system

Abstract

The system according to this embodiment aims to analyze issues that arise during an event in real time and automatically generate specific suggestions based on the actions and reactions of participants. [Solution] The system according to the embodiment comprises an analysis unit, a collection unit, an analysis unit, and a proposal unit. The analysis unit analyzes issues during the event in real time. The collection unit collects participant behavior data and reaction data. The analysis unit analyzes the data collected by the collection unit. The proposal unit automatically generates specific proposals based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0007] The system according to this embodiment can analyze issues that arise during an event in real time and automatically generate specific suggestions based on the participants' actions and reactions. [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 numbered 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] 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 management improvement system according to an embodiment of the present invention is a system that analyzes issues that arise during an event and automatically generates specific suggestions for improving event management based on participant trends and reactions. The event management improvement system uses AI to analyze troubles during the event in real time and collects and analyzes participant behavior data and reaction data. For example, if a technical problem occurs during the event, the AI ​​identifies the cause and quickly proposes a solution. In addition, by analyzing participant trends and reactions, it identifies areas for improvement in event management and automatically generates specific suggestions. First, it analyzes issues that arise during the event in real time. For example, if sound or video problems occur, the AI ​​identifies the cause and quickly proposes a solution. In this case, the AI ​​refers to past data and similar trouble cases to derive the optimal solution. Next, it collects and analyzes participant behavior data and reaction data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. This allows the system to grasp participant trends and reactions and identify areas for improvement in event management. Furthermore, based on the data collected and analyzed by the AI, it automatically generates specific suggestions for event management. For example, the system can suggest incorporating content from sessions that received positive participant feedback into other sessions, or proactively addressing points where technical problems are likely to occur. This is expected to improve the efficiency of event management and increase participant satisfaction. This system targets employees and staff responsible for managing large-scale events both inside and outside the company. The AI ​​automatically identifies the causes of problems that occur during events and generates concrete suggestions for operational improvements in real time based on participant trends and reactions, thereby minimizing event management problems and ensuring smooth operation. In this way, the event management improvement system can analyze issues during events in real time, collect and analyze participant behavior and reaction data, and automatically generate concrete suggestions, which is expected to improve the efficiency of event management and increase participant satisfaction.

[0029] The event management improvement system according to this embodiment comprises an analysis unit, a data collection unit, an analysis unit, and a proposal unit. The analysis unit analyzes issues during the event in real time. For example, if sound or video problems occur, the analysis unit identifies the cause and quickly proposes a solution. The analysis unit can derive the optimal solution by referring to past data and similar trouble cases. The data collection unit collects participant behavior data and reaction data. For example, the data collection unit collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. The data collection unit can collect data in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit grasps participant trends and reactions and finds areas for improvement in event management. Based on participant behavior data and reaction data, the analysis unit can analyze statistical trends and behavioral patterns. The proposal unit automatically generates specific proposals based on the analysis results obtained by the analysis unit. The proposal department makes suggestions such as incorporating content from sessions that received positive participant feedback into other sessions, or proactively addressing points where technical problems are likely to occur. The proposal department can generate specific suggestions using a generation AI. As a result, the event management improvement system according to this embodiment can analyze issues during the event in real time, collect and analyze participant behavior data and reaction data, and automatically generate specific suggestions, thereby improving the efficiency of event management and increasing participant satisfaction.

[0030] The analysis department analyzes issues during the event in real time. For example, if audio or video problems occur, the analysis department identifies the cause and quickly proposes solutions. Specifically, the analysis department monitors data from audio and video systems in real time and immediately issues an alert if an anomaly is detected. In the case of audio problems, the analysis department checks the connection status of microphones and speakers, volume levels, frequency characteristics, etc., to identify the problem area. In the case of video problems, it analyzes the connection status of cameras and projectors, resolution, frame rate, etc., to determine the cause. The analysis department can refer to past data and similar trouble cases to derive the optimal solution. For example, it can search the database for solutions to similar problems that have occurred in the past and quickly present countermeasures. In addition, the analysis department can use AI to learn trouble patterns and use this to predict and prevent future problems. As a result, the analysis department can quickly and accurately resolve technical issues during the event and support smooth event operation.

[0031] The data collection unit collects participant behavior and reaction data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. Specifically, the data collection unit obtains location information, heart rate, and facial recognition data from participants' smartphones and wearable devices. When participants enter a session venue, beacons and RFID tags are used to record their entry and exit times and to determine which sessions they attended. In addition, cameras and microphones are used to collect how participants reacted to specific content. For example, facial recognition technology is used to detect smiles and expressions of surprise, and speech recognition technology is used to record the timing of applause and cheers. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit also collects participant feedback and survey results, and uses this data to evaluate the event and understand areas for improvement. This allows the data collection unit to understand participant behavior and reactions in detail and use this information to improve event management.

[0032] The analysis department analyzes the data collected by the data collection department. For example, the analysis department understands participant trends and reactions and identifies areas for improvement in event management. Specifically, the analysis department can analyze statistical trends and behavioral patterns based on participant behavioral and reaction data. For example, if a large number of participants gather for a particular session, it can be determined that this indicates a high evaluation of the session's content and the instructor. Furthermore, by analyzing participant reaction data, it is possible to understand which content particularly attracted interest and at what point participants' interest increased. The analysis department uses AI to analyze this data and automatically extract participant behavioral patterns and reaction trends. For example, it uses clustering algorithms to group participants based on their interests and behavioral patterns and clarify the characteristics of each group. It also uses methods such as regression analysis and decision trees to identify factors that influence participant reactions and find areas for improvement in event management. As a result, the analysis department can analyze the collected data in detail and propose specific improvements that will help streamline event management and increase participant satisfaction.

