System

Abstract

An object of the system according to the embodiment is to efficiently perform from planning of an event to effect verification.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a collection unit, and a verification unit. The reception unit selects a type of an event. The generation unit automatically generates a plan according to the event selected by the reception unit. The collection unit collects the reaction of the participant in real time during the progress of the event based on the plan generated by the generation unit. The verification unit performs effect verification after the event ends based on the reaction data collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process from planning an event to verifying its effectiveness was time-consuming and laborious, making it difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently carry out the process from planning an event to verifying its effectiveness. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a collection unit, and a verification unit. The reception unit selects the type of event. The generation unit automatically generates a plan according to the event selected by the reception unit. The collection unit collects participant responses in real time while the event is in progress based on the plan generated by the generation unit. The verification unit verifies the effectiveness after the event ends based on the response data collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out the process from planning an event to verifying its effectiveness. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An online event production system according to an embodiment of the present invention is a system that consistently handles the entire process from planning an online event to verifying its effectiveness. In this online event production system, a user selects the type of online event, and the system automatically generates a plan for the selected event. The system collects participant responses in real time as the event progresses, and then performs a verification of the event's effectiveness after the event ends. This mechanism enables cost-effective production of online events. For example, in the online event production system, a user selects the type of online event. The user then inputs detailed information such as the purpose of the event and the attributes of the participants. For example, in the case of an all-staff meeting, the user inputs the participants' job titles and departments, as well as the purpose of the event (e.g., a performance report or policy explanation). This information is entered into the system. Next, the online event production system automatically generates a plan for the selected event based on the input information. The system analyzes past event data and participant attribute information to propose an optimal plan. For example, in the case of an all-staff meeting, the system automatically generates a performance report presentation, slides for policy explanations, and a discussion session for participants to exchange opinions. Furthermore, the online event production system collects participant responses in real time as the event progresses. For example, the system analyzes participants' facial expressions, comments, chat content, etc. to understand the progress of the event. This allows necessary adjustments to be made as the event progresses. Finally, the online event production system evaluates the effectiveness of the event after it ends. The system analyzes participants' reaction data and survey results to evaluate the effectiveness of the event. For example, it evaluates participants' satisfaction, level of understanding, and active exchange of opinions, and suggests improvements for the next event. This enables the online event production system to produce cost-effective online events. This allows users to leave complex planning and progress management to the system, resulting in the realization of an effective event.

[0029] An online event production system according to an embodiment includes a reception unit, a generation unit, a collection unit, and a verification unit. The reception unit allows a user to select the type of online event. The user can select the type of event, for example, an all-staff general meeting, an award ceremony, a job offer ceremony / entrance ceremony, a rally / kickoff, or an internal social gathering / networking event. The reception unit allows the user to input detailed information such as the purpose of the event and the attributes of the participants. For example, in the case of an all-staff general meeting, the user can input the participants' positions and departments, and the purpose of the event (e.g., a performance report or policy explanation). The generation unit automatically generates a plan based on the event selected by the reception unit. The generation unit analyzes past event data and participant attribute information to propose an optimal plan. For example, in the case of an all-staff general meeting, the generation unit automatically generates a performance report presentation, slides for policy explanation, and a discussion session for participants to exchange opinions. The generation unit generates an optimal plan based on, for example, past event data. The generation unit can also analyze participant attribute information to propose an optimal plan. Furthermore, the generation unit can automatically generate a plan using a generation AI. The collection unit collects participant responses in real time while the event is progressing based on the plan generated by the generation unit. The collection unit analyzes, for example, the participants' facial expressions, comments, and chat content to grasp the progress of the event. The collection unit analyzes the participants' facial expressions, for example, using facial expression recognition technology. The collection unit can also analyze the participants' comments using voice recognition technology. The collection unit can also analyze the chat content using text mining technology. The verification unit performs effectiveness verification after the event ends based on the response data collected by the collection unit. The verification unit analyzes, for example, the participant response data and survey results to evaluate the effectiveness of the event. The verification unit evaluates, for example, the participants' satisfaction, level of understanding, and activeness of opinion exchange. The verification unit can also suggest improvements for the next event. As a result, the online event production system according to the embodiment can consistently perform processes from selecting the type of event to plan generation, reaction collection, and effectiveness verification.

[0030] The generation unit can analyze past event data or participant attribute information to propose the optimal plan. The generation unit, for example, analyzes past event data to propose the optimal plan. Past event data includes, for example, the number of participants, satisfaction level, and feedback. The generation unit can also analyze participant attribute information to propose the optimal plan. Participant attribute information includes, for example, age, gender, occupation, and interests. Furthermore, the generation unit can use generation AI to analyze past event data and participant attribute information to propose the optimal plan. This makes it possible to propose the optimal plan by utilizing past data and participant information.

[0031] The collection unit can analyze the facial expressions or comments of participants, and the content of chat messages, to grasp the progress of the event. For example, the collection unit analyzes the facial expressions of participants to grasp the progress of the event. The collection unit analyzes the facial expressions of participants using facial expression recognition technology. For example, the collection unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. The collection unit can also analyze the content of participants' comments to grasp the progress of the event. The collection unit analyzes the content of participants' comments using voice recognition technology. For example, the collection unit analyzes the content of participants' comments in real time and makes necessary adjustments. Furthermore, the collection unit can analyze the content of chat messages to grasp the progress of the event. The collection unit analyzes the content of chat messages using text mining technology. For example, the collection unit analyzes the content of chat messages in real time and makes a comprehensive evaluation. This makes it possible to analyze participants' reactions in real time and grasp the progress of the event.

[0032] The verification unit can analyze participant reaction data or survey results to evaluate the effectiveness of the event. For example, the verification unit analyzes participant reaction data to evaluate the effectiveness of the event. The reaction data includes, for example, participants' facial expressions, comments, and chat content. The verification unit analyzes the reaction data using facial expression recognition technology, voice recognition technology, and text mining technology. For example, the verification unit analyzes changes in participants' facial expressions to evaluate changes in emotions. The verification unit can also analyze participants' comments to evaluate their level of understanding and the liveliness of exchange of opinions. Furthermore, the verification unit can analyze chat content to evaluate the progress of the event. The verification unit can also analyze survey results to evaluate the effectiveness of the event. The survey results include, for example, participants' satisfaction, level of understanding, and the liveliness of exchange of opinions. The verification unit analyzes the survey results using a survey analysis algorithm. For example, the verification unit analyzes the survey results in detail and proposes improvements for the next event. This makes it possible to analyze participant reaction data and survey results after the event is over to evaluate the effectiveness.

[0033] The generation unit can include an analysis unit that analyzes the attribute information of the participants. The generation unit includes an analysis unit and analyzes the attribute information of the participants. The analysis unit analyzes attribute information such as the age, gender, occupation, and interests of the participants. The analysis unit proposes the most appropriate plan based on the attribute information of the participants. For example, the analysis unit proposes a plan that suits the age group of the participants. The analysis unit can also propose a plan that suits the occupation or position of the participants. Furthermore, the analysis unit can propose a highly relevant plan based on the interests of the participants. In this way, by analyzing the attribute information of the participants, more accurate plans can be generated.

[0034] The collection unit may include an expression analysis unit that analyzes the facial expressions and speech content of the participants. The collection unit includes an expression analysis unit and analyzes the facial expressions and speech content of the participants. The expression analysis unit, for example, uses expression recognition technology to analyze the facial expressions of the participants. The expression analysis unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. For example, the expression analysis unit analyzes the participants' smiling or surprised expressions to evaluate changes in emotions. The expression analysis unit can also analyze the speech content of the participants. The expression analysis unit analyzes the speech content of the participants using voice recognition technology. For example, the expression analysis unit analyzes the speech content of the participants in real time and makes necessary adjustments. In this way, more detailed reaction data can be collected by analyzing the participants' facial expressions and speech content.

[0035] The verification unit may include a survey analysis unit that analyzes the survey results. The verification unit includes a survey analysis unit and analyzes the survey results. The survey analysis unit analyzes the survey results, for example, using a survey analysis algorithm. The survey analysis unit evaluates participants' satisfaction, level of understanding, and active exchange of opinions. For example, the survey analysis unit analyzes the survey results in detail and suggests improvements for the next event. The survey analysis unit can also identify factors that contributed to the success of the event based on the survey results. In this way, the effectiveness of the event can be evaluated in more detail by analyzing the survey results.

[0036] The reception unit can analyze the user's past event participation history and suggest the most suitable event type. The reception unit analyzes the user's past event participation history and suggest the most suitable event type. For example, the reception unit can suggest similar events based on the types of events the user has previously participated in. The reception unit can also suggest events related to a specific theme based on the user's past participation history. Furthermore, the reception unit can analyze the user's past participation history and suggest events based on the event that generated the highest satisfaction. In this way, the most suitable event can be suggested to the user by analyzing the past participation history.

[0037] The reception unit can perform filtering based on the user's current work situation and areas of interest when selecting an event type. The reception unit can perform filtering based on the user's current work situation and areas of interest when selecting an event type. For example, the reception unit takes into account the user's work situation and preferentially suggests events related to the user's work. The reception unit can also filter and suggest related events based on the user's areas of interest. Furthermore, the reception unit can also preferentially suggest events related to the user's current project. In this way, by filtering events based on the user's work situation and areas of interest, more relevant events can be suggested.

