system

Abstract

The system according to this embodiment aims to grasp the success or failure of an event in real time and analyze it from multiple perspectives. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an analysis unit. The collection unit collects reactions from the venue. The analysis unit analyzes the reactions collected by the collection unit. The provision unit provides the reactions analyzed by the analysis unit to the storyteller. The analysis unit analyzes the success of the event from multiple perspectives based on the reactions provided by the provision 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to grasp the success or failure of an event in real time, and there is a problem that the result can only be seen after the event is over.

[0005] The system according to the embodiment aims to grasp the success or failure of an event in real time and analyze it from multiple perspectives.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an analysis unit. The collection unit collects reactions from the venue. The analysis unit analyzes the reactions collected by the collection unit. The provision unit provides the reactions analyzed by the analysis unit to the storyteller. The analysis unit analyzes the success of the event from multiple perspectives based on the reactions provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the success or failure of an event in real time and analyze it from multiple perspectives. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] [[ID=…]] 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 success determination system according to an embodiment of the present invention is a system that can determine the success or failure of an event in real time without having to collect questionnaires or other information from people at the venue. The event success determination system allows storytellers to "visualize" the audience's reactions and respond in real time. Furthermore, the event success determination system can analyze the success of the event from multiple perspectives using numerical data. For example, the event success determination system collects the reactions of people at the venue in real time. For example, the event success determination system uses sensors and cameras installed in the venue to detect the facial expressions and actions of the audience. This information is analyzed by AI, and the audience's reactions are quantified. For example, the number of smiles and applause from the audience are tallied in real time. Next, based on the analyzed data, the event success determination system allows storytellers to check the audience's reactions in real time. For example, the event success determination system displays the audience's reactions in graphs and charts on a monitor installed in front of the storyteller. This allows the storyteller to immediately grasp the audience's reactions and modify the content of their lecture as needed. Furthermore, after the event ends, the event success determination system performs a multifaceted analysis based on the collected data. For example, an event success evaluation system can analyze audience reactions over time to identify which parts were particularly well-received. It can also analyze audience reactions by attribute, understanding differences in reactions based on age and gender. This allows the system to identify areas for improvement in future events and plan more successful ones. This system is expected to be useful in places where people gather, such as shareholder meetings, employee conferences, university lectures, concert venues, celebrity presentations, and theater events. By providing real-time feedback and multifaceted analysis, it can support event success. This enables the event success evaluation system to grasp event success in real time, allowing for rapid response and improvement.

[0029] The event success determination system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an analysis unit. The collection unit collects reactions from the venue. The collection unit detects the facial expressions and movements of the audience using, for example, sensors or cameras. The collection unit can detect the movements of the audience using, for example, an infrared sensor. The collection unit can also capture the facial expressions of the audience in high resolution using an HD camera. Furthermore, the collection unit can also detect applause and laughter from the audience using a sound sensor. The analysis unit analyzes the reactions collected by the collection unit. The analysis unit compiles, for example, the number of smiles and applause from the audience in real time. The analysis unit can detect smiles from the audience using, for example, facial recognition technology. Furthermore, the analysis unit can also count the number of applause from the audience using voice recognition technology. Furthermore, the analysis unit can also analyze the movements of the audience using motion analysis technology. The provision unit provides the reactions analyzed by the analysis unit to the storyteller. The provision unit displays the audience reactions in graphs or charts on a monitor installed in front of the storyteller, for example. The data provision unit can, for example, visually display audience reactions using bar graphs. It can also display the proportion of audience reactions using pie charts. Furthermore, it can display changes in audience reactions using line graphs. The analysis unit analyzes the success of the event from multiple perspectives based on the reactions provided by the data provision unit. For example, the analysis unit analyzes audience reactions over time to identify which parts were particularly well-received. The analysis unit can analyze audience reactions in minute increments, or even second increments. Furthermore, the analysis unit can analyze audience reactions by attribute to understand differences in reactions based on age and gender. For example, the analysis unit can analyze reactions by audience age, gender, and occupation. Thus, the event success determination system according to this embodiment can comprehensively assess the success of an event by collecting, analyzing, providing, and analyzing venue reactions in real time.

[0030] The data collection unit collects audience reactions. For example, it uses sensors and cameras to detect audience expressions and movements. Specifically, it can use infrared sensors to detect audience movement. Infrared sensors accurately capture audience movement and position, collecting movement patterns in real time. It can also use HD cameras to capture audience expressions in high resolution. HD cameras capture subtle changes in audience facial expressions, providing data for analyzing emotions such as smiles, surprise, and excitement. Furthermore, it can use audio sensors to detect audience applause and laughter. Audio sensors monitor the acoustic environment within the venue, recording the intensity and frequency of applause, and the volume and duration of laughter. This allows the data collection unit to combine various sensors to collect audience reactions from multiple angles, enabling real-time monitoring of the event's progress. The collected data is transmitted to a central database, making it accessible to the analysis and data provision units. Additionally, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, data collection can be enhanced during specific sessions or performances to record detailed reactions. 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 reactions collected by the collection unit. For example, the analysis unit compiles data in real time, such as the number of smiles and applause from the audience. Specifically, it can detect smiles from the audience using facial recognition technology. Facial recognition technology identifies the faces of the audience from camera footage and analyzes the presence and duration of smiles. It can also count the number of applause from the audience using speech recognition technology. Speech recognition technology analyzes acoustic data acquired from sound sensors, identifies the sound of applause, and compiles the number of times it is applause is performed. Furthermore, it can analyze the movements of the audience using motion analysis technology. Motion analysis technology analyzes the patterns and intensity of audience movements based on data acquired from infrared sensors and cameras. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp audience reactions in real time. In addition, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past event data, it can predict fluctuations in audience reactions to specific performances or speakers, which can be used to plan future events. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides the storyteller with the reactions analyzed by the analysis unit. For example, the service provider displays audience reactions in graphs and charts on a monitor placed in front of the storyteller. Specifically, bar graphs can be used to visually display audience reactions. Bar graphs display the number of smiles and applause along a time axis, allowing for a quick understanding of when reactions increased. Pie charts can also be used to display the proportion of audience reactions. Pie charts show the proportion of each element, such as smiles, applause, and movement, within the overall reaction, serving as a tool for visually understanding the audience's overall reaction. Furthermore, line graphs can be used to display changes in audience reactions. Line graphs show fluctuations in reactions over time, providing data for detailed analysis of how specific sessions or performances affected the audience. This allows the service provider to provide storytellers with real-time feedback on audience reactions and information to adjust their performance on the spot. In addition, the service provider can collect feedback from storytellers and continuously improve the accuracy and effectiveness of the service provided. For example, based on feedback from storytellers, the format of displayed graphs and charts can be reviewed to provide more intuitive and easy-to-understand information. This allows the service provider to provide storytellers with quick and accurate information, supporting the success of the event.