[0033] The proposal department automatically generates specific proposals based on the analysis results obtained by the analysis department. For example, the proposal department may suggest incorporating the content of sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. Specifically, the proposal department uses a generation AI to generate specific proposals based on the analysis results. The generation AI uses natural language processing technology to understand the analysis results and generate appropriate proposals in written form. For example, based on participant feedback data, if the content or instructor of a particular session is highly rated, it will suggest incorporating that content into other sessions. It will also generate specific procedures and checklists to take preventative measures against points where technical problems are likely to occur. Furthermore, the proposal department notifies the event management team of the generated proposals to encourage a quick response. The proposal department can use the generation AI to refer to past data and similar cases to derive the optimal proposal. As a result, the proposal department can automatically generate specific proposals based on analysis results, supporting the efficiency of event management and improving participant satisfaction.

[0034] The analysis unit includes a reference unit that refers to past data and similar trouble cases. For example, the analysis unit can search a database of troubles that occurred in past events and find similar trouble cases. The analysis unit can also use similarity calculation to evaluate the similarity between the current trouble and past troubles. This improves the accuracy of the analysis by referring to past data and similar trouble cases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past trouble data into a generating AI and have the generating AI perform a search for similar trouble cases.

[0035] The data collection unit includes a real-time data collection unit that collects data in real time. The data collection unit can, for example, collect participant behavior data and reaction data in real time during an event. The data collection unit can collect data in seconds or minutes and use it for immediate analysis. This allows for an immediate understanding of the situation during an event by collecting data in real time. Some or all of the above-described processing in the real-time data collection unit may be performed using AI, for example, or without AI. For example, the real-time data collection unit can input participant behavior data into a generating AI and have the generating AI perform real-time data collection.

[0036] The analysis unit includes a trend recognition unit that grasps the tendencies and reactions of participants. The analysis unit can, for example, analyze statistical trends and behavioral patterns based on participant behavior data and reaction data. The analysis unit can use data mining techniques and machine learning algorithms to grasp participant tendencies. By doing so, it is possible to identify areas for improvement in event management by understanding participant tendencies and reactions. Some or all of the above processing in the trend recognition unit may be performed using AI, for example, or without AI. For example, the trend recognition unit can input participant behavior data into a generating AI and have the generating AI perform trend recognition.

[0037] The proposal unit includes a proposal generation unit that generates specific proposals. The proposal unit makes suggestions such as incorporating the content of sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. The proposal unit can generate specific proposals using a generation AI. This allows for rapid improvement of event management by generating specific proposals. Some or all of the above-described processes in the proposal generation unit may be performed using AI, for example, or without AI. For example, the proposal generation unit can input analysis results into the generation AI and have the generation AI generate specific proposals.

[0038] The analysis unit, when identifying the cause of technical troubles during an event, refers not only to historical data but also to real-time environmental data. For example, the analysis unit can refer to real-time audio data to identify the cause of audio troubles. The analysis unit can refer to real-time video data to identify the cause of video troubles. The analysis unit can refer to real-time network data to identify the cause of communication troubles. This allows for rapid identification of the cause of technical troubles by referring to real-time environmental data. 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 real-time environmental data into a generating AI and have the generating AI perform the identification of the cause of technical troubles.

[0039] The analysis unit applies different analysis algorithms depending on the type and scale of the event during analysis. For example, for large-scale events, the analysis unit can apply multiple analysis algorithms in parallel to perform rapid analysis. For small-scale events, the analysis unit can apply a simpler analysis algorithm to perform efficient analysis. For events with a specific theme, the analysis unit can apply an analysis algorithm specialized for that theme. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the type and scale of the event. 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 data on the type and scale of the event into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0040] The analysis unit performs its analysis while considering the geographical location information of the event. For example, the analysis unit can analyze participants' movement patterns based on the geographical location information of the event venue. The analysis unit can analyze participants' arrival times based on traffic information around the event venue. The analysis unit can analyze participants' behavior patterns based on weather information for the event venue. By considering geographical location information, participants' behavior patterns can be analyzed more accurately. 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 geographical location information into a generating AI and have the generating AI perform the behavior pattern analysis.

[0041] The analysis unit improves the accuracy of its analysis by referring to relevant literature and past success stories related to the event during the analysis. For example, the analysis unit can compare and evaluate the analysis results of the current event based on past success stories. The analysis unit can refer to relevant literature and apply the latest analysis methods. The analysis unit can analyze the risks of the current event based on past failure stories. In this way, the accuracy of the analysis is improved by referring to relevant literature and past success stories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and past success stories into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0042] The data collection unit selects the optimal data collection method, taking into account the participant's device information, during data collection. For example, the data collection unit can collect GPS data from participants using smartphones. For participants using wearable devices, the data collection unit can collect heart rate data. For participants using personal computers, the data collection unit can collect click data. This allows for optimal data collection by considering the participant'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 participant's device information into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit dynamically changes the type of data it collects according to the progress of the event. For example, at the start of the event, the data collection unit can collect participants' expectations. In the middle of the event, the data collection unit can collect participants' satisfaction levels. At the end of the event, the data collection unit can collect participants' overall evaluations. By dynamically changing the type of data according to the progress of the event, more appropriate data collection becomes possible. 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 event progress data into a generating AI and have the generating AI perform the dynamic change of the type of data to collect.

[0044] The data collection unit analyzes participants' social media activity and collects relevant data during the collection process. For example, the data collection unit can collect and analyze in real time what participants post about the event. The data collection unit can collect posts made by participants using hashtags related to the event. The data collection unit can collect and analyze comments and feedback from participants about the event. This allows for a real-time understanding of participants' reactions by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect the relevant data.

