system

Abstract

The system according to this embodiment aims to efficiently and accurately exchange information about events. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an update unit. The collection unit collects questions from participants. The analysis unit analyzes the questions collected by the collection unit. The provision unit provides information based on the results of the analysis performed by the analysis unit. The update unit allows the organizer to update the information.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that information may become old or misunderstandings may occur in the information exchange of events.

[0005] The system according to the embodiment aims to efficiently and accurately perform information exchange of events.

Means for Solving the Problems

[0006] ]>The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an update unit. The collection unit collects questions from participants. The analysis unit analyzes the questions collected by the collection unit. The provision unit provides information based on the results analyzed by the analysis unit. The update unit updates the information by the organizer.

Effects of the Invention

[0007] The system according to this embodiment can efficiently and accurately exchange information about events. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Event Info chatbot system according to an embodiment of the present invention is an AI chatbot system for facilitating efficient information exchange between event organizers and participants. This system aims to improve the sharing of event information and communication, contributing to the success of events. Conventional methods carried the risk of information becoming outdated or misunderstandings arising, but the Event Info chatbot solves these problems. Participants can easily and in real time obtain the information they need through the AI ​​chatbot. They can also ask questions anonymously, allowing them to ask any question without hesitation. Meanwhile, organizers can also use the AI ​​chatbot to automatically update information, always providing the latest and most accurate information. This allows participants to smoothly obtain information and enjoy the event, while organizers can accurately grasp the situation and ensure smooth event progress. For example, when a participant inputs a question into the AI ​​chatbot, the AI ​​chatbot analyzes the question and provides an appropriate answer. When an organizer updates information through the AI ​​chatbot, the AI ​​chatbot provides the latest information to participants. This mechanism streamlines the exchange of event information, improving convenience for both participants and organizers. As a result, the Event Info chatbot system can contribute to the success of events by enabling efficient information exchange between participants and organizers.

[0029] The event info chatbot system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an update unit. The collection unit collects questions from participants. Questions from participants include, but are not limited to, text format, voice format, and questions on specific topics. The collection unit collects questions in text format, for example, through a chatbot interface. The collection unit can also collect questions in voice format using speech recognition technology. Furthermore, the collection unit can filter and collect questions on specific topics. For example, the collection unit analyzes questions in text format using natural language processing technology and extracts questions related to specific topics. The analysis unit analyzes the questions collected by the collection unit. The analysis is performed using, for example, natural language processing technology or machine learning algorithms, but is not limited to these examples. For example, the analysis unit classifies the collected questions using a topic model and extracts relevant information. The analysis unit can also analyze the intent of questions using machine learning algorithms and generate appropriate answers. The provision unit provides information based on the results analyzed by the analysis unit. The information provided may include, but is not limited to, text information, image information, and links. For example, the provisioning unit generates text information based on analysis results and provides it to participants. The provisioning unit may also generate image information and provide it to participants. Furthermore, the provisioning unit may also generate relevant links and provide them to participants. The update unit allows the organizer to update information. The updated information may include, but is not limited to, event schedules, participant lists, and venue maps. For example, the update unit saves new schedule information entered by the organizer to the database and provides it to participants. The update unit may also save new participant lists entered by the organizer to the database and provide them to participants. Furthermore, the update unit may save new venue maps entered by the organizer to the database and provide them to participants. As a result, the event info chatbot system according to this embodiment can enable efficient information exchange between participants and organizers and contribute to the success of the event.

[0030] The EventInfo chatbot system includes an anonymous question reception section that allows participants to ask questions anonymously. This anonymous reception section enables participants to ask questions anonymously. Specific definitions and methods of anonymity include, but are not limited to, hiding user IDs and not recording IP addresses. For example, the anonymous reception section allows participants to ask questions anonymously by hiding user IDs. It also allows participants to ask questions anonymously by not recording IP addresses. Furthermore, the anonymous reception section maintains complete anonymity by not collecting any personal information of participants. This allows participants to ask questions freely and facilitates smooth information exchange about the event.

[0031] The EventInfo chatbot system features a real-time information delivery section that ensures participants always have access to the latest information. Specific definitions and criteria for "real-time" include, but are not limited to, acceptable latency and update frequency. For example, the real-time information delivery section may use a latency of less than one second as a criterion. The information delivery section can also be configured to update every minute. Furthermore, the real-time information delivery section maintains real-time functionality by responding immediately to participant requests for information. This ensures participants always have access to the latest information and facilitates smooth information exchange regarding the event.