[0038] The reception unit can provide an optimal selection means according to the user's input method when selecting an event type. The reception unit provides an optimal selection means according to the user's input method when selecting an event type. For example, when the user uses voice input, the reception unit selects the event type using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also provide a keyword search function to select the event type. Furthermore, when the user uses image input, the reception unit can also suggest related events using image analysis technology. This makes it easier to select an event type by providing an optimal selection means according to the user's input method.

[0039] The reception unit can prioritize suggesting highly relevant events in consideration of the user's geographical location information when selecting an event type. The reception unit prioritizes suggesting highly relevant events in consideration of the user's geographical location information when selecting an event type. For example, the reception unit prioritizes suggesting events close to the user's current location. The reception unit can also suggest region-specific events based on the user's geographical location information. Furthermore, the reception unit can also suggest easily accessible events in consideration of the user's range of movement. In this way, highly relevant events can be suggested by taking the user's geographical location information into consideration.

[0040] The reception unit can analyze the user's social media activity and suggest related events when selecting an event type. The reception unit can analyze the user's social media activity and suggest related events when selecting an event type. The reception unit can, for example, analyze the content of the user's social media posts and suggest related events. The reception unit can also suggest related events by referring to the activity of the user's friends on social media. Furthermore, the reception unit can suggest related events based on the user's social media check-in information. In this way, related events can be suggested by analyzing the user's social media activity.

[0041] The reception unit can customize the selection method by reflecting the user's past feedback when selecting an event type. The reception unit customizes the selection method by reflecting the user's past feedback when selecting an event type. For example, the reception unit can suggest similar events based on events that the user has given high ratings to in the past. The reception unit can also analyze the user's past feedback and suggest an optimal selection method. Furthermore, the reception unit can customize the interface for selecting an event type based on the user's past feedback. In this way, the selection method can be customized by reflecting the user's past feedback.

[0042] The generation unit can adjust the level of detail of the plan based on the importance of the event when generating the plan. The generation unit adjusts the level of detail of the plan based on the importance of the event when generating the plan. For example, in the case of an important event, the generation unit generates a plan including a detailed schedule and content. In addition, in the case of an event with low importance, the generation unit can also generate a plan including a concise schedule and content. Furthermore, in the case of an event with medium importance, the generation unit can also generate a plan including a schedule and content with a moderate level of detail. In this way, by adjusting the level of detail of the plan based on the importance of the event, an appropriate plan can be generated.

[0043] The generation unit can apply different plan generation algorithms depending on the event category when generating a plan. The generation unit can apply different plan generation algorithms depending on the event category when generating a plan. For example, in the case of a general employee meeting, the generation unit applies a plan generation algorithm that focuses on performance reports and policy explanations. In addition, in the case of an award ceremony, the generation unit can also apply a plan generation algorithm that focuses on introducing winners and the contents of the awards. Furthermore, in the case of a job offer ceremony or induction ceremony, the generation unit can apply a plan generation algorithm that focuses on introducing new employees and welcoming messages. In this way, by applying different algorithms depending on the event category, the optimal plan can be generated.

[0044] The generation unit can improve the accuracy of a plan by referring to the user's past event planning results when generating the plan. The generation unit can improve the accuracy of a plan by referring to the user's past event planning results when generating the plan. For example, the generation unit generates a new plan by referring to plans for events that the user has previously successfully planned. The generation unit can also analyze the user's past event planning results and generate a plan that reflects improvements. Furthermore, the generation unit can apply an optimal plan generation algorithm based on the user's past event planning results. In this way, the accuracy of the plan can be improved by referring to the past event planning results.

[0045] The generation unit can determine the priority of the plans based on the timing of the events when generating the plans. The generation unit determines the priority of the plans based on the timing of the events when generating the plans. For example, the generation unit prioritizes planning events that will be held in the near future. The generation unit can also prioritize generating plans related to seasons or specific events. Furthermore, the generation unit can generate plans at optimal timing based on the user's schedule. In this way, by determining the priority of the plans based on the timing of the events, plans can be generated at appropriate timing.

[0046] The generation unit can adjust the order of plans based on the relevance of events when generating a plan. The generation unit adjusts the order of plans based on the relevance of events when generating a plan. The generation unit, for example, places important sessions first to generate a plan that attracts participants' interest. The generation unit can also generate a plan that arranges highly related sessions consecutively to achieve a smooth progress. Furthermore, the generation unit can generate a plan that arranges sessions in an optimal order based on participants' interests. In this way, by adjusting the order of plans based on the relevance of events, a smooth progress can be achieved.

[0047] The generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise when generating the plan. The generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise when generating the plan. For example, the generation unit generates a plan that makes heavy use of technical terminology for users with high levels of expertise. The generation unit can also generate a plan that explains things in easy-to-understand language for users with low levels of expertise. Furthermore, the generation unit can generate a plan in which the use of appropriate technical terminology is adjusted according to the user's level of expertise. In this way, an appropriate plan can be generated by adjusting the use of technical terminology according to the user's level of expertise.

[0048] The collection unit can improve the accuracy of collection when collecting reaction data by taking into account the attribute information of the participants. The collection unit improves the accuracy of collection when collecting reaction data by taking into account the attribute information of the participants. The collection unit sets appropriate questions and collects reaction data based on, for example, the age and gender of the participants. The collection unit can also collect highly relevant reaction data based on the occupation and position of the participants. Furthermore, the collection unit can collect optimal reaction data based on the interests and concerns of the participants. In this way, the accuracy of collection can be improved by taking into account the attribute information of the participants.

[0049] The collection unit can analyze the content of participants' remarks and changes in facial expressions in real time when collecting reaction data. The collection unit analyzes the content of participants' remarks and changes in facial expressions in real time when collecting reaction data. For example, the collection unit analyzes the content of participants' remarks in real time and collects reaction data. The collection unit can also analyze changes in participants' facial expressions in real time and collect reaction data that reflects changes in emotions. Furthermore, the collection unit can combine the content of participants' remarks and changes in facial expressions to collect comprehensive reaction data. In this way, more detailed reaction data can be collected by analyzing the content of participants' remarks and changes in facial expressions in real time.

[0050] The collection unit can optimize the collection method by referring to the participants' past reaction data when collecting reaction data. The collection unit optimizes the collection method by referring to the participants' past reaction data when collecting reaction data. For example, the collection unit sets optimal questions based on the participants' past reaction data and collects reaction data. The collection unit can also analyze the participants' past reaction data and improve the collection method. Furthermore, the collection unit can optimize the timing of collection by referring to the participants' past reaction data. In this way, the collection method can be optimized by referring to the past reaction data.

[0051] When collecting reaction data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the participants. When collecting reaction data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the participants. For example, the collection unit prioritizes collecting region-specific reaction data based on the current location of the participants. The collection unit can also collect reaction data by setting highly relevant questions based on the geographical location information of the participants. Furthermore, the collection unit can prioritize collecting optimal reaction data by taking into account the movement range of the participants. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the participants.

[0052] The collection unit can analyze the participants' social media activities and collect related data when collecting reaction data. The collection unit analyzes the participants' social media activities and collects related data when collecting reaction data. For example, the collection unit analyzes the content of the participants' posts on social media and collects related reaction data. The collection unit can also collect related reaction data by referring to the activities of the participants' friends on social media. Furthermore, the collection unit can collect related reaction data based on the participants' check-in information on social media. In this way, related data can be collected by analyzing the participants' social media activities.

[0053] The collection unit can customize the collection method by reflecting participants' past feedback when collecting reaction data. The collection unit customizes the collection method by reflecting participants' past feedback when collecting reaction data. For example, the collection unit collects reaction data using a similar method based on a collection method that participants have previously rated highly. The collection unit can also analyze participants' past feedback and suggest an optimal collection method. Furthermore, the collection unit can customize the collection method based on participants' past feedback. In this way, the collection method can be customized by reflecting past feedback.

[0054] The verification unit can improve the accuracy of the verification when verifying the effectiveness by taking into account the attribute information of the participants. The verification unit improves the accuracy of the verification when verifying the effectiveness by taking into account the attribute information of the participants. The verification unit performs appropriate effectiveness verification based on, for example, the age and gender of the participants. The verification unit can also perform highly relevant effectiveness verification based on the occupation and position of the participants. Furthermore, the verification unit can also perform optimal effectiveness verification based on the interests and concerns of the participants. In this way, the accuracy of the verification can be improved by taking into account the attribute information of the participants.

[0055] The verification unit can analyze the participant's reaction data and questionnaire results in detail when verifying effectiveness. The verification unit analyzes the participant's reaction data and questionnaire results in detail when verifying effectiveness. For example, the verification unit analyzes the participant's reaction data in detail to verify effectiveness. The verification unit can also analyze the participant's questionnaire results in detail to verify effectiveness. Furthermore, the verification unit can combine the participant's reaction data and the questionnaire results to perform comprehensive effectiveness verification. As a result, by analyzing the participant's reaction data and the questionnaire results in detail, more accurate effectiveness verification can be performed.

[0056] The verification unit can optimize the verification method by referring to past event data when verifying effectiveness. The verification unit optimizes the verification method by referring to past event data when verifying effectiveness. The verification unit, for example, sets an optimal effectiveness verification method based on past event data. The verification unit can also analyze past event data and improve the verification method. Furthermore, the verification unit can also optimize the timing of verification by referring to past event data. In this way, the verification method can be optimized by referring to past event data.