[0033] The analytics department conducts a multifaceted analysis of the event's success based on the responses provided by the service department. Specifically, it analyzes audience responses over time to identify which parts were particularly well-received. For example, the analytics department can analyze audience responses minute by minute. Minute-by-minute analysis allows for a detailed evaluation of each session and performance of the event, clearly identifying peaks and dips in audience response at specific time points. It can also analyze audience responses second by second. Second-by-second analysis captures instantaneous fluctuations in responses and provides data for a detailed evaluation of the impact of specific comments or actions on the audience. Furthermore, the analytics department can analyze audience responses by attribute to understand differences in responses by age and gender. For example, it can analyze responses by age of the audience. Age-based analysis clarifies how audiences of different age groups responded, which can be used to plan and improve events tailored to target audiences. It can also analyze responses by gender of the audience. Gender-based analysis reveals differences between male and female audiences, which can be used to adjust marketing strategies and content according to gender. Furthermore, it can also analyze responses by occupation of the audience. Occupational analysis clarifies how audiences with different occupational backgrounds reacted, providing data to optimize event content and approaches according to occupation. This allows the analysis department to analyze audience reactions from multiple perspectives and gain a detailed understanding of the factors contributing to event success. Furthermore, based on these analysis results, the analysis department can make concrete suggestions that will be useful for planning and improving future events. Thus, the event success determination system according to this embodiment can comprehensively understand the success of an event by collecting, analyzing, providing, and analyzing venue reactions in real time.

[0034] The data collection unit can detect the facial expressions and movements of the audience using sensors and cameras. For example, the data collection unit can detect the movements of the audience using an infrared sensor. For example, the data collection unit can also capture the facial expressions of the audience in high resolution using an HD camera. For example, the data collection unit can detect the applause and laughter of the audience using an audio sensor. This allows for the collection of accurate reactions in real time by detecting the facial expressions and movements of the audience. 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 the audience's facial expression data into a generating AI and have the generating AI perform facial expression analysis.

[0035] The analysis unit can collect data in real time, such as the number of smiles and applause from the audience. For example, the analysis unit can detect smiles from the audience using facial recognition technology. The analysis unit can also count the number of applause from the audience using speech recognition technology. The analysis unit can also analyze the movements of the audience using motion analysis technology. This allows for immediate understanding of audience reactions by collecting data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the number of smiles from the audience into a generating AI and have the generating AI perform smile detection.

[0036] The display unit can display audience reactions in graphs or charts on a monitor placed in front of the storyteller. The display unit can, for example, visually display audience reactions using bar graphs. The display unit can also, for example, display the percentage of audience reactions using pie charts. The display unit can also, for example, display changes in audience reactions using line graphs. This allows the storyteller to visually grasp audience reactions in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input audience reaction data into a generating AI and have the generating AI generate graphs or charts.

[0037] The analysis unit can analyze audience reactions over time to identify which parts were particularly well-received. For example, the analysis unit can analyze audience reactions minute by minute. The analysis unit can also analyze audience reactions second by second. This allows for the identification of which parts of the event were particularly well-received, which can be used to improve future events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the analysis over time.

[0038] The analysis unit can analyze audience reactions by attribute and understand differences in reactions based on age and gender. For example, the analysis unit can analyze audience reactions by age. The analysis unit can also analyze audience reactions by gender. By understanding differences in reactions based on audience attributes, it becomes possible to improve events to suit the target audience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI perform attribute-based analysis.

[0039] The data collection unit can analyze past audience reaction data and select the optimal data collection method. For example, the data collection unit can optimize the placement of specific sensors based on reaction data from past events. The data collection unit can also predict changes in reactions during specific time periods based on past reaction data and adjust the data collection method accordingly. For example, the data collection unit can analyze past reaction data and select a data collection method that is effective for a specific audience group. This enables efficient data collection by selecting the optimal data collection method based on past reaction data. 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 past reaction data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter the data while considering the audience's seating location information. For example, the data collection unit can prioritize collecting the reactions of audience members in the front rows based on seating location information. The data collection unit can also focus on collecting the reactions of audience members in a specific area, taking seating location information into consideration. The data collection unit can also identify the direction of the audience's gaze based on seating location information and filter the reaction data accordingly. This allows for more accurate data collection by considering the audience's seating location information. 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 seating location information into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the audience's device information during data collection. For example, if an audience member is using a smartphone, the data collection unit will prioritize the collection of the device's sensor information. If an audience member is using a tablet, the data collection unit can also prioritize the collection of screen touch information. If an audience member is using a smartwatch, the data collection unit can also prioritize the collection of heart rate and motion data. This allows for the efficient collection of highly relevant data by considering the audience member's device information. 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 the audience member's device information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the audience's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect the content of social media posts made by the audience during the event. The data collection unit can also collect, for example, the audience's social media reactions (likes, shares, etc.). The data collection unit can also collect data considering, for example, the number of followers and influence of the audience's social media accounts. This allows for the efficient collection of relevant data by analyzing the audience's social media activity. 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 the audience's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the intensity of the audience's reaction during the analysis. For example, if the reaction is strong, the analysis unit can perform a detailed analysis and extract detailed data. For example, if the reaction is weak, the analysis unit can perform a simplified analysis and extract only the main data. The analysis unit can also dynamically adjust the accuracy of the analysis according to the intensity of the reaction. This allows for efficient analysis by adjusting the level of detail of the analysis according to the intensity of the audience's reaction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis methods depending on the audience's attribute information during analysis. For example, the analysis unit can apply different analysis methods depending on age group. The analysis unit can also apply different analysis methods depending on gender. The analysis unit can also apply different analysis methods depending on occupation or interests. By applying different analysis methods depending on the audience's attribute information, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI execute the application of different analysis methods.