[0032] The data collection unit can analyze participants' past question history and select appropriate collection methods. For example, the unit can prioritize collecting topics that participants have frequently asked about in the past. Furthermore, based on participants' past question history, the unit can concentrate data collection during specific time periods if there are many questions during those times. In addition, the unit can analyze participants' past question history and select the most effective collection method. Specific content and storage methods for question history include, but are not limited to, past question content, question date and time, and questioner information. For example, the unit can store past question content in a database and extract frequently asked topics. The unit can also analyze question dates and times to identify trends in question concentration during specific time periods. Furthermore, the unit can analyze questioner information to identify topics frequently asked by specific participants. This allows the unit to effectively collect questions based on past question history.

[0033] The data collection unit can filter questions based on participants' current areas of interest. For example, it can prioritize collecting questions related to topics that participants are currently interested in. The data collection unit can also filter out less relevant questions based on participants' areas of interest. Furthermore, the data collection unit can analyze participants' current areas of interest and collect the most relevant questions. Specific methods and criteria for identifying areas of interest include, but are not limited to, survey results, past questions, and browsing history. For example, the data collection unit can analyze survey results to identify participants' areas of interest. It can also analyze past questions to identify topics that participants frequently ask about. Furthermore, it can analyze participants' browsing history to identify topics they are currently interested in. This allows the data collection unit to collect highly relevant questions based on participants' areas of interest.

[0034] The data collection unit can prioritize collecting questions that are highly relevant based on the participant's geographical location information. For example, if a participant is in a specific region, the data collection unit will prioritize collecting questions related to that region. The data collection unit can also filter out irrelevant questions based on the participant's geographical location information. Furthermore, the data collection unit can collect the most relevant questions by considering the participant's current location. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location estimation from IP addresses. For example, the data collection unit can use GPS data to identify the participant's current location and prioritize collecting questions related to that region. The data collection unit can also obtain the participant's geographical location information using location estimation from IP addresses and filter out irrelevant questions. Furthermore, the data collection unit can develop algorithms to collect the most relevant questions by considering the participant's current location. This allows the data collection unit to collect highly relevant questions based on geographical location information.

[0035] The data collection unit can analyze participants' social media activity when collecting questions and collect relevant questions. For example, the data collection unit can prioritize collecting questions related to topics that participants have shown interest in on social media. The data collection unit can also filter out irrelevant questions based on participants' social media activity. Furthermore, the data collection unit can analyze participants' social media activity and collect the most relevant questions. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, number of likes, and number of followers. For example, the data collection unit can analyze participants' posts to identify topics they are interested in. The data collection unit can also identify topics that participants are interested in based on the number of likes. Furthermore, the data collection unit can assess participants' influence based on their number of followers and collect relevant questions. In this way, the data collection unit can collect relevant questions based on social media activity.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions during the analysis. For example, the analysis unit will perform a detailed analysis for high-importance questions. It can also perform a simplified analysis for low-importance questions. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the questions. Specific criteria and methods for evaluating the importance of questions include, but are not limited to, the scope of the question's impact and the questioner's position. For example, the analysis unit can evaluate the scope of the question's impact and perform a detailed analysis for questions with a wide scope of impact. It can also evaluate the questioner's position and perform a detailed analysis for questions from high-ranking questioners. Furthermore, the analysis unit can analyze the content of the questions and perform a detailed analysis for high-importance questions. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the questions.

[0037] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. It can also apply a simpler analysis algorithm to general questions. Furthermore, the analysis unit can select the most suitable analysis algorithm depending on the category of the question. Specific types of analysis algorithms and their application methods include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can use a clustering algorithm to classify technical questions and perform a specialized analysis. It can also use a classification algorithm to classify general questions and perform a simpler analysis. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the category of the question. This allows the analysis unit to apply the most suitable analysis algorithm depending on the category of the question.

[0038] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. It can also postpone the analysis of older questions. Furthermore, the analysis unit can adjust the priority of analysis in stages according to when the questions were submitted. Specific criteria and methods for evaluating when a question was submitted include, but are not limited to, submission date and time, and the progress of the event. For example, the analysis unit may prioritize the analysis of recently submitted questions based on submission date and time. It can also postpone the analysis of older questions, taking into account the progress of the event. Furthermore, the analysis unit can develop algorithms to adjust the priority of analysis in stages according to when the questions were submitted. This allows the analysis unit to adjust the priority of analysis according to when the questions were submitted.