[0057] When verifying effectiveness, the verification unit can prioritize verification of highly relevant data by taking into account the geographical location information of the participants. When verifying effectiveness, the verification unit prioritizes verification of highly relevant data by taking into account the geographical location information of the participants. For example, the verification unit prioritizes verification of region-specific data based on the current location of the participants. The verification unit can also prioritize verification of highly relevant data based on the geographical location information of the participants. Furthermore, the verification unit can also prioritize verification of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0058] The verification unit can analyze the participants' social media activities and verify related data when verifying effectiveness. The verification unit analyzes the participants' social media activities and verify related data when verifying effectiveness. For example, the verification unit analyzes the content posted by the participants on social media and verifies the related data. The verification unit can also verify the related data by referring to the activities of the participants' friends on social media. Furthermore, the verification unit can also verify the related data based on the participants' check-in information on social media. In this way, the related data can be verified by analyzing the participants' social media activities.

[0059] The verification unit can customize the verification method by reflecting participants' past feedback when verifying effectiveness. The verification unit customizes the verification method by reflecting participants' past feedback when verifying effectiveness. For example, the verification unit verifies effectiveness using a similar method based on a verification method that participants have previously given a high rating. The verification unit can also analyze participants' past feedback and propose an optimal verification method. Furthermore, the verification unit can customize the verification method based on participants' past feedback. In this way, the verification method can be customized by reflecting past feedback.

[0060] The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants during analysis. The analysis unit performs appropriate analysis based on, for example, the age and gender of the participants. The analysis unit can also perform highly relevant analysis based on the occupation and position of the participants. Furthermore, the analysis unit can perform optimal analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0061] The analysis unit can analyze past event data in detail during analysis. The analysis unit analyzes past event data in detail during analysis. For example, the analysis unit analyzes past event data in detail and reflects the results in a current event. The analysis unit can also set an optimal analysis method based on past event data. Furthermore, the analysis unit can analyze past event data and improve the analysis method. In this way, the past event data can be analyzed in detail and reflected in a current event.

[0062] The analysis unit can analyze the participant's reaction data in real time during the analysis. The analysis unit analyzes the participant's reaction data in real time during the analysis. For example, the analysis unit analyzes the participant's reaction data in real time to grasp the progress of the event. The analysis unit can also analyze the participant's reaction data in real time and make necessary adjustments. Furthermore, the analysis unit can analyze the participant's reaction data in real time and make a comprehensive evaluation. In this way, the progress of the event can be grasped by analyzing the participant's reaction data in real time.

[0063] During analysis, the analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. During analysis, the analysis unit prioritizes analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the analysis unit prioritizes analysis of region-specific data based on the current location of the participants. The analysis unit can also prioritize analysis of highly relevant data based on the geographical location information of the participants. Furthermore, the analysis unit can prioritize analysis of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0064] The analysis unit can analyze the social media activities of the participants and analyze the related data during the analysis. The analysis unit analyzes the social media activities of the participants and analyzes the related data during the analysis. For example, the analysis unit analyzes the content posted by the participants on social media and analyzes the related data. The analysis unit can also analyze the related data by referring to the activities of the participants' friends on social media. Furthermore, the analysis unit can analyze the related data based on the check-in information of the participants on social media. In this way, the related data can be analyzed by analyzing the social media activities of the participants.

[0065] The analysis unit can customize the analysis method by reflecting the participants' past feedback during analysis. The analysis unit can customize the analysis method by reflecting the participants' past feedback during analysis. For example, the analysis unit performs analysis using a similar method based on an analysis method that the participants have previously rated highly. The analysis unit can also analyze the participants' past feedback and suggest the optimal analysis method. Furthermore, the analysis unit can customize the analysis method based on the participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback.

[0066] The facial expression analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants when analyzing their facial expressions. The facial expression analysis unit improves the accuracy of the analysis by taking into account the attribute information of the participants when analyzing their facial expressions. The facial expression analysis unit performs appropriate facial expression analysis based on, for example, the age and gender of the participants. The facial expression analysis unit can also perform highly relevant facial expression analysis based on the occupation and position of the participants. Furthermore, the facial expression analysis unit can also perform optimal facial expression analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0067] The facial expression analysis unit can optimize the analysis method by referring to past event data when analyzing facial expressions. The facial expression analysis unit optimizes the analysis method by referring to past event data when analyzing facial expressions. The facial expression analysis unit, for example, sets an optimal facial expression analysis method based on past event data. The facial expression analysis unit can also analyze past event data and improve the analysis method. Furthermore, the facial expression analysis unit can also optimize the timing of analysis by referring to past event data. In this way, the analysis method can be optimized by referring to past event data.

[0068] The facial expression analysis unit can analyze changes in the participants' facial expressions in real time during facial expression analysis. The facial expression analysis unit analyzes changes in the participants' facial expressions in real time during facial expression analysis. For example, the facial expression analysis unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. The facial expression analysis unit can also analyze changes in the participants' facial expressions in real time and make necessary adjustments. Furthermore, the facial expression analysis unit can analyze changes in the participants' facial expressions in real time and make a comprehensive evaluation. In this way, changes in emotions can be grasped by analyzing changes in the participants' facial expressions in real time.

[0069] When analyzing facial expressions, the facial expression analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. When analyzing facial expressions, the facial expression analysis unit prioritizes analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the facial expression analysis unit prioritizes analysis of region-specific data based on the current location of the participants. The facial expression analysis unit can also prioritize analysis of highly relevant data based on the geographical location information of the participants. Furthermore, the facial expression analysis unit can also prioritize analysis of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0070] The facial expression analysis unit can analyze the social media activities of the participants and analyze related data when analyzing their facial expressions. The facial expression analysis unit analyzes the social media activities of the participants and analyzes related data when analyzing their facial expressions. For example, the facial expression analysis unit analyzes the content posted by the participants on social media and analyzes related data. The facial expression analysis unit can also analyze related data by referring to the activities of the participants' friends on social media. Furthermore, the facial expression analysis unit can analyze related data based on the check-in information of the participants on social media. In this way, related data can be analyzed by analyzing the social media activities of the participants.

[0071] The facial expression analysis unit can customize the analysis method by reflecting the participants' past feedback when analyzing facial expressions. The facial expression analysis unit customizes the analysis method by reflecting the participants' past feedback when analyzing facial expressions. For example, the facial expression analysis unit performs facial expression analysis using a similar method based on an analysis method that the participants have previously rated highly. The facial expression analysis unit can also analyze the participants' past feedback and propose the optimal analysis method. Furthermore, the facial expression analysis unit can customize the analysis method based on the participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback.

[0072] The survey analysis unit can improve the accuracy of the analysis when analyzing a survey by taking into account the attribute information of the participants. The survey analysis unit improves the accuracy of the analysis when analyzing a survey by taking into account the attribute information of the participants. The survey analysis unit performs appropriate survey analysis based on, for example, the age and gender of the participants. The survey analysis unit can also perform highly relevant survey analysis based on the occupation and position of the participants. Furthermore, the survey analysis unit can also perform optimal survey analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0073] The survey analysis unit can optimize the analysis method by referring to past event data when analyzing a survey. The survey analysis unit can optimize the analysis method by referring to past event data when analyzing a survey. The survey analysis unit, for example, sets an optimal survey analysis method based on past event data. The survey analysis unit can also analyze past event data and improve the analysis method. Furthermore, the survey analysis unit can also optimize the timing of analysis by referring to past event data. In this way, the analysis method can be optimized by referring to past event data.

[0074] The questionnaire analysis unit can analyze the content of participants' responses in detail when analyzing the questionnaire. The questionnaire analysis unit analyzes the content of participants' responses in detail when analyzing the questionnaire. For example, the questionnaire analysis unit analyzes the content of participants' responses in detail and evaluates the effectiveness of the event. The questionnaire analysis unit can also suggest improvements for the next event based on the content of participants' responses. Furthermore, the questionnaire analysis unit can comprehensively analyze the content of participants' responses and identify the factors that contributed to the success of the event. In this way, the effectiveness of the event can be evaluated by analyzing the content of participants' responses in detail.

[0075] When analyzing the survey, the survey analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. When analyzing the survey, the survey analysis unit prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the survey analysis unit prioritizes analysis of region-specific data based on the participant's current location. The survey analysis unit can also prioritize analysis of highly relevant data based on the participant's geographical location information. Furthermore, the survey analysis unit can also prioritize analysis of optimal data by taking into account the participant's range of movement. In this way, highly relevant data can be prioritized analyzed by taking into account the participant's geographical location information.

[0076] The survey analysis unit can analyze the participants' social media activities and analyze related data when analyzing the survey. The survey analysis unit analyzes the participants' social media activities and analyzes related data when analyzing the survey. For example, the survey analysis unit analyzes the content posted by the participants on social media and analyzes related data. The survey analysis unit can also analyze related data by referring to the activities of the participants' friends on social media. Furthermore, the survey analysis unit can analyze related data based on the participants' check-in information on social media. In this way, related data can be analyzed by analyzing the participants' social media activities.