[0045] The analysis unit can determine the priority of analysis based on the timing of audience reactions during the analysis. For example, the analysis unit may prioritize the analysis of reactions during specific time periods. The analysis unit may also prioritize the analysis of reactions at the start or end of an event. For example, the analysis unit may identify peak reaction times and prioritize the analysis of data during those times. This enables efficient analysis by determining the priority of analysis based on the timing of audience reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the priority determination.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the audience during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to past research on audience reactions. For example, the analysis unit can also improve the analysis algorithm by referring to relevant academic papers. For example, the analysis unit can improve the accuracy of its analysis by referring to reaction data from other events. Thus, the accuracy of the analysis is improved by referring to relevant literature on the audience. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0047] The information provider can adjust the level of detail of the information provided based on the importance of the audience's reactions at the time of provision. For example, the provider can provide detailed information for important reactions. For example, the provider can also provide simplified information for less important reactions. The provider can also dynamically adjust the level of detail of the information according to the importance of the reactions. This allows important information to be provided preferentially by adjusting the level of detail of the information according to the importance of the audience's reactions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0048] The content delivery unit can apply different delivery methods depending on the audience's attribute information at the time of delivery. For example, the content delivery unit can apply different delivery methods depending on age group. For example, the content delivery unit can also apply different delivery methods depending on gender. For example, the content delivery unit can also apply different delivery methods depending on occupation or interests. This makes it possible to provide more appropriate information by applying different delivery methods depending on the audience's attribute information. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or without using AI. For example, the content delivery unit can input audience attribute data into a generating AI and have the generating AI execute the application of different delivery methods.

[0049] The information provider can adjust the order of information provided based on the timing of audience reactions. For example, the provider can adjust the order of information based on reactions at a specific time. The provider can also adjust the order of information based on reactions at the start or end of an event. For example, the provider can identify peak reaction times and provide important information during those times. This allows for efficient information provision by adjusting the order of information based on the timing of audience reactions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the information order.

[0050] The information provider can improve the accuracy of the information it provides by referring to relevant literature for the audience at the time of provision. For example, the information provider can improve the accuracy of the information by referring to past research on audience reactions. For example, the information provider can also improve the accuracy of the information it provides by referring to relevant academic papers. For example, the information provider can improve the accuracy of the information it provides by referring to reaction data from other events. In this way, the accuracy of the information provided is improved by referring to relevant literature for the audience. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input relevant literature data into a generating AI and have the generating AI perform the information accuracy improvement.

[0051] The analysis unit can adjust the level of detail of the analysis based on the intensity of the audience's reaction during the analysis. For example, if the reaction is strong, the analysis unit can perform a detailed analysis and extract detailed data. For example, if the reaction is weak, the analysis unit can perform a simplified analysis and extract only the main data. The analysis unit can also dynamically adjust the accuracy of the analysis according to the intensity of the reaction. This allows for efficient analysis by adjusting the level of detail of the analysis according to the intensity of the audience's reaction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0052] The analysis unit can apply different analysis methods depending on the audience's attribute information during analysis. For example, the analysis unit can apply different analysis methods depending on age group. The analysis unit can also apply different analysis methods depending on gender. The analysis unit can also apply different analysis methods depending on occupation or interests. By applying different analysis methods depending on the audience's attribute information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI execute the application of different analysis methods.

[0053] The analysis unit can determine the priority of analysis based on the timing of audience reactions during the analysis. For example, the analysis unit may prioritize analyzing reactions during specific time periods. The analysis unit may also prioritize analyzing reactions at the start or end of an event. For example, the analysis unit may identify peak reaction times and prioritize analyzing data from those times. This enables efficient analysis by determining the priority of analysis based on the timing of audience reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the priority determination.

[0054] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the audience during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to past research on audience reactions. For example, the analysis unit can also improve the analysis algorithm by referring to relevant academic papers. For example, the analysis unit can improve the accuracy of its analysis by referring to reaction data from other events. Thus, the accuracy of the analysis is improved by referring to relevant literature on the audience. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

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

[0056] The event success evaluation system can also include a feedback collection unit. This unit collects feedback provided by the audience after the event. For example, it can send questionnaires to audience members' smartphones to collect their evaluations and opinions on the event. It can also collect comments and ratings posted by audience members on social media. Furthermore, it can collect feedback provided by audience members in real time during the event. This allows the event success evaluation system to collect audience feedback and evaluate the event's success from a more multifaceted perspective.

[0057] The event success assessment system can also include a prediction unit. The prediction unit predicts the success of future events based on collected data. For example, the prediction unit can analyze past event data to predict audience reactions at future events. It can also analyze current event data in real time to predict future reactions during the event. Furthermore, the prediction unit can predict the reactions of specific audience segments based on audience attribute information. This allows the event success assessment system to predict the success of future events and take proactive measures.

[0058] The event success evaluation system can also include an interaction section. This interaction section facilitates interaction between the audience and the storyteller. For example, the interaction section can provide a function that allows the audience to post questions in real time. It can also provide a function that allows the audience to participate in voting or surveys. Furthermore, it can provide a function that allows the audience to provide real-time feedback to the storyteller. In this way, the event success evaluation system can facilitate interaction between the audience and the storyteller, thereby supporting the success of the event.

[0059] The event success assessment system can also include a personalization component. This component provides individually optimized content based on audience attribute information and past response data. For example, the personalization component can provide different content depending on the audience's age and gender. It can also provide customized content based on the audience's interests and preferences. Furthermore, it can provide optimal content based on the audience's past response data. This allows the event success assessment system to provide the most suitable content for each individual audience member, thereby supporting the success of the event.