[0039] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the questions. Specific criteria and methods for evaluating the relevance of questions include, but are not limited to, the similarity of the question content and related topics. For example, the analysis unit can evaluate the similarity of the question content and prioritize the analysis of highly relevant questions. It can also evaluate related topics and prioritize the analysis of highly relevant questions. Furthermore, the analysis unit can develop algorithms for adjusting the order of analysis in stages according to the relevance of the questions. This allows the analysis unit to adjust the order of analysis according to the relevance of the questions.

[0040] The information provider can adjust the level of detail provided based on the importance of the question. For example, the provider can provide detailed information for high-importance questions, and simplified information for low-importance questions. Furthermore, the provider can adjust the level of detail progressively according to the importance of the question. Specific criteria and methods for adjusting the level of detail include, but are not limited to, summary information, detailed information, and technical explanations. For example, the provider can provide summary information and then provide detailed information for high-importance questions. Alternatively, the provider can provide technical explanations and then provide detailed information for high-importance questions. Furthermore, the provider can develop algorithms for progressively adjusting the level of detail according to the importance of the question. This allows the provider to adjust the level of detail according to the importance of the question.

[0041] The information provider can apply different information provision algorithms depending on the category of the question. For example, the provider can apply a specialized information provision algorithm to technical questions. It can also apply a simpler information provision algorithm to general questions. Furthermore, the provider can select the most suitable information provision algorithm depending on the category of the question. Specific types and application methods of information provision algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. For example, the provider can use a recommendation algorithm to provide specialized information to technical questions. It can also use a filtering algorithm to provide simpler information to general questions. Furthermore, the provider can select and apply the most suitable information provision algorithm depending on the category of the question. This allows the provider to apply the most suitable information provision algorithm depending on the category of the question.

[0042] The information provider can determine the priority of information provision based on when the questions were submitted. For example, the provider may prioritize information for recently submitted questions. Alternatively, the provider may postpone information for older questions. Furthermore, the provider may adjust the priority of information provision in stages according to when the questions were submitted. Specific criteria and methods for determining the priority of information provision include, but are not limited to, the urgency of the question and the importance of the questioner. For example, the provider may assess the urgency of the questions and prioritize information provision for high-urgency questions. The provider may also assess the importance of the questioners and prioritize information provision for high-importance questions. Furthermore, the provider may develop algorithms to adjust the priority of information provision in stages according to when the questions were submitted. This allows the provider to adjust the priority of information provision according to when the questions were submitted.

[0043] The information provider can adjust the order in which information is provided based on the relevance of the questions. For example, the provider will prioritize providing information to highly relevant questions. It can also postpone providing information to less relevant questions. Furthermore, the provider can adjust the order of information provision in stages according to the relevance of the questions. Specific criteria and methods for adjusting the order of information provision include, but are not limited to, prioritizing information by relevance or importance. For example, the provider can provide information in order of relevance, prioritizing information for highly relevant questions. It can also provide information in order of importance, prioritizing information for important questions. Furthermore, the provider can develop algorithms for adjusting the order of information provision in stages according to the relevance of the questions. This allows the provider to adjust the order of information provision according to the relevance of the questions.

[0044] The update unit can select an appropriate update method by referring to past update history when updating information. For example, the update unit can prioritize selecting update methods that have been effective in the past. Furthermore, the update unit can select an update method that is effective for a specific time period based on past update history. In addition, the update unit can analyze past update history and select the most effective update method. The specific content and storage method of the update history may include, but are not limited to, past update content, update date and time, and updater information. For example, the update unit can store past update content in a database and extract effective update methods. The update unit can also analyze update date and time to select an update method that is effective for a specific time period. Furthermore, the update unit can analyze updater information and develop an algorithm to select the most effective update method. This allows the update unit to effectively update information based on past update history.

[0045] The update unit can customize the content of updates based on the progress of the event. For example, the update unit can prioritize updating necessary information according to the progress of the event. It can also postpone updating less relevant information, taking the event's progress into consideration. Furthermore, the update unit can analyze the event's progress and select the most effective updates. Specific criteria and methods for evaluating the event's progress include, but are not limited to, the event's phase and participant activity. For example, the update unit can evaluate the event's phase and prioritize updating necessary information. It can also evaluate participant activity and postpone updating less relevant information. Furthermore, the update unit can develop algorithms to analyze the event's progress and select the most effective updates. This allows the update unit to update the optimal information according to the event's progress.