[0077] The survey analysis unit can customize the analysis method by reflecting participants' past feedback when analyzing a survey. The survey analysis unit customizes the analysis method by reflecting participants' past feedback when analyzing a survey. For example, the survey analysis unit analyzes the survey using a method similar to an analysis method that participants have previously given a high rating to. The survey analysis unit can also analyze participants' past feedback and propose the optimal analysis method. Furthermore, the survey analysis unit can customize the analysis method based on participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The reception unit can analyze the user's past event participation history and suggest the most suitable event type. For example, it can suggest similar events based on the types of events the user has previously participated in. The reception unit can also suggest events related to a specific theme based on the user's past participation history. Furthermore, the reception unit can analyze the user's past participation history and suggest events based on the event with the highest satisfaction. In this way, it is possible to suggest the most suitable event for the user by analyzing the user's past participation history.

[0080] The generation unit can adjust the level of detail of the plan based on the importance of the event when generating the plan. For example, for an important event, the generation unit generates a plan including a detailed schedule and content. For an event with low importance, the generation unit can also generate a plan including a simple schedule and content. Furthermore, for an event with medium importance, the generation unit can also generate a plan including a schedule and content with a moderate level of detail. In this way, an appropriate plan can be generated by adjusting the level of detail of the plan based on the importance of the event.

[0081] When collecting reaction data, the collection unit can improve the accuracy of collection by taking into account the attribute information of the participants. For example, appropriate questions are set based on the age and gender of the participants to collect reaction data. The collection unit can also collect highly relevant reaction data based on the participants' occupations and job titles. Furthermore, the collection unit can also collect optimal reaction data based on the participants' interests and concerns. In this way, by taking into account the attribute information of the participants, the accuracy of collection can be improved.

[0082] The verification unit can improve the accuracy of the effectiveness verification by taking into account the attribute information of the participants. For example, the verification unit can perform appropriate effectiveness verification based on the age and gender of the participants. The verification unit can also perform highly relevant effectiveness verification based on the occupation and position of the participants. Furthermore, the verification unit can perform optimal effectiveness verification based on the interests and concerns of the participants. In this way, the accuracy of the verification can be improved by taking into account the attribute information of the participants.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the participant's attribute information. For example, the analysis unit can perform appropriate analysis based on the participant's age and gender. The analysis unit can also perform highly relevant analysis based on the participant's occupation and position. Furthermore, the analysis unit can perform optimal analysis based on the participant's interests and concerns. In this way, the analysis accuracy can be improved by taking into account the participant's attribute information.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The reception unit allows the user to select the type of online event. For example, the user can select the type of event, such as an employee general meeting, award ceremony, job offer ceremony / entrance ceremony, rally / kick-off, or company social gathering / networking event. The reception unit also allows the user to enter detailed information, such as the purpose of the event and the attributes of the participants. For example, in the case of an employee general meeting, the user can enter the participants' positions and departments, as well as the purpose of the event (e.g., performance report or policy explanation). Step 2: The generation unit automatically generates a plan based on the event selected by the reception unit. The generation unit analyzes past event data and participant attribute information to propose the optimal plan. For example, in the case of a general meeting of employees, it automatically generates a performance report presentation, slides explaining policies, and a discussion session for participants to exchange opinions. The generation unit can also use generation AI to automatically generate plans. Step 3: The collection unit collects participants' reactions in real time while the event is in progress based on the plan generated by the generation unit. The collection unit analyzes, for example, participants' facial expressions, comments, and chat content to understand the progress of the event. The collection unit analyzes participants' reactions using facial expression recognition technology, voice recognition technology, and text mining technology. Step 4: The Verification Department verifies the effectiveness of the event after it has ended based on the reaction data collected by the Collection Department. The Verification Department analyzes the participant reaction data and survey results to evaluate the effectiveness of the event. For example, they evaluate participants' satisfaction, level of understanding, and the lively exchange of opinions, and propose improvements for the next event.

[0086] (Example 2) An online event production system according to an embodiment of the present invention is a system that consistently handles the entire process from planning an online event to verifying its effectiveness. In this online event production system, a user selects the type of online event, and the system automatically generates a plan for the selected event. The system collects participant responses in real time as the event progresses, and then performs a verification of the event's effectiveness after the event ends. This mechanism enables cost-effective production of online events. For example, in the online event production system, a user selects the type of online event. The user then inputs detailed information such as the purpose of the event and the attributes of the participants. For example, in the case of an all-staff meeting, the user inputs the participants' job titles and departments, as well as the purpose of the event (e.g., a performance report or policy explanation). This information is entered into the system. Next, the online event production system automatically generates a plan for the selected event based on the input information. The system analyzes past event data and participant attribute information to propose an optimal plan. For example, in the case of an all-staff meeting, the system automatically generates a performance report presentation, slides for policy explanations, and a discussion session for participants to exchange opinions. Furthermore, the online event production system collects participant responses in real time as the event progresses. For example, the system analyzes participants' facial expressions, comments, chat content, etc. to understand the progress of the event. This allows necessary adjustments to be made as the event progresses. Finally, the online event production system evaluates the effectiveness of the event after it ends. The system analyzes participants' reaction data and survey results to evaluate the effectiveness of the event. For example, it evaluates participants' satisfaction, level of understanding, and active exchange of opinions, and suggests improvements for the next event. This enables the online event production system to produce cost-effective online events. This allows users to leave complex planning and progress management to the system, resulting in the realization of an effective event.

[0087] An online event production system according to an embodiment includes a reception unit, a generation unit, a collection unit, and a verification unit. The reception unit allows a user to select the type of online event. The user can select the type of event, for example, an all-staff general meeting, an award ceremony, a job offer ceremony / entrance ceremony, a rally / kickoff, or an internal social gathering / networking event. The reception unit allows the user to input detailed information such as the purpose of the event and the attributes of the participants. For example, in the case of an all-staff general meeting, the user can input the participants' positions and departments, and the purpose of the event (e.g., a performance report or policy explanation). The generation unit automatically generates a plan based on the event selected by the reception unit. The generation unit analyzes past event data and participant attribute information to propose an optimal plan. For example, in the case of an all-staff general meeting, the generation unit automatically generates a performance report presentation, slides for policy explanation, and a discussion session for participants to exchange opinions. The generation unit generates an optimal plan based on, for example, past event data. The generation unit can also analyze participant attribute information to propose an optimal plan. Furthermore, the generation unit can automatically generate a plan using a generation AI. The collection unit collects participant responses in real time while the event is progressing based on the plan generated by the generation unit. The collection unit analyzes, for example, the participants' facial expressions, comments, and chat content to grasp the progress of the event. The collection unit analyzes the participants' facial expressions, for example, using facial expression recognition technology. The collection unit can also analyze the participants' comments using voice recognition technology. The collection unit can also analyze the chat content using text mining technology. The verification unit performs effectiveness verification after the event ends based on the response data collected by the collection unit. The verification unit analyzes, for example, the participant response data and survey results to evaluate the effectiveness of the event. The verification unit evaluates, for example, the participants' satisfaction, level of understanding, and activeness of opinion exchange. The verification unit can also suggest improvements for the next event. As a result, the online event production system according to the embodiment can consistently perform processes from selecting the type of event to plan generation, reaction collection, and effectiveness verification.

[0088] The generation unit can analyze past event data or participant attribute information to propose the optimal plan. The generation unit, for example, analyzes past event data to propose the optimal plan. Past event data includes, for example, the number of participants, satisfaction level, and feedback. The generation unit can also analyze participant attribute information to propose the optimal plan. Participant attribute information includes, for example, age, gender, occupation, and interests. Furthermore, the generation unit can use generation AI to analyze past event data and participant attribute information to propose the optimal plan. This makes it possible to propose the optimal plan by utilizing past data and participant information.

[0089] The collection unit can analyze the facial expressions or comments of participants, and the content of chat messages, to grasp the progress of the event. For example, the collection unit analyzes the facial expressions of participants to grasp the progress of the event. The collection unit analyzes the facial expressions of participants using facial expression recognition technology. For example, the collection unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. The collection unit can also analyze the content of participants' comments to grasp the progress of the event. The collection unit analyzes the content of participants' comments using voice recognition technology. For example, the collection unit analyzes the content of participants' comments in real time and makes necessary adjustments. Furthermore, the collection unit can analyze the content of chat messages to grasp the progress of the event. The collection unit analyzes the content of chat messages using text mining technology. For example, the collection unit analyzes the content of chat messages in real time and makes a comprehensive evaluation. This makes it possible to analyze participants' reactions in real time and grasp the progress of the event.

[0090] The verification unit can analyze participant reaction data or survey results to evaluate the effectiveness of the event. For example, the verification unit analyzes participant reaction data to evaluate the effectiveness of the event. The reaction data includes, for example, participants' facial expressions, comments, and chat content. The verification unit analyzes the reaction data using facial expression recognition technology, voice recognition technology, and text mining technology. For example, the verification unit analyzes changes in participants' facial expressions to evaluate changes in emotions. The verification unit can also analyze participants' comments to evaluate their level of understanding and the liveliness of exchange of opinions. Furthermore, the verification unit can analyze chat content to evaluate the progress of the event. The verification unit can also analyze survey results to evaluate the effectiveness of the event. The survey results include, for example, participants' satisfaction, level of understanding, and the liveliness of exchange of opinions. The verification unit analyzes the survey results using a survey analysis algorithm. For example, the verification unit analyzes the survey results in detail and proposes improvements for the next event. This makes it possible to analyze participant reaction data and survey results after the event is over to evaluate the effectiveness.