[0060] The event success evaluation system can also be equipped with a real-time translation unit. This unit translates the storyteller's remarks in real time and provides them to audiences who speak different languages. For example, the real-time translation unit can translate the storyteller's remarks from English to Japanese and provide them to Japanese-speaking audiences. It can also translate the storyteller's remarks from Spanish to English and provide them to English-speaking audiences. Furthermore, the real-time translation unit can simultaneously translate the storyteller's remarks into multiple languages ​​and provide them to audiences who speak different languages. This allows the event success evaluation system to effectively provide information to audiences who speak different languages, thereby supporting the success of the event.

[0061] The event success determination system can also include a data integration unit. This unit integrates data collected from multiple data sources and performs comprehensive analysis. For example, it can integrate audience facial expression data, motion data, and audio data to analyze overall reactions. It can also integrate audience biometric data and social media data to more accurately understand the audience's emotional state. Furthermore, it can integrate past and current event data to identify factors contributing to event success. This allows the event success determination system to integrate multiple data sources, perform comprehensive analysis, and support event success.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects the audience's reactions. The collection unit uses sensors and cameras to detect the audience's facial expressions and movements. For example, it uses infrared sensors to detect audience movement, HD cameras to capture the audience's facial expressions in high resolution, and audio sensors to detect audience applause and laughter. Step 2: The analysis unit analyzes the reactions collected by the collection unit. The analysis unit compiles data in real time, such as the number of smiles and applause from the audience. For example, it uses facial recognition technology to detect smiles from the audience, speech recognition technology to count the number of applause, and motion analysis technology to analyze the movements of the audience. Step 3: The distribution unit provides the storyteller with the reactions analyzed by the analysis unit. The distribution unit displays the audience reactions in graphs and charts on a monitor placed in front of the storyteller. For example, bar graphs, pie charts, and line graphs are used to visually display the audience reactions. Step 4: The analysis department conducts a multifaceted analysis of the event's success based on the feedback provided by the delivery department. The analysis department analyzes audience reactions over time to identify which parts were particularly well-received. For example, they analyze audience reactions minute by minute or second by second, and analyze audience reactions by attribute to understand differences in reactions by age and gender.

[0064] (Example of form 2) The event success determination system according to an embodiment of the present invention is a system that can determine the success or failure of an event in real time without having to collect questionnaires or other information from people at the venue. The event success determination system allows storytellers to "visualize" the audience's reactions and respond in real time. Furthermore, the event success determination system can analyze the success of the event from multiple perspectives using numerical data. For example, the event success determination system collects the reactions of people at the venue in real time. For example, the event success determination system uses sensors and cameras installed in the venue to detect the facial expressions and actions of the audience. This information is analyzed by AI, and the audience's reactions are quantified. For example, the number of smiles and applause from the audience are tallied in real time. Next, based on the analyzed data, the event success determination system allows storytellers to check the audience's reactions in real time. For example, the event success determination system displays the audience's reactions in graphs and charts on a monitor installed in front of the storyteller. This allows the storyteller to immediately grasp the audience's reactions and modify the content of their lecture as needed. Furthermore, after the event ends, the event success determination system performs a multifaceted analysis based on the collected data. For example, an event success evaluation system can analyze audience reactions over time to identify which parts were particularly well-received. It can also analyze audience reactions by attribute, understanding differences in reactions based on age and gender. This allows the system to identify areas for improvement in future events and plan more successful ones. This system is expected to be useful in places where people gather, such as shareholder meetings, employee conferences, university lectures, concert venues, celebrity presentations, and theater events. By providing real-time feedback and multifaceted analysis, it can support event success. This enables the event success evaluation system to grasp event success in real time, allowing for rapid response and improvement.

[0065] The event success determination system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an analysis unit. The collection unit collects reactions from the venue. The collection unit detects the facial expressions and movements of the audience using, for example, sensors or cameras. The collection unit can detect the movements of the audience using, for example, an infrared sensor. The collection unit can also capture the facial expressions of the audience in high resolution using an HD camera. Furthermore, the collection unit can also detect applause and laughter from the audience using a sound sensor. The analysis unit analyzes the reactions collected by the collection unit. The analysis unit compiles, for example, the number of smiles and applause from the audience in real time. The analysis unit can detect smiles from the audience using, for example, facial recognition technology. Furthermore, the analysis unit can also count the number of applause from the audience using voice recognition technology. Furthermore, the analysis unit can also analyze the movements of the audience using motion analysis technology. The provision unit provides the reactions analyzed by the analysis unit to the storyteller. The provision unit displays the audience reactions in graphs or charts on a monitor installed in front of the storyteller, for example. The data provision unit can, for example, visually display audience reactions using bar graphs. It can also display the proportion of audience reactions using pie charts. Furthermore, it can display changes in audience reactions using line graphs. The analysis unit analyzes the success of the event from multiple perspectives based on the reactions provided by the data provision unit. For example, the analysis unit analyzes audience reactions over time to identify which parts were particularly well-received. The analysis unit can analyze audience reactions in minute increments, or even second increments. Furthermore, the analysis unit can analyze audience reactions by attribute to understand differences in reactions based on age and gender. For example, the analysis unit can analyze reactions by audience age, gender, and occupation. Thus, the event success determination system according to this embodiment can comprehensively assess the success of an event by collecting, analyzing, providing, and analyzing venue reactions in real time.