[0045] The data collection unit improves the accuracy of data collection by referring to participants' past event participation history during the collection process. For example, the data collection unit can predict behavioral patterns at the current event based on data from events participants have attended in the past. The data collection unit can predict satisfaction levels at the current event by referring to participants' past feedback. The data collection unit can predict interest levels in specific sessions or content based on participants' past participation history. This improves the accuracy of data collection by referring to past event participation history. 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 participants' past event participation history into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0046] The analysis unit considers participant attribute information (age, gender, occupation, etc.) during analysis. For example, the analysis unit can apply different analysis methods depending on the participant's age group. The analysis unit can apply different analysis methods depending on the participant's gender. The analysis unit can apply different analysis methods depending on the participant's occupation. This allows for more accurate analysis by considering participant attribute information. 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 participant attribute information into a generating AI and have the generating AI perform improvements to the accuracy of the analysis.

[0047] The analysis unit applies different analysis algorithms depending on the type and theme of the event during analysis. For example, for large-scale events, the analysis unit can apply multiple analysis algorithms in parallel to perform rapid analysis. For small-scale events, the analysis unit can apply a simple analysis algorithm to perform efficient analysis. For events with a specific theme, the analysis unit can apply an analysis algorithm specialized for that theme. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the type and theme of the event. 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 data on the type and theme of the event into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0048] The analysis unit considers the geographical location of the event during the analysis. For example, the analysis unit can analyze participants' movement patterns based on the geographical location of the event venue. The analysis unit can analyze participants' arrival times based on traffic information around the event venue. The analysis unit can analyze participants' behavior patterns based on weather information for the event venue. By considering geographical location, participants' behavior patterns can be analyzed more accurately. 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 geographical location information into a generating AI and have the generating AI perform the behavior pattern analysis.

[0049] The analysis department improves the accuracy of its analysis by referring to relevant literature and past success stories related to the event. For example, the analysis department can compare and evaluate the analysis results of the current event based on past success stories. The analysis department can refer to relevant literature and apply the latest analytical methods. The analysis department can analyze the risks of the current event based on past failure stories. This improves the accuracy of the analysis by referring to relevant literature and past success stories. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant literature and past success stories into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0050] The proposal unit generates the optimal proposal by referring to participants' past response data when generating proposals. For example, the proposal unit can generate proposals for the current event based on content that participants have previously responded favorably to. The proposal unit can generate proposals that avoid points that participants have previously expressed dissatisfaction with. The proposal unit can analyze participants' past response data and generate the most effective proposals. In this way, the optimal proposal is generated by referring to past response data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input participants' past response data into a generation AI and have the generation AI perform the generation of the optimal proposal.

[0051] The proposal unit applies different proposal algorithms depending on the type and theme of the event when generating proposals. For example, for large-scale events, the proposal unit can apply multiple proposal algorithms in parallel to generate proposals quickly. For small-scale events, the proposal unit can apply a simple proposal algorithm to generate efficient proposals. For events with a specific theme, the proposal unit can apply a proposal algorithm specialized for that theme. This improves the accuracy of proposals by applying proposal algorithms appropriate to the type and theme of the event. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the type and theme of the event into a generation AI and have the generation AI select an appropriate proposal algorithm.

[0052] The proposal unit considers the geographical location of the event when generating proposals. For example, the proposal unit can make proposals that consider participants' travel patterns based on the geographical location of the event venue. The proposal unit can make proposals that consider participants' arrival times based on traffic information around the event venue. The proposal unit can make proposals that consider participants' behavior patterns based on weather information around the event venue. By considering geographical location information, it becomes possible to make proposals that more accurately understand participants' behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generation AI and have the generation AI perform the generation of proposals.

[0053] The proposal generation unit improves the accuracy of its proposals by referring to relevant literature and past success stories related to the event. For example, the proposal unit can generate proposals for the current event based on past success stories. The proposal unit can refer to relevant literature and apply the latest proposal methodologies. The proposal unit can generate proposals to avoid risks in the current event based on past failures. In this way, the accuracy of the proposals is improved by referring to relevant literature and past success stories. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevant literature and past success stories into a generation AI and have the generation AI perform the improvement of proposal accuracy.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can refer to real-time environmental data as well as historical data when identifying the cause of technical problems during an event. For example, it can refer to real-time audio data to identify the cause of audio problems. It can also refer to real-time video data to identify the cause of video problems. Furthermore, it can refer to real-time network data to identify the cause of communication problems. This allows for rapid identification of the cause of technical problems by referring to real-time environmental data.

[0056] The analysis unit can apply different analysis algorithms depending on the type and scale of the event during analysis. For example, for large-scale events, multiple analysis algorithms can be applied in parallel to perform rapid analysis. For small-scale events, a simpler analysis algorithm can be applied for efficient analysis. Furthermore, for events with a specific theme, an analysis algorithm specialized for that theme can be applied. In this way, the accuracy of the analysis is improved by applying an analysis algorithm appropriate to the type and scale of the event.

[0057] The data collection unit can select the optimal data collection method by considering the participant's device information during collection. For example, GPS data can be collected from participants using smartphones. Heart rate data can be collected from participants using wearable devices. Furthermore, click data can be collected from participants using personal computers. This allows for optimal data collection by considering the participant's device information.

[0058] The data collection unit can dynamically change the type of data collected according to the progress of the event. For example, at the start of the event, participant expectations can be collected. In the middle of the event, participant satisfaction levels can be collected. Furthermore, at the end of the event, overall participant evaluations can be collected. By dynamically changing the type of data according to the progress of the event, more appropriate data collection becomes possible.