[0091] The generation unit can include an analysis unit that analyzes the attribute information of the participants. The generation unit includes an analysis unit and analyzes the attribute information of the participants. The analysis unit analyzes attribute information such as the age, gender, occupation, and interests of the participants. The analysis unit proposes the most appropriate plan based on the attribute information of the participants. For example, the analysis unit proposes a plan that suits the age group of the participants. The analysis unit can also propose a plan that suits the occupation or position of the participants. Furthermore, the analysis unit can propose a highly relevant plan based on the interests of the participants. In this way, by analyzing the attribute information of the participants, more accurate plans can be generated.

[0092] The collection unit may include an expression analysis unit that analyzes the facial expressions and speech content of the participants. The collection unit includes an expression analysis unit and analyzes the facial expressions and speech content of the participants. The expression analysis unit, for example, uses expression recognition technology to analyze the facial expressions of the participants. The expression analysis unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. For example, the expression analysis unit analyzes the participants' smiling or surprised expressions to evaluate changes in emotions. The expression analysis unit can also analyze the speech content of the participants. The expression analysis unit analyzes the speech content of the participants using voice recognition technology. For example, the expression analysis unit analyzes the speech content of the participants in real time and makes necessary adjustments. In this way, more detailed reaction data can be collected by analyzing the participants' facial expressions and speech content.

[0093] The verification unit may include a survey analysis unit that analyzes the survey results. The verification unit includes a survey analysis unit and analyzes the survey results. The survey analysis unit analyzes the survey results, for example, using a survey analysis algorithm. The survey analysis unit evaluates participants' satisfaction, level of understanding, and active exchange of opinions. For example, the survey analysis unit analyzes the survey results in detail and suggests improvements for the next event. The survey analysis unit can also identify factors that contributed to the success of the event based on the survey results. In this way, the effectiveness of the event can be evaluated in more detail by analyzing the survey results.

[0094] The reception unit estimates the user's emotions and supports the user in selecting an event type based on the estimated user emotions. The reception unit estimates the user's emotions and supports the user in selecting an event type based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface to facilitate the user in selecting an event type. Furthermore, if the user is relaxed, the reception unit can provide detailed event information to broaden the user's options. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly select an event type. This supports the user in selecting an event type based on the user's emotions, enabling the user to select a more appropriate event. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0095] The reception unit can analyze the user's past event participation history and suggest the most suitable event type. The reception unit analyzes the user's past event participation history and suggest the most suitable event type. For example, the reception unit can suggest similar events based on the types of events the user has previously participated in. The reception unit can also suggest events related to a specific theme based on the user's past participation history. Furthermore, the reception unit can analyze the user's past participation history and suggest events based on the event that generated the highest satisfaction. In this way, the most suitable event can be suggested to the user by analyzing the past participation history.

[0096] The reception unit can perform filtering based on the user's current work situation and areas of interest when selecting an event type. The reception unit can perform filtering based on the user's current work situation and areas of interest when selecting an event type. For example, the reception unit takes into account the user's work situation and preferentially suggests events related to the user's work. The reception unit can also filter and suggest related events based on the user's areas of interest. Furthermore, the reception unit can also preferentially suggest events related to the user's current project. In this way, by filtering events based on the user's work situation and areas of interest, more relevant events can be suggested.

[0097] The reception unit can provide an optimal selection means according to the user's input method when selecting an event type. The reception unit provides an optimal selection means according to the user's input method when selecting an event type. For example, when the user uses voice input, the reception unit selects the event type using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also provide a keyword search function to select the event type. Furthermore, when the user uses image input, the reception unit can also suggest related events using image analysis technology. This makes it easier to select an event type by providing an optimal selection means according to the user's input method.

[0098] The reception unit estimates the user's emotions and determines the priority of event type selection based on the estimated user emotions. The reception unit estimates the user's emotions and determines the priority of event type selection based on the estimated user emotions. For example, if the user is nervous, the reception unit may preferentially suggest relaxing events. Furthermore, if the user is having fun, the reception unit may preferentially suggest entertaining events. Furthermore, if the user is tired, the reception unit may preferentially suggest refreshing events. By determining the priority of event type selection based on the user's emotions, more appropriate events can be suggested. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The reception unit can prioritize suggesting highly relevant events in consideration of the user's geographical location information when selecting an event type. The reception unit prioritizes suggesting highly relevant events in consideration of the user's geographical location information when selecting an event type. For example, the reception unit prioritizes suggesting events close to the user's current location. The reception unit can also suggest region-specific events based on the user's geographical location information. Furthermore, the reception unit can also suggest easily accessible events in consideration of the user's range of movement. In this way, highly relevant events can be suggested by taking the user's geographical location information into consideration.

[0100] The reception unit can analyze the user's social media activity and suggest related events when selecting an event type. The reception unit can analyze the user's social media activity and suggest related events when selecting an event type. The reception unit can, for example, analyze the content of the user's social media posts and suggest related events. The reception unit can also suggest related events by referring to the activity of the user's friends on social media. Furthermore, the reception unit can suggest related events based on the user's social media check-in information. In this way, related events can be suggested by analyzing the user's social media activity.

[0101] The reception unit can customize the selection method by reflecting the user's past feedback when selecting an event type. The reception unit customizes the selection method by reflecting the user's past feedback when selecting an event type. For example, the reception unit can suggest similar events based on events that the user has given high ratings to in the past. The reception unit can also analyze the user's past feedback and suggest an optimal selection method. Furthermore, the reception unit can customize the interface for selecting an event type based on the user's past feedback. In this way, the selection method can be customized by reflecting the user's past feedback.

[0102] The generation unit estimates the user's emotions and adjusts the presentation of the plan based on the estimated user emotions. The generation unit estimates the user's emotions and adjusts the presentation of the plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a plan using soft colors and calming music. If the user is excited, the generation unit can also generate a plan using bright colors and energetic music. Furthermore, if the user is stressed, the generation unit can generate a plan with a simple, visually calming design. This allows for the generation of more appropriate plans by adjusting the presentation of the plan based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0103] The generation unit can adjust the level of detail of the plan based on the importance of the event when generating the plan. The generation unit adjusts the level of detail of the plan based on the importance of the event when generating the plan. For example, in the case of an important event, the generation unit generates a plan including a detailed schedule and content. In addition, in the case of an event with low importance, the generation unit can also generate a plan including a concise schedule and content. Furthermore, in the case of an event with medium importance, the generation unit can also generate a plan including a schedule and content with a moderate level of detail. In this way, by adjusting the level of detail of the plan based on the importance of the event, an appropriate plan can be generated.

[0104] The generation unit can apply different plan generation algorithms depending on the event category when generating a plan. The generation unit can apply different plan generation algorithms depending on the event category when generating a plan. For example, in the case of a general employee meeting, the generation unit applies a plan generation algorithm that focuses on performance reports and policy explanations. In addition, in the case of an award ceremony, the generation unit can also apply a plan generation algorithm that focuses on introducing winners and the contents of the awards. Furthermore, in the case of a job offer ceremony or induction ceremony, the generation unit can apply a plan generation algorithm that focuses on introducing new employees and welcoming messages. In this way, by applying different algorithms depending on the event category, the optimal plan can be generated.

[0105] The generation unit can improve the accuracy of a plan by referring to the user's past event planning results when generating the plan. The generation unit can improve the accuracy of a plan by referring to the user's past event planning results when generating the plan. For example, the generation unit generates a new plan by referring to plans for events that the user has previously successfully planned. The generation unit can also analyze the user's past event planning results and generate a plan that reflects improvements. Furthermore, the generation unit can apply an optimal plan generation algorithm based on the user's past event planning results. In this way, the accuracy of the plan can be improved by referring to the past event planning results.

[0106] The generation unit estimates the user's emotions and adjusts the length of the plan based on the estimated user emotions. The generation unit estimates the user's emotions and adjusts the length of the plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point plan. If the user is relaxed, the generation unit can also generate a longer plan with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate a plan with visually stimulating effects. By adjusting the length of the plan based on the user's emotions, more appropriate plans can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0107] The generation unit can determine the priority of the plans based on the timing of the events when generating the plans. The generation unit determines the priority of the plans based on the timing of the events when generating the plans. For example, the generation unit prioritizes planning events that will be held in the near future. The generation unit can also prioritize generating plans related to seasons or specific events. Furthermore, the generation unit can generate plans at optimal timing based on the user's schedule. In this way, by determining the priority of the plans based on the timing of the events, plans can be generated at appropriate timing.

[0108] The generation unit can adjust the order of plans based on the relevance of events when generating a plan. The generation unit adjusts the order of plans based on the relevance of events when generating a plan. The generation unit, for example, places important sessions first to generate a plan that attracts participants' interest. The generation unit can also generate a plan that arranges highly related sessions consecutively to achieve a smooth progress. Furthermore, the generation unit can generate a plan that arranges sessions in an optimal order based on participants' interests. In this way, by adjusting the order of plans based on the relevance of events, a smooth progress can be achieved.