[0066] The data collection unit collects audience reactions. For example, it uses sensors and cameras to detect audience expressions and movements. Specifically, it can use infrared sensors to detect audience movement. Infrared sensors accurately capture audience movement and position, collecting movement patterns in real time. It can also use HD cameras to capture audience expressions in high resolution. HD cameras capture subtle changes in audience facial expressions, providing data for analyzing emotions such as smiles, surprise, and excitement. Furthermore, it can use audio sensors to detect audience applause and laughter. Audio sensors monitor the acoustic environment within the venue, recording the intensity and frequency of applause, and the volume and duration of laughter. This allows the data collection unit to combine various sensors to collect audience reactions from multiple angles, enabling real-time monitoring of the event's progress. The collected data is transmitted to a central database, making it accessible to the analysis and data provision units. Additionally, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, data collection can be enhanced during specific sessions or performances to record detailed reactions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes the reactions collected by the collection unit. For example, the analysis unit compiles data in real time, such as the number of smiles and applause from the audience. Specifically, it can detect smiles from the audience using facial recognition technology. Facial recognition technology identifies the faces of the audience from camera footage and analyzes the presence and duration of smiles. It can also count the number of applause from the audience using speech recognition technology. Speech recognition technology analyzes acoustic data acquired from sound sensors, identifies the sound of applause, and compiles the number of times it is applause is performed. Furthermore, it can analyze the movements of the audience using motion analysis technology. Motion analysis technology analyzes the patterns and intensity of audience movements based on data acquired from infrared sensors and cameras. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp audience reactions in real time. In addition, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, based on past event data, it can predict fluctuations in audience reactions to specific performances or speakers, which can be used to plan future events. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The service provider provides the storyteller with the reactions analyzed by the analysis unit. For example, the service provider displays audience reactions in graphs and charts on a monitor placed in front of the storyteller. Specifically, bar graphs can be used to visually display audience reactions. Bar graphs display the number of smiles and applause along a time axis, allowing for a quick understanding of when reactions increased. Pie charts can also be used to display the proportion of audience reactions. Pie charts show the proportion of each element, such as smiles, applause, and movement, within the overall reaction, serving as a tool for visually understanding the audience's overall reaction. Furthermore, line graphs can be used to display changes in audience reactions. Line graphs show fluctuations in reactions over time, providing data for detailed analysis of how specific sessions or performances affected the audience. This allows the service provider to provide storytellers with real-time feedback on audience reactions and information to adjust their performance on the spot. In addition, the service provider can collect feedback from storytellers and continuously improve the accuracy and effectiveness of the service provided. For example, based on feedback from storytellers, the format of displayed graphs and charts can be reviewed to provide more intuitive and easy-to-understand information. This allows the service provider to provide storytellers with quick and accurate information, supporting the success of the event.

[0069] The analytics department conducts a multifaceted analysis of the event's success based on the responses provided by the service department. Specifically, it analyzes audience responses over time to identify which parts were particularly well-received. For example, the analytics department can analyze audience responses minute by minute. Minute-by-minute analysis allows for a detailed evaluation of each session and performance of the event, clearly identifying peaks and dips in audience response at specific time points. It can also analyze audience responses second by second. Second-by-second analysis captures instantaneous fluctuations in responses and provides data for a detailed evaluation of the impact of specific comments or actions on the audience. Furthermore, the analytics department can analyze audience responses by attribute to understand differences in responses by age and gender. For example, it can analyze responses by age of the audience. Age-based analysis clarifies how audiences of different age groups responded, which can be used to plan and improve events tailored to target audiences. It can also analyze responses by gender of the audience. Gender-based analysis reveals differences between male and female audiences, which can be used to adjust marketing strategies and content according to gender. Furthermore, it can also analyze responses by occupation of the audience. Occupational analysis clarifies how audiences with different occupational backgrounds reacted, providing data to optimize event content and approaches according to occupation. This allows the analysis department to analyze audience reactions from multiple perspectives and gain a detailed understanding of the factors contributing to event success. Furthermore, based on these analysis results, the analysis department can make concrete suggestions that will be useful for planning and improving future events. Thus, the event success determination system according to this embodiment can comprehensively understand the success of an event by collecting, analyzing, providing, and analyzing venue reactions in real time.

[0070] The data collection unit can detect the facial expressions and movements of the audience using sensors and cameras. For example, the data collection unit can detect the movements of the audience using an infrared sensor. For example, the data collection unit can also capture the facial expressions of the audience in high resolution using an HD camera. For example, the data collection unit can detect the applause and laughter of the audience using an audio sensor. This allows for the collection of accurate reactions in real time by detecting the facial expressions and movements of the audience. 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 the audience's facial expression data into a generating AI and have the generating AI perform facial expression analysis.

[0071] The analysis unit can collect data in real time, such as the number of smiles and applause from the audience. For example, the analysis unit can detect smiles from the audience using facial recognition technology. The analysis unit can also count the number of applause from the audience using speech recognition technology. The analysis unit can also analyze the movements of the audience using motion analysis technology. This allows for immediate understanding of audience reactions by collecting data in real time. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the number of smiles from the audience into a generating AI and have the generating AI perform smile detection.

[0072] The display unit can display audience reactions in graphs or charts on a monitor placed in front of the storyteller. The display unit can, for example, visually display audience reactions using bar graphs. The display unit can also, for example, display the percentage of audience reactions using pie charts. The display unit can also, for example, display changes in audience reactions using line graphs. This allows the storyteller to visually grasp audience reactions in real time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input audience reaction data into a generating AI and have the generating AI generate graphs or charts.

[0073] The analysis unit can analyze audience reactions over time to identify which parts were particularly well-received. For example, the analysis unit can analyze audience reactions minute by minute. The analysis unit can also analyze audience reactions second by second. This allows for the identification of which parts of the event were particularly well-received, which can be used to improve future events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the analysis over time.

[0074] The analysis unit can analyze audience reactions by attribute and understand differences in reactions based on age and gender. For example, the analysis unit can analyze audience reactions by age. The analysis unit can also analyze audience reactions by gender. By understanding differences in reactions based on audience attributes, it becomes possible to improve events to suit the target audience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI perform attribute-based analysis.

[0075] The data collection unit can estimate the audience's emotions and adjust the type of data collected based on the estimated emotions. For example, if the audience is excited, the data collection unit may prioritize collecting changes in facial expressions and the number of claps. If the audience is bored, the data collection unit may also collect minimal movement and eye movements. If the audience is focused, the data collection unit may also collect changes in face direction and posture. By adjusting the type of data collected according to the audience's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can analyze past audience reaction data and select the optimal data collection method. For example, the data collection unit can optimize the placement of specific sensors based on reaction data from past events. The data collection unit can also predict changes in reactions during specific time periods based on past reaction data and adjust the data collection method accordingly. For example, the data collection unit can analyze past reaction data and select a data collection method that is effective for a specific audience group. This enables efficient data collection by selecting the optimal data collection method based on past reaction data. 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 past reaction data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can filter the data while considering the audience's seating location information. For example, the data collection unit can prioritize collecting the reactions of audience members in the front rows based on seating location information. The data collection unit can also focus on collecting the reactions of audience members in a specific area, taking seating location information into consideration. The data collection unit can also identify the direction of the audience's gaze based on seating location information and filter the reaction data accordingly. This allows for more accurate data collection by considering the audience's seating location information. 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 seating location information into a generating AI and have the generating AI perform data filtering.