[0059] The proposal function can apply different proposal algorithms depending on the type and theme of the event during proposal generation. For example, for large-scale events, multiple proposal algorithms can be applied in parallel to generate proposals quickly. For smaller events, a simpler proposal algorithm can be applied for efficient proposal generation. Furthermore, for events with a specific theme, a proposal algorithm tailored to that theme can be applied. This improves the accuracy of proposals by applying proposal algorithms appropriate to the type and theme of the event.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes issues during the event in real time. For example, if audio or video problems occur, it identifies the cause and quickly proposes a solution. The analysis unit can refer to past data and similar trouble cases to derive the optimal solution. Step 2: The data collection unit collects participant behavioral and response data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they responded. The data collection unit can collect data in real time. Step 3: The analysis department analyzes the data collected by the data collection department. For example, they understand participant trends and reactions and identify areas for improvement in event management. Based on participant behavioral and reaction data, the analysis department can analyze statistical trends and behavioral patterns. Step 4: The proposal department automatically generates specific proposals based on the analysis results obtained by the analysis department. For example, it may suggest incorporating content from sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. The proposal department can generate specific proposals using a generation AI.

[0062] (Example of form 2) The event management improvement system according to an embodiment of the present invention is a system that analyzes issues that arise during an event and automatically generates specific suggestions for improving event management based on participant trends and reactions. The event management improvement system uses AI to analyze troubles during the event in real time and collects and analyzes participant behavior data and reaction data. For example, if a technical problem occurs during the event, the AI ​​identifies the cause and quickly proposes a solution. In addition, by analyzing participant trends and reactions, it identifies areas for improvement in event management and automatically generates specific suggestions. First, it analyzes issues that arise during the event in real time. For example, if sound or video problems occur, the AI ​​identifies the cause and quickly proposes a solution. In this case, the AI ​​refers to past data and similar trouble cases to derive the optimal solution. Next, it collects and analyzes participant behavior data and reaction data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. This allows the system to grasp participant trends and reactions and identify areas for improvement in event management. Furthermore, based on the data collected and analyzed by the AI, it automatically generates specific suggestions for event management. For example, the system can suggest incorporating content from sessions that received positive participant feedback into other sessions, or proactively addressing points where technical problems are likely to occur. This is expected to improve the efficiency of event management and increase participant satisfaction. This system targets employees and staff responsible for managing large-scale events both inside and outside the company. The AI ​​automatically identifies the causes of problems that occur during events and generates concrete suggestions for operational improvements in real time based on participant trends and reactions, thereby minimizing event management problems and ensuring smooth operation. In this way, the event management improvement system can analyze issues during events in real time, collect and analyze participant behavior and reaction data, and automatically generate concrete suggestions, which is expected to improve the efficiency of event management and increase participant satisfaction.

[0063] The event management improvement system according to this embodiment comprises an analysis unit, a data collection unit, an analysis unit, and a proposal unit. The analysis unit analyzes issues during the event in real time. For example, if sound or video problems occur, the analysis unit identifies the cause and quickly proposes a solution. The analysis unit can derive the optimal solution by referring to past data and similar trouble cases. The data collection unit collects participant behavior data and reaction data. For example, the data collection unit collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. The data collection unit can collect data in real time. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit grasps participant trends and reactions and finds areas for improvement in event management. Based on participant behavior data and reaction data, the analysis unit can analyze statistical trends and behavioral patterns. The proposal unit automatically generates specific proposals based on the analysis results obtained by the analysis unit. The proposal department makes suggestions such as incorporating content from sessions that received positive participant feedback into other sessions, or proactively addressing points where technical problems are likely to occur. The proposal department can generate specific suggestions using a generation AI. As a result, the event management improvement system according to this embodiment can analyze issues during the event in real time, collect and analyze participant behavior data and reaction data, and automatically generate specific suggestions, thereby improving the efficiency of event management and increasing participant satisfaction.

[0064] The analysis department analyzes issues during the event in real time. For example, if audio or video problems occur, the analysis department identifies the cause and quickly proposes solutions. Specifically, the analysis department monitors data from audio and video systems in real time and immediately issues an alert if an anomaly is detected. In the case of audio problems, the analysis department checks the connection status of microphones and speakers, volume levels, frequency characteristics, etc., to identify the problem area. In the case of video problems, it analyzes the connection status of cameras and projectors, resolution, frame rate, etc., to determine the cause. The analysis department can refer to past data and similar trouble cases to derive the optimal solution. For example, it can search the database for solutions to similar problems that have occurred in the past and quickly present countermeasures. In addition, the analysis department can use AI to learn trouble patterns and use this to predict and prevent future problems. As a result, the analysis department can quickly and accurately resolve technical issues during the event and support smooth event operation.

[0065] The data collection unit collects participant behavior and reaction data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they reacted. Specifically, the data collection unit obtains location information, heart rate, and facial recognition data from participants' smartphones and wearable devices. When participants enter a session venue, beacons and RFID tags are used to record their entry and exit times and to determine which sessions they attended. In addition, cameras and microphones are used to collect how participants reacted to specific content. For example, facial recognition technology is used to detect smiles and expressions of surprise, and speech recognition technology is used to record the timing of applause and cheers. The data collection unit collects this data in real time and transmits it to a central database. Furthermore, the data collection unit also collects participant feedback and survey results, and uses this data to evaluate the event and understand areas for improvement. This allows the data collection unit to understand participant behavior and reactions in detail and use this information to improve event management.