[0046] The update unit can select the optimal update method when updating information, taking into account the geographical context of the event. For example, the update unit can prioritize updating information relevant to a specific region. It can also postpone updating less relevant information based on the event's geographical context. Furthermore, the update unit can analyze the event's geographical context and select the most effective update method. Specific methods for obtaining and using geographical context include, but are not limited to, the event location and participant location information. For example, the update unit can prioritize updating relevant information based on the event location. It can also postpone updating less relevant information based on participant location information. Furthermore, the update unit can develop algorithms to analyze the event's geographical context and select the most effective update method. This allows the update unit to effectively update information based on geographical context.

[0047] The update unit can appropriately update information by referring to relevant external data when updating information. For example, the update unit can select the optimal update content based on relevant external data. The update unit can also refer to external data to postpone updating less relevant information. Furthermore, the update unit can analyze external data to select the most effective update content. Specific types of external data and methods of acquisition include, but are not limited to, public databases and data acquisition from APIs. For example, the update unit can refer to public databases to acquire relevant information. The update unit can also acquire external data using APIs and postpone updating less relevant information. Furthermore, the update unit can analyze external data and develop algorithms to select the most effective update content. This allows the update unit to update information optimally based on external data.

[0048] The anonymous question reception unit can select the most effective reception method when receiving an anonymous question by referring to past anonymous question history. For example, the anonymous question reception unit can prioritize selecting anonymous question reception methods that have been effective in the past. Furthermore, the anonymous question reception unit can select a reception method that is effective for a specific time period based on past anonymous question history. In addition, the anonymous question reception unit can analyze past anonymous question history to select the most effective reception method. The specific contents and storage methods of the anonymous question history include, but are not limited to, past question content, question date and time, and questioner information. For example, the anonymous question reception unit can store past question content in a database and extract effective reception methods. The anonymous question reception unit can also analyze question date and time to select a reception method that is effective for a specific time period. Furthermore, the anonymous question reception unit can analyze questioner information and develop an algorithm to select the most effective reception method. This allows the anonymous question reception unit to effectively receive anonymous questions based on past anonymous question history.

[0049] The anonymous question reception system can select the optimal reception method when receiving anonymous questions, taking into account the participant's device information. For example, if a participant is using a smartphone, the anonymous question reception system can accept anonymous questions with a simple interface. If a participant is using a tablet, the anonymous question reception system can accept anonymous questions with an interface optimized for a larger screen. Furthermore, if a participant is using a desktop computer, the anonymous question reception system can accept anonymous questions with a detailed interface. Specific methods for obtaining and using device information include, but are not limited to, device type, OS, and browser information. For example, the anonymous question reception system can identify the device type and provide a simple interface to participants using smartphones. It can also identify the OS and provide an interface optimized for a larger screen to participants using tablets. Furthermore, it can identify browser information and provide a detailed interface to participants using desktop computers. This allows the anonymous question reception system to effectively accept anonymous questions based on the participant's device information.

[0050] The real-time information delivery unit can select the optimal delivery method by referring to past delivery history when providing real-time information. For example, the real-time information delivery unit can prioritize selecting real-time information delivery methods that have been effective in the past. Furthermore, the real-time information delivery unit can select a delivery method that is effective for a specific time period based on past delivery history. In addition, the real-time information delivery unit can analyze past delivery history to select the most effective delivery method. The specific contents and storage methods of the delivery history include, but are not limited to, past delivery content, delivery date and time, and provider information. For example, the real-time information delivery unit can store past delivery content in a database and extract effective delivery methods. It can also analyze delivery dates and times to select a delivery method that is effective for a specific time period. Furthermore, the real-time information delivery unit can analyze provider information and develop algorithms to select the most effective delivery method. This allows the real-time information delivery unit to effectively provide real-time information based on past delivery history.