[0078] The data collection unit can estimate the audience's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the audience is excited, the data collection unit may prioritize collecting data on smiles and applause. If the audience is bored, the data collection unit may also prioritize collecting data on eye movements and changes in posture. If the audience is focused, the data collection unit may also prioritize collecting data on facial orientation and changes in facial expression. This allows for the priority collection of important data by prioritizing data according to the audience's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input audience facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the audience's device information during data collection. For example, if an audience member is using a smartphone, the data collection unit will prioritize the collection of the device's sensor information. If an audience member is using a tablet, the data collection unit can also prioritize the collection of screen touch information. If an audience member is using a smartwatch, the data collection unit can also prioritize the collection of heart rate and motion data. This allows for the efficient collection of highly relevant data by considering the audience member's device information. 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 the audience member's device information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit can analyze the audience's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect the content of social media posts made by the audience during the event. The data collection unit can also collect, for example, the audience's social media reactions (likes, shares, etc.). The data collection unit can also collect data considering, for example, the number of followers and influence of the audience's social media accounts. This allows for the efficient collection of relevant data by analyzing the audience's social media activity. 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 the audience's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the audience's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the audience is excited, the analysis unit can apply an analysis algorithm that emphasizes the intensity of the emotion. For example, if the audience is bored, the analysis unit can also apply an analysis algorithm that emphasizes the lack of reaction. For example, if the audience is focused, the analysis unit can also apply an analysis algorithm that emphasizes subtle changes in facial expressions. By adjusting the analysis algorithm according to the audience's emotions, more accurate analysis becomes possible. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience emotion data into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0082] The analysis unit can adjust the level of detail of the analysis based on the intensity of the audience's reaction during the analysis. For example, if the reaction is strong, the analysis unit can perform a detailed analysis and extract detailed data. For example, if the reaction is weak, the analysis unit can perform a simplified analysis and extract only the main data. The analysis unit can also dynamically adjust the accuracy of the analysis according to the intensity of the reaction. This allows for efficient analysis by adjusting the level of detail of the analysis according to the intensity of the audience's reaction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis methods depending on the audience's attribute information during analysis. For example, the analysis unit can apply different analysis methods depending on age group. The analysis unit can also apply different analysis methods depending on gender. The analysis unit can also apply different analysis methods depending on occupation or interests. By applying different analysis methods depending on the audience's attribute information, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI execute the application of different analysis methods.

[0084] 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, if the audience is excited, the analysis unit can provide a visually stimulating display method. For example, if the audience is bored, the analysis unit can also provide a simple and easy-to-read display method. For example, if the audience is attentive, the analysis unit can also provide a display method that includes detailed information. This allows for a highly visible display by adjusting the display method of the analysis results according to 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience emotion data into the generative AI and have the generative AI adjust the display method.

[0085] The analysis unit can determine the priority of analysis based on the timing of audience reactions during the analysis. For example, the analysis unit may prioritize the analysis of reactions during specific time periods. The analysis unit may also prioritize the analysis of reactions at the start or end of an event. For example, the analysis unit may identify peak reaction times and prioritize the analysis of data during those times. This enables efficient analysis by determining the priority of analysis based on the timing of audience reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the priority determination.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the audience during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to past research on audience reactions. For example, the analysis unit can also improve the analysis algorithm by referring to relevant academic papers. For example, the analysis unit can improve the accuracy of its analysis by referring to reaction data from other events. Thus, the accuracy of the analysis is improved by referring to relevant literature on the audience. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0087] The information provider can estimate the audience's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the audience is excited, the provider can provide visually stimulating graphs or charts. If the audience is bored, the provider can also provide simple and easy-to-read graphs or charts. If the audience is attentive, the provider can also provide graphs or charts containing detailed information. This allows for the provision of highly visual information by adjusting the way the information is presented according to 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 processing in the information provider may be performed using AI or not. For example, the information provider can input audience emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0088] The information provider can adjust the level of detail of the information provided based on the importance of the audience's reactions at the time of provision. For example, the provider can provide detailed information for important reactions. For example, the provider can also provide simplified information for less important reactions. The provider can also dynamically adjust the level of detail of the information according to the importance of the reactions. This allows important information to be provided preferentially by adjusting the level of detail of the information according to the importance of the audience's reactions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the information.

[0089] The content delivery unit can apply different delivery methods depending on the audience's attribute information at the time of delivery. For example, the content delivery unit can apply different delivery methods depending on age group. For example, the content delivery unit can also apply different delivery methods depending on gender. For example, the content delivery unit can also apply different delivery methods depending on occupation or interests. This makes it possible to provide more appropriate information by applying different delivery methods depending on the audience's attribute information. Some or all of the above processing in the content delivery unit may be performed using AI, for example, or without using AI. For example, the content delivery unit can input audience attribute data into a generating AI and have the generating AI execute the application of different delivery methods.

[0090] The information provider can estimate the audience's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the audience is excited, the information provider may prioritize providing important information. For example, if the audience is bored, the information provider may prioritize providing visually stimulating information. For example, if the audience is attentive, the information provider may prioritize providing detailed information. In this way, by prioritizing information according to the audience's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input audience emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0091] The information provider can adjust the order of information provided based on the timing of audience reactions. For example, the provider can adjust the order of information based on reactions at a specific time. The provider can also adjust the order of information based on reactions at the start or end of an event. For example, the provider can identify peak reaction times and provide important information during those times. This allows for efficient information provision by adjusting the order of information based on the timing of audience reactions. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the information order.

[0092] The information provider can improve the accuracy of the information it provides by referring to relevant literature for the audience at the time of provision. For example, the information provider can improve the accuracy of the information by referring to past research on audience reactions. For example, the information provider can also improve the accuracy of the information it provides by referring to relevant academic papers. For example, the information provider can improve the accuracy of the information it provides by referring to reaction data from other events. In this way, the accuracy of the information provided is improved by referring to relevant literature for the audience. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input relevant literature data into a generating AI and have the generating AI perform the information accuracy improvement.