[0066] The analysis department analyzes the data collected by the data collection department. For example, the analysis department understands participant trends and reactions and identifies areas for improvement in event management. Specifically, the analysis department can analyze statistical trends and behavioral patterns based on participant behavioral and reaction data. For example, if a large number of participants gather for a particular session, it can be determined that this indicates a high evaluation of the session's content and the instructor. Furthermore, by analyzing participant reaction data, it is possible to understand which content particularly attracted interest and at what point participants' interest increased. The analysis department uses AI to analyze this data and automatically extract participant behavioral patterns and reaction trends. For example, it uses clustering algorithms to group participants based on their interests and behavioral patterns and clarify the characteristics of each group. It also uses methods such as regression analysis and decision trees to identify factors that influence participant reactions and find areas for improvement in event management. As a result, the analysis department can analyze the collected data in detail and propose specific improvements that will help streamline event management and increase participant satisfaction.

[0067] The proposal department automatically generates specific proposals based on the analysis results obtained by the analysis department. For example, the proposal department may suggest incorporating the content of sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. Specifically, the proposal department uses a generation AI to generate specific proposals based on the analysis results. The generation AI uses natural language processing technology to understand the analysis results and generate appropriate proposals in written form. For example, based on participant feedback data, if the content or instructor of a particular session is highly rated, it will suggest incorporating that content into other sessions. It will also generate specific procedures and checklists to take preventative measures against points where technical problems are likely to occur. Furthermore, the proposal department notifies the event management team of the generated proposals to encourage a quick response. The proposal department can use the generation AI to refer to past data and similar cases to derive the optimal proposal. As a result, the proposal department can automatically generate specific proposals based on analysis results, supporting the efficiency of event management and improving participant satisfaction.

[0068] The analysis unit includes a reference unit that refers to past data and similar trouble cases. For example, the analysis unit can search a database of troubles that occurred in past events and find similar trouble cases. The analysis unit can also use similarity calculation to evaluate the similarity between the current trouble and past troubles. This improves the accuracy of the analysis by referring to past data and similar trouble cases. Some or all of the above processing in the reference unit may be performed using AI, for example, or without AI. For example, the reference unit can input past trouble data into a generating AI and have the generating AI perform a search for similar trouble cases.

[0069] The data collection unit includes a real-time data collection unit that collects data in real time. The data collection unit can, for example, collect participant behavior data and reaction data in real time during an event. The data collection unit can collect data in seconds or minutes and use it for immediate analysis. This allows for an immediate understanding of the situation during an event by collecting data in real time. Some or all of the above-described processing in the real-time data collection unit may be performed using AI, for example, or without AI. For example, the real-time data collection unit can input participant behavior data into a generating AI and have the generating AI perform real-time data collection.

[0070] The analysis unit includes a trend recognition unit that grasps the tendencies and reactions of participants. The analysis unit can, for example, analyze statistical trends and behavioral patterns based on participant behavior data and reaction data. The analysis unit can use data mining techniques and machine learning algorithms to grasp participant tendencies. By doing so, it is possible to identify areas for improvement in event management by understanding participant tendencies and reactions. Some or all of the above processing in the trend recognition unit may be performed using AI, for example, or without AI. For example, the trend recognition unit can input participant behavior data into a generating AI and have the generating AI perform trend recognition.

[0071] The proposal unit includes a proposal generation unit that generates specific proposals. The proposal unit makes suggestions such as incorporating the content of sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. The proposal unit can generate specific proposals using a generation AI. This allows for rapid improvement of event management by generating specific proposals. Some or all of the above-described processes in the proposal generation unit may be performed using AI, for example, or without AI. For example, the proposal generation unit can input analysis results into the generation AI and have the generation AI generate specific proposals.

[0072] The analysis unit estimates the emotions of participants and adjusts the priority of analysis based on the estimated emotions. For example, if a participant is feeling dissatisfied, the AI ​​in the analysis unit detects that emotion and prioritizes the analysis of related problems. If a participant is excited, the AI ​​in the analysis unit detects that emotion and prioritizes analysis to elicit a positive response. If a participant is tired, the AI ​​in the analysis unit detects that emotion and prioritizes analysis for rest or refreshment. By adjusting the priority of analysis based on the emotions of participants, more effective analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input participant emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit, when identifying the cause of technical troubles during an event, refers not only to historical data but also to real-time environmental data. For example, the analysis unit can refer to real-time audio data to identify the cause of audio troubles. The analysis unit can refer to real-time video data to identify the cause of video troubles. The analysis unit can refer to real-time network data to identify the cause of communication troubles. This allows for rapid identification of the cause of technical troubles by referring to real-time environmental data. 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 real-time environmental data into a generating AI and have the generating AI perform the identification of the cause of technical troubles.

[0074] The analysis unit applies different analysis algorithms depending on the type and scale of the event during analysis. For example, for large-scale events, the analysis unit can apply multiple analysis algorithms in parallel to perform rapid analysis. For small-scale events, the analysis unit can apply a simpler analysis algorithm to perform efficient analysis. For events with a specific theme, the analysis unit can apply an analysis algorithm specialized for that theme. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the type and scale of the event. 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 data on the type and scale of the event into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0075] The analysis unit estimates the participant's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if a participant is feeling dissatisfied, the analysis unit can display the analysis results in a simple and easy-to-understand format. If a participant is excited, the analysis unit can display the analysis results in detail to elicit a positive response. If a participant is tired, the analysis unit can display the analysis results in a visually relaxing format. This allows for the provision of more appropriate information by adjusting the display method of the analysis results based on the participant's emotions. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input participant emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit performs its analysis while considering the geographical location information of the event. For example, the analysis unit can analyze participants' movement patterns based on the geographical location information of the event venue. The analysis unit can analyze participants' arrival times based on traffic information around the event venue. The analysis unit can analyze participants' behavior patterns based on weather information for the event venue. By considering geographical location information, participants' behavior patterns can be analyzed more accurately. 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 geographical location information into a generating AI and have the generating AI perform the behavior pattern analysis.