[0051] The real-time information delivery unit can select the optimal delivery method when providing real-time information, taking into account the participant's device information. For example, if a participant is using a smartphone, the real-time information delivery unit can provide a real-time information delivery method that is adapted to the screen size. Furthermore, if a participant is using a tablet, the real-time information delivery unit can provide a real-time information delivery method optimized for larger screens. In addition, if a participant is using a desktop, the real-time information delivery unit can provide a detailed real-time information delivery method. Specific criteria and methods for real-time information delivery include, but are not limited to, live streaming and real-time chat. For example, the real-time information delivery unit can use live streaming to provide information adapted to the screen size of a smartphone. It can also use real-time chat to provide information optimized for the larger screen of a tablet. Furthermore, the real-time information delivery unit can develop algorithms to provide detailed real-time information delivery methods to participants using desktops. This allows the real-time information delivery unit to effectively provide real-time information based on the participant's device information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The EventInfo chatbot system can also include a feedback collection unit to gather participant feedback. This unit collects feedback from participants after the event and provides it to the organizers. For example, the feedback collection unit can collect participant opinions in the form of a questionnaire. It can also provide a feedback form where participants can freely enter comments. Furthermore, the feedback collection unit can analyze the collected feedback and report areas for improvement and successes to the organizers. This allows organizers to understand areas for improvement for future events and host better events.

[0054] The event info chatbot system can also include an entertainment section to further engage participants. This section provides content that participants can enjoy during the event. For example, it could offer quizzes and games to keep participants entertained while they participate. It could also provide live performances and video content. Furthermore, it could offer interactive content that participants can engage with in real time. This allows participants to learn while having fun during the event, increasing their satisfaction with the event.

[0055] The event info chatbot system can also include a networking support section to assist participants in networking. This section provides a platform for participants to interact with each other. For example, it can create group chats based on topics of interest to participants. It can also match participants based on their profile information, connecting those with shared interests. Furthermore, it can support the exchange of contact information so that participants can stay in touch even after the event ends. This allows participants to build new connections through the event, enhancing its value.

[0056] The EventInfo chatbot system can also be equipped with a health monitoring unit to monitor participants' health status. The health monitoring unit monitors participants' health status in real time and issues alerts if any abnormalities are detected. For example, the health monitoring unit can measure participants' heart rate and body temperature and notify the organizers if any abnormalities are detected. The health monitoring unit can also provide a function to contact medical staff if a participant feels unwell. Furthermore, the health monitoring unit can suggest appropriate rest times and hydration timings based on the participant's health status. This allows participants to participate in the event with peace of mind, and organizers can ensure the safety of participants.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects questions from participants. These questions may be in text format, audio format, or on specific topics. The collection unit collects text-based questions through a chatbot interface and audio-based questions using speech recognition technology. It can also filter and collect questions on specific topics. Step 2: The analysis unit analyzes the questions collected by the collection unit. The analysis is performed using natural language processing techniques and machine learning algorithms. For example, the collected questions are classified using topic models, and relevant information is extracted. It is also possible to analyze the intent of the questions using machine learning algorithms and generate appropriate answers. Step 3: The provision unit provides information based on the results analyzed by the analysis unit. The information provided includes text information, image information, links, etc. For example, it can generate text information based on the analysis results and provide it to participants. It can also generate image information and related links and provide them to participants. Step 4: The update section involves the organizer updating the information. This includes the event schedule, participant list, and venue map. For example, the organizer saves the new schedule information, participant list, and venue map they enter into the database and provides them to participants.

[0059] (Example of form 2) The Event Info chatbot system according to an embodiment of the present invention is an AI chatbot system for facilitating efficient information exchange between event organizers and participants. This system aims to improve the sharing of event information and communication, contributing to the success of events. Conventional methods carried the risk of information becoming outdated or misunderstandings arising, but the Event Info chatbot solves these problems. Participants can easily and in real time obtain the information they need through the AI ​​chatbot. They can also ask questions anonymously, allowing them to ask any question without hesitation. Meanwhile, organizers can also use the AI ​​chatbot to automatically update information, always providing the latest and most accurate information. This allows participants to smoothly obtain information and enjoy the event, while organizers can accurately grasp the situation and ensure smooth event progress. For example, when a participant inputs a question into the AI ​​chatbot, the AI ​​chatbot analyzes the question and provides an appropriate answer. When an organizer updates information through the AI ​​chatbot, the AI ​​chatbot provides the latest information to participants. This mechanism streamlines the exchange of event information, improving convenience for both participants and organizers. As a result, the Event Info chatbot system can contribute to the success of events by enabling efficient information exchange between participants and organizers.