[0093] The analysis unit can estimate the audience's emotions and adjust the analysis method based on the estimated emotions. For example, if the audience is excited, the analysis unit may apply an analysis method that emphasizes the intensity of the emotion. For example, if the audience is bored, the analysis unit may apply an analysis method that emphasizes the lack of reaction. For example, if the audience is attentive, the analysis unit may apply an analysis method that emphasizes subtle changes in facial expressions. By adjusting the analysis method according to the audience's emotions, a more accurate analysis becomes possible. 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 processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input audience emotion data into a generative AI and have the generative AI adjust the analysis method.

[0094] The analysis unit can adjust the level of detail of the analysis based on the intensity of the audience's reaction during the analysis. For example, if the reaction is strong, the analysis unit can perform a detailed analysis and extract detailed data. For example, if the reaction is weak, the analysis unit can perform a simplified analysis and extract only the main data. The analysis unit can also dynamically adjust the accuracy of the analysis according to the intensity of the reaction. This allows for efficient analysis by adjusting the level of detail of the analysis according to the intensity of the audience's reaction. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0095] The analysis unit can apply different analysis methods depending on the audience's attribute information during analysis. For example, the analysis unit can apply different analysis methods depending on age group. The analysis unit can also apply different analysis methods depending on gender. The analysis unit can also apply different analysis methods depending on occupation or interests. By applying different analysis methods depending on the audience's attribute information, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience attribute data into a generating AI and have the generating AI execute the application of different analysis methods.

[0096] 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, if the audience is excited, the analysis unit can provide a visually stimulating display method. For example, if the audience is bored, the analysis unit can provide a simple and easy-to-read display method. For example, if the audience is attentive, the analysis unit can provide a display method that includes detailed information. This allows for a highly visible display by adjusting the display method of the analysis results according to 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience emotion data into a generative AI and have the generative AI adjust the display method.

[0097] The analysis unit can determine the priority of analysis based on the timing of audience reactions during the analysis. For example, the analysis unit may prioritize analyzing reactions during specific time periods. The analysis unit may also prioritize analyzing reactions at the start or end of an event. For example, the analysis unit may identify peak reaction times and prioritize analyzing data from those times. This enables efficient analysis by determining the priority of analysis based on the timing of audience reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audience reaction data into a generating AI and have the generating AI perform the priority determination.

[0098] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the audience during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to past research on audience reactions. For example, the analysis unit can also improve the analysis algorithm by referring to relevant academic papers. For example, the analysis unit can improve the accuracy of its analysis by referring to reaction data from other events. Thus, the accuracy of the analysis is improved by referring to relevant literature on the audience. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

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

[0100] The event success evaluation system can also include a feedback collection unit. This unit collects feedback provided by the audience after the event. For example, it can send questionnaires to audience members' smartphones to collect their evaluations and opinions on the event. It can also collect comments and ratings posted by audience members on social media. Furthermore, it can collect feedback provided by audience members in real time during the event. This allows the event success evaluation system to collect audience feedback and evaluate the event's success from a more multifaceted perspective.

[0101] The event success assessment system can also include a prediction unit. The prediction unit predicts the success of future events based on collected data. For example, the prediction unit can analyze past event data to predict audience reactions at future events. It can also analyze current event data in real time to predict future reactions during the event. Furthermore, the prediction unit can predict the reactions of specific audience segments based on audience attribute information. This allows the event success assessment system to predict the success of future events and take proactive measures.

[0102] The event success evaluation system may also include an emotion estimation unit. The emotion estimation unit estimates the emotions of the audience and evaluates the success of the event based on the estimated emotions. For example, the emotion estimation unit can analyze the audience's facial expressions and movements to estimate whether they are enjoying themselves or are bored. It can also analyze the tone and volume of the audience's voices to estimate their level of excitement. Furthermore, the emotion estimation unit can analyze the audience's biometric information (such as heart rate and skin electrical responses) to estimate their emotional state. This allows the event success evaluation system to evaluate the success of the event based on the audience's emotions and provide more accurate feedback.

[0103] The event success evaluation system can also include an interaction section. This interaction section facilitates interaction between the audience and the storyteller. For example, the interaction section can provide a function that allows the audience to post questions in real time. It can also provide a function that allows the audience to participate in voting or surveys. Furthermore, it can provide a function that allows the audience to provide real-time feedback to the storyteller. In this way, the event success evaluation system can facilitate interaction between the audience and the storyteller, thereby supporting the success of the event.

[0104] The event success assessment system can also include a personalization component. This component provides individually optimized content based on audience attribute information and past response data. For example, the personalization component can provide different content depending on the audience's age and gender. It can also provide customized content based on the audience's interests and preferences. Furthermore, it can provide optimal content based on the audience's past response data. This allows the event success assessment system to provide the most suitable content for each individual audience member, thereby supporting the success of the event.

[0105] The event success assessment system can also include an emotional feedback unit. This unit estimates the audience's emotions and provides feedback to the speaker based on those estimates. For example, if the audience is excited, the emotional feedback unit can notify the speaker in real time. It can also notify the speaker if the audience is bored, prompting them to adjust the presentation. Furthermore, if the audience is engaged, the emotional feedback unit can notify the speaker, supporting the smooth progress of the presentation. In this way, the event success assessment system can provide feedback to the speaker based on the audience's emotions, thereby supporting the success of the event.

[0106] The event success evaluation system can also be equipped with a real-time translation unit. This unit translates the storyteller's remarks in real time and provides them to audiences who speak different languages. For example, the real-time translation unit can translate the storyteller's remarks from English to Japanese and provide them to Japanese-speaking audiences. It can also translate the storyteller's remarks from Spanish to English and provide them to English-speaking audiences. Furthermore, the real-time translation unit can simultaneously translate the storyteller's remarks into multiple languages ​​and provide them to audiences who speak different languages. This allows the event success evaluation system to effectively provide information to audiences who speak different languages, thereby supporting the success of the event.