[0077] The analysis unit improves the accuracy of its analysis by referring to relevant literature and past success stories related to the event during the analysis. For example, the analysis unit can compare and evaluate the analysis results of the current event based on past success stories. The analysis unit can refer to relevant literature and apply the latest analysis methods. The analysis unit can analyze the risks of the current event based on past failure stories. In this way, the accuracy of the analysis is improved by referring to relevant literature and past success stories. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and past success stories into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0078] The data collection unit estimates the emotions of participants and adjusts the timing of data collection based on the estimated emotions. For example, if a participant is excited, the data collection unit can collect data in real time to grasp their immediate reaction. If a participant is relaxed, the data collection unit can collect data at regular intervals to grasp long-term trends. If a participant is dissatisfied, the data collection unit can collect data frequently to aim for early detection of problems. This allows for more effective data collection by adjusting the timing of data collection based on the emotions of participants. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input participant emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0079] The data collection unit selects the optimal data collection method, taking into account the participant's device information, during data collection. For example, the data collection unit can collect GPS data from participants using smartphones. For participants using wearable devices, the data collection unit can collect heart rate data. For participants using personal computers, the data collection unit can collect click data. This allows for optimal data collection by considering the participant'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 participant's device information into a generating AI and have the generating AI select the optimal data collection method.

[0080] The data collection unit dynamically changes the type of data it collects according to the progress of the event. For example, at the start of the event, the data collection unit can collect participants' expectations. In the middle of the event, the data collection unit can collect participants' satisfaction levels. At the end of the event, the data collection unit can collect participants' overall evaluations. By dynamically changing the type of data according to the progress of the event, more appropriate data collection becomes possible. 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 event progress data into a generating AI and have the generating AI perform the dynamic change of the type of data to collect.

[0081] The data collection unit estimates the participants' emotions and prioritizes the data to be collected based on the estimated emotions. For example, if a participant is feeling dissatisfied, the data collection unit can prioritize collecting data related to the cause of that dissatisfaction. If a participant is excited, the data collection unit can prioritize collecting data related to their positive reactions. If a participant is tired, the data collection unit can prioritize collecting data related to rest and refreshment. This allows for the priority collection of important data by prioritizing data based on the participants' 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. For example, the data collection unit can input participant emotion data into a generative AI and have the generative AI perform data prioritization.

[0082] The data collection unit analyzes participants' social media activity and collects relevant data during the collection process. For example, the data collection unit can collect and analyze in real time what participants post about the event. The data collection unit can collect posts made by participants using hashtags related to the event. The data collection unit can collect and analyze comments and feedback from participants about the event. This allows for a real-time understanding of participants' reactions by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect the relevant data.

[0083] The data collection unit improves the accuracy of data collection by referring to participants' past event participation history during the collection process. For example, the data collection unit can predict behavioral patterns at the current event based on data from events participants have attended in the past. The data collection unit can predict satisfaction levels at the current event by referring to participants' past feedback. The data collection unit can predict interest levels in specific sessions or content based on participants' past participation history. This improves the accuracy of data collection by referring to past event participation history. 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 participants' past event participation history into a generating AI and have the generating AI perform the task of improving the accuracy of data collection.

[0084] The analysis unit estimates the emotions of participants and adjusts the priority of the analysis based on the estimated emotions. For example, if a participant is feeling dissatisfied, the analysis unit can prioritize analyses to identify the cause. If a participant is excited, the analysis unit can prioritize analyses to elicit a positive response. If a participant is tired, the analysis unit can prioritize analyses for rest and refreshment. By adjusting the priority of analyses based on the emotions of participants, more effective analyses become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input participant emotion data into a generative AI and have the generative AI perform the adjustment of analysis priorities.

[0085] The analysis unit considers participant attribute information (age, gender, occupation, etc.) during analysis. For example, the analysis unit can apply different analysis methods depending on the participant's age group. The analysis unit can apply different analysis methods depending on the participant's gender. The analysis unit can apply different analysis methods depending on the participant's occupation. This allows for more accurate analysis by considering participant attribute information. 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 participant attribute information into a generating AI and have the generating AI perform improvements to the accuracy of the analysis.

[0086] The analysis unit applies different analysis algorithms depending on the type and theme of the event during analysis. For example, for large-scale events, the analysis unit can apply multiple analysis algorithms in parallel to perform rapid analysis. For small-scale events, the analysis unit can apply a simple analysis algorithm to perform efficient analysis. For events with a specific theme, the analysis unit can apply an analysis algorithm specialized for that theme. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the type and theme of the event. 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 data on the type and theme of the event into a generating AI and have the generating AI select an appropriate analysis algorithm.

[0087] The analysis unit estimates the emotions of participants and adjusts how the analysis results are displayed based on the estimated emotions. For example, if a participant is feeling dissatisfied, the analysis unit can display the analysis results in a simple and easy-to-understand format. If a participant is feeling excited, the analysis unit can display the analysis results in detail to elicit a positive response. If a participant is feeling tired, the analysis unit can display the analysis results in a visually relaxing format. By adjusting how the analysis results are displayed based on the emotions of participants, more appropriate information can be provided. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input participant emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit considers the geographical location of the event during the analysis. For example, the analysis unit can analyze participants' movement patterns based on the geographical location of the event venue. The analysis unit can analyze participants' arrival times based on traffic information around the event venue. The analysis unit can analyze participants' behavior patterns based on weather information for the event venue. By considering geographical location, participants' behavior patterns can be analyzed more accurately. 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 geographical location information into a generating AI and have the generating AI perform the behavior pattern analysis.