[0109] The generation unit can adjust the use of technical terminology in the plan according to the user's level of expertise when generating the plan. The generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise when generating the plan. For example, the generation unit generates a plan that makes heavy use of technical terminology for users with high levels of expertise. The generation unit can also generate a plan that explains things in easy-to-understand language for users with low levels of expertise. Furthermore, the generation unit can generate a plan in which the use of appropriate technical terminology is adjusted according to the user's level of expertise. In this way, an appropriate plan can be generated by adjusting the use of technical terminology according to the user's level of expertise.

[0110] The collection unit estimates the user's emotions and adjusts the reaction data collection method based on the estimated user emotions. The collection unit estimates the user's emotions and adjusts the reaction data collection method based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects reaction data that emphasizes natural conversation. When the user is nervous, the collection unit can collect reaction data mainly through simple questions. Furthermore, when the user is excited, the collection unit can use an interactive method to collect detailed reaction data. This allows for more appropriate data to be collected by adjusting the reaction data collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0111] The collection unit can improve the accuracy of collection when collecting reaction data by taking into account the attribute information of the participants. The collection unit improves the accuracy of collection when collecting reaction data by taking into account the attribute information of the participants. The collection unit sets appropriate questions and collects reaction data based on, for example, the age and gender of the participants. The collection unit can also collect highly relevant reaction data based on the occupation and position of the participants. Furthermore, the collection unit can collect optimal reaction data based on the interests and concerns of the participants. In this way, the accuracy of collection can be improved by taking into account the attribute information of the participants.

[0112] The collection unit can analyze the content of participants' remarks and changes in facial expressions in real time when collecting reaction data. The collection unit analyzes the content of participants' remarks and changes in facial expressions in real time when collecting reaction data. For example, the collection unit analyzes the content of participants' remarks in real time and collects reaction data. The collection unit can also analyze changes in participants' facial expressions in real time and collect reaction data that reflects changes in emotions. Furthermore, the collection unit can combine the content of participants' remarks and changes in facial expressions to collect comprehensive reaction data. In this way, more detailed reaction data can be collected by analyzing the content of participants' remarks and changes in facial expressions in real time.

[0113] The collection unit can optimize the collection method by referring to the participants' past reaction data when collecting reaction data. The collection unit optimizes the collection method by referring to the participants' past reaction data when collecting reaction data. For example, the collection unit sets optimal questions based on the participants' past reaction data and collects reaction data. The collection unit can also analyze the participants' past reaction data and improve the collection method. Furthermore, the collection unit can optimize the timing of collection by referring to the participants' past reaction data. In this way, the collection method can be optimized by referring to the past reaction data.

[0114] The collection unit estimates the user's emotions and determines the priority of the reaction data to be collected based on the estimated user emotions. The collection unit estimates the user's emotions and determines the priority of the reaction data to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit prioritizes collecting questions that will help the user relax. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting detailed reaction data. Furthermore, if the user is excited, the collection unit can also prioritize collecting reaction data that reflects changes in emotion. This allows for prioritized collection of more important data by determining the priority of reaction data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0115] When collecting reaction data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the participants. When collecting reaction data, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the participants. For example, the collection unit prioritizes collecting region-specific reaction data based on the current location of the participants. The collection unit can also collect reaction data by setting highly relevant questions based on the geographical location information of the participants. Furthermore, the collection unit can prioritize collecting optimal reaction data by taking into account the movement range of the participants. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information of the participants.

[0116] The collection unit can analyze the participants' social media activities and collect related data when collecting reaction data. The collection unit analyzes the participants' social media activities and collects related data when collecting reaction data. For example, the collection unit analyzes the content of the participants' posts on social media and collects related reaction data. The collection unit can also collect related reaction data by referring to the activities of the participants' friends on social media. Furthermore, the collection unit can collect related reaction data based on the participants' check-in information on social media. In this way, related data can be collected by analyzing the participants' social media activities.

[0117] The collection unit can customize the collection method by reflecting participants' past feedback when collecting reaction data. The collection unit customizes the collection method by reflecting participants' past feedback when collecting reaction data. For example, the collection unit collects reaction data using a similar method based on a collection method that participants have previously rated highly. The collection unit can also analyze participants' past feedback and suggest an optimal collection method. Furthermore, the collection unit can customize the collection method based on participants' past feedback. In this way, the collection method can be customized by reflecting past feedback.

[0118] The verification unit estimates the user's emotions and adjusts the effect verification method based on the estimated user emotions. The verification unit estimates the user's emotions and adjusts the effect verification method based on the estimated user emotions. For example, the verification unit performs detailed effect verification when the user is relaxed. The verification unit can also perform brief effect verification when the user is nervous. Furthermore, the verification unit can perform effect verification that reflects changes in emotions when the user is excited. This allows for more appropriate effect verification by adjusting the effect verification method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0119] The verification unit can improve the accuracy of the verification when verifying the effectiveness by taking into account the attribute information of the participants. The verification unit improves the accuracy of the verification when verifying the effectiveness by taking into account the attribute information of the participants. The verification unit performs appropriate effectiveness verification based on, for example, the age and gender of the participants. The verification unit can also perform highly relevant effectiveness verification based on the occupation and position of the participants. Furthermore, the verification unit can also perform optimal effectiveness verification based on the interests and concerns of the participants. In this way, the accuracy of the verification can be improved by taking into account the attribute information of the participants.

[0120] The verification unit can analyze the participant's reaction data and questionnaire results in detail when verifying effectiveness. The verification unit analyzes the participant's reaction data and questionnaire results in detail when verifying effectiveness. For example, the verification unit analyzes the participant's reaction data in detail to verify effectiveness. The verification unit can also analyze the participant's questionnaire results in detail to verify effectiveness. Furthermore, the verification unit can combine the participant's reaction data and the questionnaire results to perform comprehensive effectiveness verification. As a result, by analyzing the participant's reaction data and the questionnaire results in detail, more accurate effectiveness verification can be performed.

[0121] The verification unit can optimize the verification method by referring to past event data when verifying effectiveness. The verification unit optimizes the verification method by referring to past event data when verifying effectiveness. The verification unit, for example, sets an optimal effectiveness verification method based on past event data. The verification unit can also analyze past event data and improve the verification method. Furthermore, the verification unit can also optimize the timing of verification by referring to past event data. In this way, the verification method can be optimized by referring to past event data.

[0122] The verification unit estimates the user's emotions and determines the priority of effect verification based on the estimated user emotions. The verification unit estimates the user's emotions and determines the priority of effect verification based on the estimated user emotions. For example, if the user is nervous, the verification unit prioritizes effect verification that can relax the user. Furthermore, if the user is relaxed, the verification unit can also prioritize detailed effect verification. Furthermore, if the user is excited, the verification unit can also prioritize effect verification that reflects changes in emotion. In this way, by determining the priority of effect verification based on the user's emotions, more important data can be verified preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0123] When verifying effectiveness, the verification unit can prioritize verification of highly relevant data by taking into account the geographical location information of the participants. When verifying effectiveness, the verification unit prioritizes verification of highly relevant data by taking into account the geographical location information of the participants. For example, the verification unit prioritizes verification of region-specific data based on the current location of the participants. The verification unit can also prioritize verification of highly relevant data based on the geographical location information of the participants. Furthermore, the verification unit can also prioritize verification of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0124] The verification unit can analyze the participants' social media activities and verify related data when verifying effectiveness. The verification unit analyzes the participants' social media activities and verify related data when verifying effectiveness. For example, the verification unit analyzes the content posted by the participants on social media and verifies the related data. The verification unit can also verify the related data by referring to the activities of the participants' friends on social media. Furthermore, the verification unit can also verify the related data based on the participants' check-in information on social media. In this way, the related data can be verified by analyzing the participants' social media activities.

[0125] The verification unit can customize the verification method by reflecting participants' past feedback when verifying effectiveness. The verification unit customizes the verification method by reflecting participants' past feedback when verifying effectiveness. For example, the verification unit verifies effectiveness using a similar method based on a verification method that participants have previously given a high rating. The verification unit can also analyze participants' past feedback and propose an optimal verification method. Furthermore, the verification unit can customize the verification method based on participants' past feedback. In this way, the verification method can be customized by reflecting past feedback.

[0126] The analysis unit estimates the user's emotion and adjusts the analysis method based on the estimated user emotion. The analysis unit estimates the user's emotion and adjusts the analysis method based on the estimated user emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is nervous. Furthermore, the analysis unit can perform an analysis that reflects changes in emotion when the user is excited. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0127] The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants during analysis. The analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants during analysis. The analysis unit performs appropriate analysis based on, for example, the age and gender of the participants. The analysis unit can also perform highly relevant analysis based on the occupation and position of the participants. Furthermore, the analysis unit can perform optimal analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0128] The analysis unit can analyze past event data in detail during analysis. The analysis unit analyzes past event data in detail during analysis. For example, the analysis unit analyzes past event data in detail and reflects the results in a current event. The analysis unit can also set an optimal analysis method based on past event data. Furthermore, the analysis unit can analyze past event data and improve the analysis method. In this way, the past event data can be analyzed in detail and reflected in a current event.

[0129] The analysis unit can analyze the participant's reaction data in real time during the analysis. The analysis unit analyzes the participant's reaction data in real time during the analysis. For example, the analysis unit analyzes the participant's reaction data in real time to grasp the progress of the event. The analysis unit can also analyze the participant's reaction data in real time and make necessary adjustments. Furthermore, the analysis unit can analyze the participant's reaction data in real time and make a comprehensive evaluation. In this way, the progress of the event can be grasped by analyzing the participant's reaction data in real time.