[0107] The event success determination system can also be equipped with an emotion prediction unit. This unit predicts future emotions based on past audience reaction data. For example, it can analyze past event data to predict audience emotions at future events. It can also analyze current event data in real time to predict future emotions during the event. Furthermore, it can predict the emotions of specific audience groups based on audience attribute information. This allows the event success determination system to predict future emotions and take proactive measures.

[0108] The event success determination system can also include a data integration unit. This unit integrates data collected from multiple data sources and performs comprehensive analysis. For example, it can integrate audience facial expression data, motion data, and audio data to analyze overall reactions. It can also integrate audience biometric data and social media data to more accurately understand the audience's emotional state. Furthermore, it can integrate past and current event data to identify factors contributing to event success. This allows the event success determination system to integrate multiple data sources, perform comprehensive analysis, and support event success.

[0109] The event success determination system may also include an emotion adjustment unit. This unit estimates the audience's emotions and adjusts the event's progress based on those estimates. For example, if the audience is excited, the emotion adjustment unit can notify the speaker and encourage them to speed up the pace of the presentation. If the audience is bored, the emotion adjustment unit can notify the speaker and encourage them to change the content of the presentation. Furthermore, if the audience is attentive, the emotion adjustment unit can notify the speaker and encourage them to continue with a detailed explanation. In this way, the event success determination system can adjust the event's progress based on the audience's emotions and support the event's success.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects the audience's reactions. The collection unit uses sensors and cameras to detect the audience's facial expressions and movements. For example, it uses infrared sensors to detect audience movement, HD cameras to capture the audience's facial expressions in high resolution, and audio sensors to detect audience applause and laughter. Step 2: The analysis unit analyzes the reactions collected by the collection unit. The analysis unit compiles data in real time, such as the number of smiles and applause from the audience. For example, it uses facial recognition technology to detect smiles from the audience, speech recognition technology to count the number of applause, and motion analysis technology to analyze the movements of the audience. Step 3: The distribution unit provides the storyteller with the reactions analyzed by the analysis unit. The distribution unit displays the audience reactions in graphs and charts on a monitor placed in front of the storyteller. For example, bar graphs, pie charts, and line graphs are used to visually display the audience reactions. Step 4: The analysis department conducts a multifaceted analysis of the event's success based on the feedback provided by the delivery department. The analysis department analyzes audience reactions over time to identify which parts were particularly well-received. For example, they analyze audience reactions minute by minute or second by second, and analyze audience reactions by attribute to understand differences in reactions by age and gender.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and analysis unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit detects the audience's facial expressions and movements using the camera 42 and sound sensor of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and aggregates the audience's reactions in real time. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and displays the audience's reactions on a monitor placed in front of the storyteller. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs multifaceted analysis based on the collected data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0129] 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.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and analysis 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 detects the audience's facial expressions and movements using the camera 42 and voice sensor of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and aggregates the audience's reactions in real time. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and displays the audience's reactions on a monitor installed in front of the storyteller. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs multifaceted analysis based on the collected data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and analysis unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit detects the audience's facial expressions and movements using the camera 42 and voice sensor of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and aggregates the audience's reactions in real time. The provision unit is implemented in the control unit 46A of the headset terminal 314 and displays the audience's reactions on a monitor placed in front of the storyteller. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs multifaceted analysis based on the collected data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0162] 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.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and analysis unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit detects the facial expressions and movements of the audience using the camera 42 and sound sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and aggregates the audience's reactions in real time. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and displays the audience's reactions on a monitor installed in front of the storyteller. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and performs a multifaceted analysis based on the collected data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A collection department that gathers reactions from the venue, An analysis unit analyzes the reactions collected by the collection unit, A supply unit that provides the reaction analyzed by the analysis unit to the storyteller, The system includes an analysis unit that analyzes the success of an event from multiple perspectives based on the response provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Sensors and cameras are used to detect the facial expressions and movements of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system compiles real-time data on things like the number of smiles and applause from the audience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, A monitor placed in front of the storyteller displays the audience's reactions in graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze audience reactions over time to identify which parts were particularly well-received. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze audience reactions by attribute to understand differences in reactions based on age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the audience's emotions and adjust the types of data we collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past audience reaction data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the data is filtered to take into account the seating location information of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates audience emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the audience's device information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the audience's social media activity is analyzed, and relevant data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the intensity of the audience's reaction. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the audience's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of audience reactions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the audience to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned supply unit is, When providing content, adjust the level of detail of the information provided based on the importance of audience reactions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, different delivery methods will be applied depending on the audience's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned supply unit is, When providing information, adjust the order of the information presented based on the timing of the audience's reactions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we improve the accuracy of the information provided by referring to the audience's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate the audience's emotions and adjust the analysis method based on the estimated audience emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, adjust the level of detail based on the intensity of the audience's reaction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the audience's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is 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 29) The aforementioned analysis unit is During the analysis, the priority of the analysis is determined based on the timing of audience reactions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, we refer to relevant literature related to the audience to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 department that gathers reactions from the venue, An analysis unit analyzes the reactions collected by the collection unit, A supply unit that provides the reaction analyzed by the analysis unit to the storyteller, The system includes an analysis unit that analyzes the success of an event from multiple perspectives based on the response provided by the aforementioned supply unit. A system characterized by the following features.

2. The aforementioned collection unit is Sensors and cameras are used to detect the facial expressions and movements of the audience. The system according to feature 1.

3. The aforementioned analysis unit, The system compiles real-time data on things like the number of smiles and applause from the audience. The system according to feature 1.

4. The aforementioned supply unit is, A monitor placed in front of the storyteller displays the audience's reactions in graphs and charts. The system according to feature 1.

5. The aforementioned analysis unit is Analyze audience reactions over time to identify which parts were particularly well-received. The system according to feature 1.

6. The aforementioned analysis unit is Analyze audience reactions by attribute to understand differences in reactions based on age and gender. The system according to feature 1.

7. The aforementioned collection unit is We estimate the audience's emotions and adjust the types of data we collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past audience reaction data to select the optimal data collection method. The system according to feature 1.

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