[0089] The analysis department improves the accuracy of its analysis by referring to relevant literature and past success stories related to the event. For example, the analysis department can compare and evaluate the analysis results of the current event based on past success stories. The analysis department can refer to relevant literature and apply the latest analytical methods. The analysis department can analyze the risks of the current event based on past failure stories. This improves the accuracy of the analysis by referring to relevant literature and past success stories. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant literature and past success stories into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0090] The suggestion unit estimates the participants' emotions and prioritizes suggestions based on those estimated emotions. For example, if a participant is dissatisfied, the suggestion unit can prioritize suggestions to resolve the cause of their dissatisfaction. If a participant is excited, the suggestion unit can prioritize suggestions to elicit a positive response. If a participant is tired, the suggestion unit can prioritize suggestions for rest or refreshment. By prioritizing suggestions based on participants' emotions, more effective suggestions become possible. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input participant emotion data into a generative AI and have the generative AI perform the suggestion prioritization.

[0091] The proposal unit generates the optimal proposal by referring to participants' past response data when generating proposals. For example, the proposal unit can generate proposals for the current event based on content that participants have previously responded favorably to. The proposal unit can generate proposals that avoid points that participants have previously expressed dissatisfaction with. The proposal unit can analyze participants' past response data and generate the most effective proposals. In this way, the optimal proposal is generated by referring to past response data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input participants' past response data into a generation AI and have the generation AI perform the generation of the optimal proposal.

[0092] The proposal unit applies different proposal algorithms depending on the type and theme of the event when generating proposals. For example, for large-scale events, the proposal unit can apply multiple proposal algorithms in parallel to generate proposals quickly. For small-scale events, the proposal unit can apply a simple proposal algorithm to generate efficient proposals. For events with a specific theme, the proposal unit can apply a proposal algorithm specialized for that theme. This improves the accuracy of proposals by applying proposal algorithms appropriate to the type and theme of the event. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the type and theme of the event into a generation AI and have the generation AI select an appropriate proposal algorithm.

[0093] The suggestion unit estimates the participant's emotions and adjusts how the suggestions are displayed based on the estimated emotions. For example, if a participant is feeling dissatisfied, the suggestion unit can display the suggestions in a simple and easy-to-understand format. If a participant is excited, the suggestion unit can display the suggestions in detail to elicit a positive response. If a participant is tired, the suggestion unit can display the suggestions in a visually relaxing format. This allows for more appropriate information to be provided by adjusting how suggestions are displayed based on the participant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input participant emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The proposal unit considers the geographical location of the event when generating proposals. For example, the proposal unit can make proposals that consider participants' travel patterns based on the geographical location of the event venue. The proposal unit can make proposals that consider participants' arrival times based on traffic information around the event venue. The proposal unit can make proposals that consider participants' behavior patterns based on weather information around the event venue. By considering geographical location information, it becomes possible to make proposals that more accurately understand participants' behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input geographical location information into a generation AI and have the generation AI perform the generation of proposals.

[0095] The proposal generation unit improves the accuracy of its proposals by referring to relevant literature and past success stories related to the event. For example, the proposal unit can generate proposals for the current event based on past success stories. The proposal unit can refer to relevant literature and apply the latest proposal methodologies. The proposal unit can generate proposals to avoid risks in the current event based on past failures. In this way, the accuracy of the proposals is improved by referring to relevant literature and past success stories. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input relevant literature and past success stories into a generation AI and have the generation AI perform the improvement of proposal accuracy.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate participants' emotions when analyzing issues during an event in real time, and adjust the analysis priority based on those estimated emotions. For example, if a participant is feeling dissatisfied, the AI ​​can detect that emotion and prioritize the analysis of related problems. Similarly, if a participant is excited, the AI ​​can detect that emotion and prioritize analysis aimed at eliciting positive responses. Furthermore, if a participant is tired, the AI ​​can detect that emotion and prioritize analysis related to rest and refreshment. By adjusting the analysis priority based on participants' emotions, more effective analysis becomes possible.

[0098] The data collection unit can estimate participants' emotions and adjust the timing of data collection based on those estimates. For example, if a participant is excited, data can be collected in real time to immediately understand their reaction. If a participant is relaxed, data can be collected at regular intervals to understand long-term trends. Furthermore, if a participant is dissatisfied, data can be collected frequently to aim for early detection of problems. By adjusting the timing of data collection based on participants' emotions, more effective data collection becomes possible.

[0099] The analysis unit can estimate participants' emotions and adjust the priority of the analysis based on those estimated emotions. For example, if a participant is feeling dissatisfied, the analysis can be prioritized to identify the cause. If a participant is excited, the analysis can be prioritized to elicit a positive response. Furthermore, if a participant is tired, the analysis can be prioritized to encourage rest and refreshment. By adjusting the priority of the analysis based on participants' emotions, more effective analysis becomes possible.

[0100] The proposal team can estimate participants' emotions and prioritize proposals based on those estimates. For example, if a participant is dissatisfied, proposals that address the cause of their dissatisfaction can be prioritized. If a participant is excited, proposals that elicit a positive response can be prioritized. Furthermore, if a participant is tired, proposals for rest or refreshment can be prioritized. By prioritizing proposals based on participants' emotions, more effective proposals can be made.

[0101] The proposal section can estimate participants' emotions and adjust how proposals are displayed based on those estimates. For example, if a participant is feeling dissatisfied, the proposal can be displayed in a simple and easy-to-understand format. If a participant is excited, the proposal can be displayed in detail to elicit a positive response. Furthermore, if a participant is tired, the proposal can be displayed in a visually relaxing format. By adjusting how proposals are displayed based on participants' emotions, more appropriate information can be provided.

[0102] The analysis unit can refer to real-time environmental data as well as historical data when identifying the cause of technical problems during an event. For example, it can refer to real-time audio data to identify the cause of audio problems. It can also refer to real-time video data to identify the cause of video problems. Furthermore, it can refer to real-time network data to identify the cause of communication problems. This allows for rapid identification of the cause of technical problems by referring to real-time environmental data.