[0130] The analysis unit estimates the user's emotions and determines the analysis priority based on the estimated user emotions. The analysis unit estimates the user's emotions and determines the analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit prioritizes analysis that will help the user relax. Furthermore, if the user is relaxed, the analysis unit can also prioritize detailed analysis. Furthermore, if the user is excited, the analysis unit can also prioritize analysis that reflects changes in emotions. By determining the analysis priority based on the user's emotions, more important data can be prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0131] During analysis, the analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. During analysis, the analysis unit prioritizes analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the analysis unit prioritizes analysis of region-specific data based on the current location of the participants. The analysis unit can also prioritize analysis of highly relevant data based on the geographical location information of the participants. Furthermore, the analysis unit can prioritize analysis of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0132] The analysis unit can analyze the social media activities of the participants and analyze the related data during the analysis. The analysis unit analyzes the social media activities of the participants and analyzes the related data during the analysis. For example, the analysis unit analyzes the content posted by the participants on social media and analyzes the related data. The analysis unit can also analyze the related data by referring to the activities of the participants' friends on social media. Furthermore, the analysis unit can analyze the related data based on the check-in information of the participants on social media. In this way, the related data can be analyzed by analyzing the social media activities of the participants.

[0133] The analysis unit can customize the analysis method by reflecting the participants' past feedback during analysis. The analysis unit can customize the analysis method by reflecting the participants' past feedback during analysis. For example, the analysis unit performs analysis using a similar method based on an analysis method that the participants have previously rated highly. The analysis unit can also analyze the participants' past feedback and suggest the optimal analysis method. Furthermore, the analysis unit can customize the analysis method based on the participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback.

[0134] The facial expression analysis unit estimates the user's emotion and adjusts the facial expression analysis method based on the estimated user emotion. The facial expression analysis unit estimates the user's emotion and adjusts the facial expression analysis method based on the estimated user emotion. For example, the facial expression analysis unit performs detailed facial expression analysis when the user is relaxed. Furthermore, the facial expression analysis unit can also perform concise facial expression analysis when the user is nervous. Furthermore, the facial expression analysis unit can also perform facial expression analysis that reflects changes in emotion when the user is excited. This allows for more appropriate facial expression analysis by adjusting the facial expression analysis method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the facial expression analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the facial expression analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0135] The facial expression analysis unit can improve the accuracy of the analysis by taking into account the attribute information of the participants when analyzing their facial expressions. The facial expression analysis unit improves the accuracy of the analysis by taking into account the attribute information of the participants when analyzing their facial expressions. The facial expression analysis unit performs appropriate facial expression analysis based on, for example, the age and gender of the participants. The facial expression analysis unit can also perform highly relevant facial expression analysis based on the occupation and position of the participants. Furthermore, the facial expression analysis unit can also perform optimal facial expression analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0136] The facial expression analysis unit can optimize the analysis method by referring to past event data when analyzing facial expressions. The facial expression analysis unit optimizes the analysis method by referring to past event data when analyzing facial expressions. The facial expression analysis unit, for example, sets an optimal facial expression analysis method based on past event data. The facial expression analysis unit can also analyze past event data and improve the analysis method. Furthermore, the facial expression analysis unit can also optimize the timing of analysis by referring to past event data. In this way, the analysis method can be optimized by referring to past event data.

[0137] The facial expression analysis unit can analyze changes in the participants' facial expressions in real time during facial expression analysis. The facial expression analysis unit analyzes changes in the participants' facial expressions in real time during facial expression analysis. For example, the facial expression analysis unit analyzes changes in the participants' facial expressions in real time to grasp changes in emotions. The facial expression analysis unit can also analyze changes in the participants' facial expressions in real time and make necessary adjustments. Furthermore, the facial expression analysis unit can analyze changes in the participants' facial expressions in real time and make a comprehensive evaluation. In this way, changes in emotions can be grasped by analyzing changes in the participants' facial expressions in real time.

[0138] The facial expression analysis unit estimates the user's emotions and determines the priority of facial expression analysis based on the estimated user emotions. The facial expression analysis unit estimates the user's emotions and determines the priority of facial expression analysis based on the estimated user emotions. For example, if the user is nervous, the facial expression analysis unit prioritizes facial expression analysis that will relax the user. Furthermore, if the user is relaxed, the facial expression analysis unit can also prioritize detailed facial expression analysis. Furthermore, if the user is excited, the facial expression analysis unit can also prioritize facial expression analysis that reflects changes in emotions. This allows for the priority of facial expression analysis to be determined based on the user's emotions, thereby prioritizing the analysis of more important data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the facial expression analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the facial expression analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0139] When analyzing facial expressions, the facial expression analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. When analyzing facial expressions, the facial expression analysis unit prioritizes analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the facial expression analysis unit prioritizes analysis of region-specific data based on the current location of the participants. The facial expression analysis unit can also prioritize analysis of highly relevant data based on the geographical location information of the participants. Furthermore, the facial expression analysis unit can also prioritize analysis of optimal data by taking into account the range of movement of the participants. In this way, highly relevant data can be prioritized by taking into account the geographical location information of the participants.

[0140] The facial expression analysis unit can analyze the social media activities of the participants and analyze related data when analyzing their facial expressions. The facial expression analysis unit analyzes the social media activities of the participants and analyzes related data when analyzing their facial expressions. For example, the facial expression analysis unit analyzes the content posted by the participants on social media and analyzes related data. The facial expression analysis unit can also analyze related data by referring to the activities of the participants' friends on social media. Furthermore, the facial expression analysis unit can analyze related data based on the check-in information of the participants on social media. In this way, related data can be analyzed by analyzing the social media activities of the participants.

[0141] The facial expression analysis unit can customize the analysis method by reflecting the participants' past feedback when analyzing facial expressions. The facial expression analysis unit customizes the analysis method by reflecting the participants' past feedback when analyzing facial expressions. For example, the facial expression analysis unit performs facial expression analysis using a similar method based on an analysis method that the participants have previously rated highly. The facial expression analysis unit can also analyze the participants' past feedback and propose the optimal analysis method. Furthermore, the facial expression analysis unit can customize the analysis method based on the participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback.

[0142] The questionnaire analysis unit estimates the user's emotions and adjusts the method of questionnaire analysis based on the estimated user emotions. The questionnaire analysis unit estimates the user's emotions and adjusts the method of questionnaire analysis based on the estimated user emotions. For example, the questionnaire analysis unit performs detailed questionnaire analysis when the user is relaxed. Furthermore, the questionnaire analysis unit can perform concise questionnaire analysis when the user is nervous. Furthermore, the questionnaire analysis unit can perform questionnaire analysis that reflects changes in emotions when the user is excited. This allows for more appropriate questionnaire analysis by adjusting the method of questionnaire analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the questionnaire analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questionnaire analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0143] The survey analysis unit can improve the accuracy of the analysis when analyzing a survey by taking into account the attribute information of the participants. The survey analysis unit improves the accuracy of the analysis when analyzing a survey by taking into account the attribute information of the participants. The survey analysis unit performs appropriate survey analysis based on, for example, the age and gender of the participants. The survey analysis unit can also perform highly relevant survey analysis based on the occupation and position of the participants. Furthermore, the survey analysis unit can also perform optimal survey analysis based on the interests and concerns of the participants. In this way, the accuracy of the analysis can be improved by taking into account the attribute information of the participants.

[0144] The survey analysis unit can optimize the analysis method by referring to past event data when analyzing a survey. The survey analysis unit can optimize the analysis method by referring to past event data when analyzing a survey. The survey analysis unit, for example, sets an optimal survey analysis method based on past event data. The survey analysis unit can also analyze past event data and improve the analysis method. Furthermore, the survey analysis unit can also optimize the timing of analysis by referring to past event data. In this way, the analysis method can be optimized by referring to past event data.

[0145] The questionnaire analysis unit can analyze the content of participants' responses in detail when analyzing the questionnaire. The questionnaire analysis unit analyzes the content of participants' responses in detail when analyzing the questionnaire. For example, the questionnaire analysis unit analyzes the content of participants' responses in detail and evaluates the effectiveness of the event. The questionnaire analysis unit can also suggest improvements for the next event based on the content of participants' responses. Furthermore, the questionnaire analysis unit can comprehensively analyze the content of participants' responses and identify the factors that contributed to the success of the event. In this way, the effectiveness of the event can be evaluated by analyzing the content of participants' responses in detail.

[0146] The questionnaire analysis unit estimates the user's emotions and determines the priority of the questionnaire analysis based on the estimated user emotions. The questionnaire analysis unit estimates the user's emotions and determines the priority of the questionnaire analysis based on the estimated user emotions. For example, if the user is nervous, the questionnaire analysis unit prioritizes a questionnaire analysis that will help the user relax. Furthermore, if the user is relaxed, the questionnaire analysis unit can prioritize a detailed questionnaire analysis. Furthermore, if the user is excited, the questionnaire analysis unit can prioritize a questionnaire analysis that reflects the user's emotional changes. This allows for prioritizing the questionnaire analysis based on the user's emotions, thereby enabling more important data to be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the questionnaire analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the questionnaire analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0147] When analyzing the survey, the survey analysis unit can prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. When analyzing the survey, the survey analysis unit prioritize analysis of highly relevant data by taking into account the geographical location information of the participants. For example, the survey analysis unit prioritizes analysis of region-specific data based on the participant's current location. The survey analysis unit can also prioritize analysis of highly relevant data based on the participant's geographical location information. Furthermore, the survey analysis unit can also prioritize analysis of optimal data by taking into account the participant's range of movement. In this way, highly relevant data can be prioritized analyzed by taking into account the participant's geographical location information.

