analyzes the audience's reactions using the specific processing unit 290 of the data processing unit 12 to evaluate the effectiveness of the advertisements. The evaluation unit evaluates the analysis results using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement in the service. The packaging unit integrates these functions and provides them to other large-scale facility-owning companies. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, reaction analysis unit, evaluation unit, and packaging unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the movements, facial expressions, and voices of the audience. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to estimate the emotions and reactions of the audience. The provision unit uses the control unit 46A of the smart glasses 214 to provide the audience with AI commentary and advertisements. The reaction analysis unit analyzes the audience's reactions using the specific processing unit 290 of the data processing unit 12 to evaluate the effectiveness of the advertisements. The evaluation unit evaluates the analysis results using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement in the service. The packaging unit integrates these functions and provides them to other large-scale facility-owning companies. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, reaction analysis unit, evaluation unit, and packaging unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the movements, facial expressions, and voices of the audience. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to estimate the emotions and reactions of the audience. The provision unit uses the control unit 46A of the headset terminal 314 to provide the audience with AI commentary and advertisements. The reaction analysis unit analyzes the audience's reactions using the specific processing unit 290 of the data processing unit 12 to evaluate the effectiveness of the advertisements. The evaluation unit evaluates the analysis results using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement in the service. The packaging unit integrates these functions and provides them to other large-scale facility-owning companies. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, reaction analysis unit, evaluation unit, and packaging unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the movements, facial expressions, and sounds of the audience. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to estimate the emotions and reactions of the audience. The provision unit uses the control unit 46A of the robot 414 to provide the audience with AI commentary and advertisements. The reaction analysis unit analyzes the audience's reactions using the specific processing unit 290 of the data processing unit 12 to evaluate the effectiveness of the advertisements. The evaluation unit evaluates the analysis results using the specific processing unit 290 of the data processing unit 12 to identify areas for improvement in the service. The packaging unit integrates these functions and provides them to other large-scale facility-owning companies. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] (Note 1) A collection unit that collects information obtained from cameras inside the dome, An analysis unit analyzes the information collected by the aforementioned collection unit, A provision unit that provides information to the audience based on the information analyzed by the aforementioned analysis unit, A reaction analysis unit that analyzes audience reactions, The system includes an evaluation unit for evaluating the results obtained by the reaction analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information obtained from cameras inside the dome. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to generate AI commentary tailored to the audience's needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Providing AI commentary to spectators' smartphone apps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The reaction analysis unit is Analyze audience reactions to advertisements on large screens and on-site sampling. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, Evaluate the effectiveness of awareness advertising based on analyzed response data. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a package section that integrates the functions of each part and provides them to other large-scale facility-owning companies. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the audience's emotions and adjusts the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Information is collected simultaneously from different areas within the dome, and the characteristics of each area are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the movements and behavioral patterns of the audience are tracked in real time, and their reactions to specific events are collected. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the audience's emotions and determines the priority of information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We also collect information from the surrounding areas outside the dome and analyze the behavior of spectators before and after the event. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the audience's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the audience's emotions and adjusts the analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, past audience behavior data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different data sources are integrated and analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the audience's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the geographical location information of the audience will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to the audience's social media activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the audience's emotions and adjusts how the information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, we refer to the audience's past viewing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the content, the information format is adjusted according to the characteristics of the audience's device. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the audience's emotions and prioritizes the information it provides based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we will consider the audience's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, we analyze the audience's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The reaction analysis unit is We estimate the audience's emotions and adjust the criteria for reaction analysis based on the estimated audience emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reaction analysis unit is During reaction analysis, we improve the accuracy of the analysis by referring to past audience reaction data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reaction analysis unit is When performing reaction analysis, different data sources are integrated for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 29) The reaction analysis unit is The system estimates the audience's emotions and adjusts the display method of the reaction analysis results based on the estimated audience emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The reaction analysis unit is During reaction analysis, the analysis takes into account the geographical location information of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reaction analysis unit is During reaction analysis, we improve the accuracy of the analysis by referring to the audience's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 32) The evaluation unit, The system estimates the audience's emotions and adjusts the evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The evaluation unit, During the evaluation, different data sources are integrated for the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The evaluation unit, The system estimates the audience's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The evaluation unit, During the evaluation process, the audience's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 37) The evaluation unit, During the evaluation process, we refer to the audience's social media activity to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned package section is Integrate the functions of each part and adjust the way the package is delivered based on the audience's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned package section is During the packaging process, the optimal package is generated by referencing past data for each component. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned package section is The functions of each section are integrated, and the method of providing the package is adjusted considering the audience's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned package section is It integrates the functions of each part and generates the optimal package by referencing the audience's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned supply unit is, We estimate the audience's emotions and adjust how we provide services to other large-scale facility owners based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by referring to past data from other large-scale facility-owning companies. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned supply unit is, The system estimates audience sentiment and, based on that estimation, determines the priority of offering the facility to other large-scale facility owners. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by considering the geographical location information of other large-scale facility-owning companies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0212] 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 collection unit that collects information obtained from cameras inside the dome, An analysis unit analyzes the information collected by the aforementioned collection unit, A provision unit that provides information to the audience based on the information analyzed by the aforementioned analysis unit, A reaction analysis unit that analyzes audience reactions, The system includes an evaluation unit for evaluating the results obtained by the reaction analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information obtained from cameras inside the dome. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to generate AI commentary tailored to the audience's needs. The system according to feature 1.

4. The aforementioned supply unit is, Providing AI commentary to spectators' smartphone apps. The system according to feature 1.

5. The reaction analysis unit is Analyze audience reactions to advertisements on large screens and on-site sampling. The system according to feature 1.

6. The evaluation unit described above, Evaluate the effectiveness of awareness advertising based on analyzed response data. The system according to feature 1.

7. It includes a package section that integrates the functions of each part and provides them to other large-scale facility-owning companies. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the audience's emotions and adjusts the timing of information gathering based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Information is collected simultaneously from different areas within the dome, and the characteristics of each area are analyzed. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the movements and behavioral patterns of the audience are tracked in real time, and their reactions to specific events are collected. The system according to feature 1.

Images