[0103] The analysis unit can apply different analysis algorithms depending on the type and scale of the event during analysis. For example, for large-scale events, multiple analysis algorithms can be applied in parallel to perform rapid analysis. For small-scale events, a simpler analysis algorithm can be applied for efficient analysis. Furthermore, for events with a specific theme, an analysis algorithm specialized for that theme can be applied. In this way, the accuracy of the analysis is improved by applying an analysis algorithm appropriate to the type and scale of the event.

[0104] The data collection unit can select the optimal data collection method by considering the participant's device information during collection. For example, GPS data can be collected from participants using smartphones. Heart rate data can be collected from participants using wearable devices. Furthermore, click data can be collected from participants using personal computers. This allows for optimal data collection by considering the participant's device information.

[0105] The data collection unit can dynamically change the type of data collected according to the progress of the event. For example, at the start of the event, participant expectations can be collected. In the middle of the event, participant satisfaction levels can be collected. Furthermore, at the end of the event, overall participant evaluations can be collected. By dynamically changing the type of data according to the progress of the event, more appropriate data collection becomes possible.

[0106] The proposal function can apply different proposal algorithms depending on the type and theme of the event during proposal generation. For example, for large-scale events, multiple proposal algorithms can be applied in parallel to generate proposals quickly. For smaller events, a simpler proposal algorithm can be applied for efficient proposal generation. Furthermore, for events with a specific theme, a proposal algorithm tailored to that theme can be applied. This improves the accuracy of proposals by applying proposal algorithms appropriate to the type and theme of the event.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes issues during the event in real time. For example, if audio or video problems occur, it identifies the cause and quickly proposes a solution. The analysis unit can refer to past data and similar trouble cases to derive the optimal solution. Step 2: The data collection unit collects participant behavioral and response data. For example, it collects data such as which sessions participants attended, which content they showed interest in, and when they responded. The data collection unit can collect data in real time. Step 3: The analysis department analyzes the data collected by the data collection department. For example, they understand participant trends and reactions and identify areas for improvement in event management. Based on participant behavioral and reaction data, the analysis department can analyze statistical trends and behavioral patterns. Step 4: The proposal department automatically generates specific proposals based on the analysis results obtained by the analysis department. For example, it may suggest incorporating content from sessions that received positive participant feedback into other sessions, or taking preventative measures against points where technical problems are likely to occur. The proposal department can generate specific proposals using a generation AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the analysis unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the issues during the event in real time. The collection unit collects participant behavior data and reaction data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates specific proposals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the challenges during the event in real time. The collection unit collects participant behavior data and reaction data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates specific proposals. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the issues during the event in real time. The collection unit collects participant behavior data and reaction data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates specific proposals. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the analysis unit, collection unit, analysis unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the tasks during the event in real time. The collection unit collects participant behavior data and reaction data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates specific proposals. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The analysis unit analyzes issues during the event in real time, A data collection unit that collects participant behavioral data and reaction data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that automatically generates specific proposals based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a reference section that refers to past data and similar trouble cases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It is equipped with a real-time data collection unit that collects data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is It includes a trend analysis unit to understand the tendencies and reactions of participants. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It includes a proposal generation unit that generates specific proposals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate the emotions of the participants and adjust the analysis priorities based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When identifying the cause of technical problems during an event, we refer to real-time environmental data as well as historical data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type and scale of the event. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the emotions of the participants and adjusts how the analysis results are displayed based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, the geographical location information of the event is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to relevant literature and past success stories related to the event. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the participants' emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the optimal data collection method is selected, taking into account the participants' device information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the type of data collected is dynamically changed according to the progress of the event. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is We estimate the emotions of the participants and prioritize the data to collect based on the estimated emotions of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, we analyze participants' social media activity and gather relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is During data collection, we improve the accuracy of data collection by referring to participants' past event participation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the participants' emotions and adjust the analysis priorities based on the estimated participants' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When performing the analysis, the participant's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the type and theme of the event. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is The system estimates the participants' emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During the analysis, the geographical location of the events will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During the analysis, we improve the accuracy of the analysis by referring to relevant literature and past success stories related to the event. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, The system estimates the participants' emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When generating proposals, the system references participants' past response data to generate the most suitable proposals. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When generating proposals, different proposal algorithms are applied depending on the type and theme of the event. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, The system estimates the participants' emotions and adjusts how suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When generating proposals, the geographical location information of the event is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When generating proposals, we improve the accuracy of the proposals by referring to relevant literature and past success stories related to the event. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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. The analysis unit analyzes issues during the event in real time, A data collection unit that collects participant behavioral data and reaction data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that automatically generates specific proposals based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, It includes a reference section that refers to past data and similar trouble cases. The system according to feature 1.

3. The aforementioned collection unit is It is equipped with a real-time data collection unit that collects data in real time. The system according to feature 1.

4. The aforementioned analysis unit is It includes a trend analysis unit to understand the tendencies and reactions of participants. The system according to feature 1.

5. The aforementioned proposal section is, It includes a proposal generation unit that generates specific proposals. The system according to feature 1.

6. The aforementioned analysis unit, We estimate the emotions of the participants and adjust the analysis priorities based on the estimated emotions of the participants. The system according to feature 1.

7. The aforementioned analysis unit, When identifying the cause of technical problems during an event, we refer to real-time environmental data as well as historical data. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type and scale of the event. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the emotions of the participants and adjusts how the analysis results are displayed based on the estimated emotions of the participants. The system according to feature 1.

10. The aforementioned analysis unit, During the analysis, the geographical location information of the event is taken into consideration. The system according to feature 1.

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