[0148] The survey analysis unit can analyze the participants' social media activities and analyze related data when analyzing the survey. The survey analysis unit analyzes the participants' social media activities and analyzes related data when analyzing the survey. For example, the survey analysis unit analyzes the content posted by the participants on social media and analyzes related data. The survey analysis unit can also analyze related data by referring to the activities of the participants' friends on social media. Furthermore, the survey analysis unit can analyze related data based on the participants' check-in information on social media. In this way, related data can be analyzed by analyzing the participants' social media activities.

[0149] The survey analysis unit can customize the analysis method by reflecting participants' past feedback when analyzing a survey. The survey analysis unit customizes the analysis method by reflecting participants' past feedback when analyzing a survey. For example, the survey analysis unit analyzes the survey using a method similar to an analysis method that participants have previously given a high rating to. The survey analysis unit can also analyze participants' past feedback and propose the optimal analysis method. Furthermore, the survey analysis unit can customize the analysis method based on participants' past feedback. In this way, the analysis method can be customized by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, collection unit, and verification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to select the type of online event and input detailed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past event data and participant attribute information to automatically generate an optimal plan. The collection unit, for example, collects participant responses in real time using the camera 42 and microphone 38B of the smart device 14 and analyzes them using the control unit 46A. The verification unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected response data and survey results to evaluate the effectiveness of the event. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, collection unit, and verification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to select the type of online event and input detailed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past event data and participant attribute information to automatically generate an optimal plan. The collection unit, for example, collects participant responses in real time using the camera 42 and microphone 238 of the smart glasses 214 and analyzes them using the control unit 46A. The verification unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected response data and survey results to evaluate the effectiveness of the event. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, generation unit, collection unit, and verification unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to select the type of online event and input detailed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past event data and participant attribute information to automatically generate an optimal plan. The collection unit, for example, collects participant responses in real time using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes them using the control unit 46A. The verification unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected response data and survey results to evaluate the effectiveness of the event. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, generation unit, collection unit, and verification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to select the type of online event and input detailed information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past event data and participant attribute information to automatically generate an optimal plan. The collection unit, for example, collects participant responses in real time using the camera 42 and microphone 238 of the robot 414 and analyzes them using the control unit 46A. The verification unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected response data and questionnaire results to evaluate the effectiveness of the event.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The reception unit can analyze the user's past event participation history and suggest the most suitable event type. For example, it can suggest similar events based on the types of events the user has previously participated in. The reception unit can also suggest events related to a specific theme based on the user's past participation history. Furthermore, the reception unit can analyze the user's past participation history and suggest events based on the event with the highest satisfaction. In this way, it is possible to suggest the most suitable event for the user by analyzing the user's past participation history.

[0152] The generation unit can adjust the level of detail of the plan based on the importance of the event when generating the plan. For example, for an important event, the generation unit generates a plan including a detailed schedule and content. For an event with low importance, the generation unit can also generate a plan including a simple schedule and content. Furthermore, for an event with medium importance, the generation unit can also generate a plan including a schedule and content with a moderate level of detail. In this way, an appropriate plan can be generated by adjusting the level of detail of the plan based on the importance of the event.

[0153] When collecting reaction data, the collection unit can improve the accuracy of collection by taking into account the attribute information of the participants. For example, appropriate questions are set based on the age and gender of the participants to collect reaction data. The collection unit can also collect highly relevant reaction data based on the participants' occupations and job titles. Furthermore, the collection unit can also collect optimal reaction data based on the participants' interests and concerns. In this way, by taking into account the attribute information of the participants, the accuracy of collection can be improved.

[0154] The verification unit can improve the accuracy of the effectiveness verification by taking into account the attribute information of the participants. For example, the verification unit can perform appropriate effectiveness verification based on the age and gender of the participants. The verification unit can also perform highly relevant effectiveness verification based on the occupation and position of the participants. Furthermore, the verification unit can perform optimal effectiveness verification based on the interests and concerns of the participants. In this way, the accuracy of the verification can be improved by taking into account the attribute information of the participants.

[0155] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the participant's attribute information. For example, the analysis unit can perform appropriate analysis based on the participant's age and gender. The analysis unit can also perform highly relevant analysis based on the participant's occupation and position. Furthermore, the analysis unit can perform optimal analysis based on the participant's interests and concerns. In this way, the analysis accuracy can be improved by taking into account the participant's attribute information.

[0156] The reception unit estimates the user's emotions and supports the user in selecting an event type based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface to facilitate the user's event type selection. If the user is relaxed, the reception unit can provide detailed event information to broaden the user's options. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly select an event type. This allows the user to select a more appropriate event by supporting the user in selecting an event type based on the user's emotions.

[0157] The generation unit estimates the user's emotions and adjusts the presentation method of the plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a plan using soft colors and calm music. If the user is excited, the generation unit can also generate a plan using bright colors and energetic music. Furthermore, if the user is stressed, the generation unit can generate a plan with a simple, visually calming design. In this way, by adjusting the presentation method of the plan based on the user's emotions, more appropriate plans can be generated.

[0158] The collection unit estimates the user's emotions and adjusts the reaction data collection method based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects reaction data that emphasizes natural conversation. When the user is nervous, the collection unit can also collect reaction data by mainly asking simple questions. Furthermore, when the user is excited, the collection unit can use an interactive method to collect detailed reaction data. This allows more appropriate data to be collected by adjusting the reaction data collection method based on the user's emotions.

[0159] The verification unit estimates the user's emotions and adjusts the method of effect verification based on the estimated user emotions. For example, the verification unit performs detailed effect verification when the user is relaxed. The verification unit can also perform brief effect verification when the user is nervous. Furthermore, the verification unit can also perform effect verification that reflects changes in emotions when the user is excited. In this way, by adjusting the method of effect verification based on the user's emotions, more appropriate effect verification can be performed.

[0160] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is nervous. Furthermore, the analysis unit can also perform an analysis that reflects changes in emotions when the user is excited. In this way, by adjusting the analysis method based on the user's emotions, more appropriate analysis can be performed.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The reception unit allows the user to select the type of online event. For example, the user can select the type of event, such as an employee general meeting, award ceremony, job offer ceremony / entrance ceremony, rally / kick-off, or company social gathering / networking event. The reception unit also allows the user to enter detailed information, such as the purpose of the event and the attributes of the participants. For example, in the case of an employee general meeting, the user can enter the participants' positions and departments, as well as the purpose of the event (e.g., performance report or policy explanation). Step 2: The generation unit automatically generates a plan based on the event selected by the reception unit. The generation unit analyzes past event data and participant attribute information to propose the optimal plan. For example, in the case of a general meeting of employees, it automatically generates a performance report presentation, slides explaining policies, and a discussion session for participants to exchange opinions. The generation unit can also use generation AI to automatically generate plans. Step 3: The collection unit collects participants' reactions in real time while the event is in progress based on the plan generated by the generation unit. The collection unit analyzes, for example, participants' facial expressions, comments, and chat content to understand the progress of the event. The collection unit analyzes participants' reactions using facial expression recognition technology, voice recognition technology, and text mining technology. Step 4: The Verification Department verifies the effectiveness of the event after it has ended based on the reaction data collected by the Collection Department. The Verification Department analyzes the participant reaction data and survey results to evaluate the effectiveness of the event. For example, they evaluate participants' satisfaction, level of understanding, and the lively exchange of opinions, and propose improvements for the next event.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 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.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, a 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.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0206] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0207] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0208] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0210] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0211] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0212] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0213] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0217] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0218] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0219] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0220] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0221] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0222] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0223] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0224] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0225] 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.

[0226] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0227] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0228] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0229] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0230] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0231] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0232] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0233] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0234] [Explanation of symbols]

[0235] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for selecting the type of event; a generation unit that automatically generates a plan according to the event selected by the reception unit; a collection unit that collects responses from participants in real time while the event is in progress based on the plan generated by the generation unit; a verification unit that verifies the effectiveness of the event after the event ends based on the reaction data collected by the collection unit; Equipped with A system characterized by:

2. The generation unit Analyze past event data or participant attribute information to propose optimal plans 2. The system of claim 1.

3. The collecting unit Analyze participants' facial expressions, comments, and chat content to understand the progress of the event 2. The system of claim 1.

4. The verification unit Analyze participant response data or survey results to evaluate the effectiveness of the event 2. The system of claim 1.

5. The generation unit Equipped with an analysis unit that analyzes participant attribute information 2. The system of claim 1.

6. The collecting unit Equipped with an expression analysis unit that analyzes participants' facial expressions and comments 2. The system of claim 1.

7. The verification unit Equipped with a survey analysis section that analyzes survey results 2. The system of claim 1.

8. The reception unit Estimates user's emotions and supports event type selection based on the estimated user's emotions 2. The system of claim 1.

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