[0060] The event info chatbot system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an update unit. The collection unit collects questions from participants. Questions from participants include, but are not limited to, text format, voice format, and questions on specific topics. The collection unit collects questions in text format, for example, through a chatbot interface. The collection unit can also collect questions in voice format using speech recognition technology. Furthermore, the collection unit can filter and collect questions on specific topics. For example, the collection unit analyzes questions in text format using natural language processing technology and extracts questions related to specific topics. The analysis unit analyzes the questions collected by the collection unit. The analysis is performed using, for example, natural language processing technology or machine learning algorithms, but is not limited to these examples. For example, the analysis unit classifies the collected questions using a topic model and extracts relevant information. The analysis unit can also analyze the intent of questions using machine learning algorithms and generate appropriate answers. The provision unit provides information based on the results analyzed by the analysis unit. The information provided may include, but is not limited to, text information, image information, and links. For example, the provisioning unit generates text information based on analysis results and provides it to participants. The provisioning unit may also generate image information and provide it to participants. Furthermore, the provisioning unit may also generate relevant links and provide them to participants. The update unit allows the organizer to update information. The updated information may include, but is not limited to, event schedules, participant lists, and venue maps. For example, the update unit saves new schedule information entered by the organizer to the database and provides it to participants. The update unit may also save new participant lists entered by the organizer to the database and provide them to participants. Furthermore, the update unit may save new venue maps entered by the organizer to the database and provide them to participants. As a result, the event info chatbot system according to this embodiment can enable efficient information exchange between participants and organizers and contribute to the success of the event.

[0061] The EventInfo chatbot system includes an anonymous question reception section that allows participants to ask questions anonymously. This anonymous reception section enables participants to ask questions anonymously. Specific definitions and methods of anonymity include, but are not limited to, hiding user IDs and not recording IP addresses. For example, the anonymous reception section allows participants to ask questions anonymously by hiding user IDs. It also allows participants to ask questions anonymously by not recording IP addresses. Furthermore, the anonymous reception section maintains complete anonymity by not collecting any personal information of participants. This allows participants to ask questions freely and facilitates smooth information exchange about the event.

[0062] The EventInfo chatbot system features a real-time information delivery section that ensures participants always have access to the latest information. Specific definitions and criteria for "real-time" include, but are not limited to, acceptable latency and update frequency. For example, the real-time information delivery section may use a latency of less than one second as a criterion. The information delivery section can also be configured to update every minute. Furthermore, the real-time information delivery section maintains real-time functionality by responding immediately to participant requests for information. This ensures participants always have access to the latest information and facilitates smooth information exchange regarding the event.

[0063] The data collection unit can analyze participants' emotions and adjust the timing of question collection based on the analyzed emotions. For example, if a participant is excited, the data collection unit can accelerate the question collection timing to respond immediately. Conversely, if a participant is relaxed, the data collection unit can set the question collection timing to normal. Furthermore, if a participant is feeling anxious, the data collection unit can delay the question collection timing to provide reassurance. Specific methods and criteria for emotion analysis include, but are not limited to, facial recognition, voice analysis, and text analysis. For example, the data collection unit can use facial recognition technology to analyze a participant's facial expressions and estimate their emotions. It can also use voice analysis technology to analyze the tone and speed of a participant's voice and estimate their emotions. Furthermore, it can use text analysis technology to analyze a participant's text messages and estimate their emotions. This allows the data collection unit to collect questions at the appropriate time according to the participant's emotions.

[0064] The data collection unit can analyze participants' past question history and select appropriate collection methods. For example, the unit can prioritize collecting topics that participants have frequently asked about in the past. Furthermore, based on participants' past question history, the unit can concentrate data collection during specific time periods if there are many questions during those times. In addition, the unit can analyze participants' past question history and select the most effective collection method. Specific content and storage methods for question history include, but are not limited to, past question content, question date and time, and questioner information. For example, the unit can store past question content in a database and extract frequently asked topics. The unit can also analyze question dates and times to identify trends in question concentration during specific time periods. Furthermore, the unit can analyze questioner information to identify topics frequently asked by specific participants. This allows the unit to effectively collect questions based on past question history.

[0065] The data collection unit can filter questions based on participants' current areas of interest. For example, it can prioritize collecting questions related to topics that participants are currently interested in. The data collection unit can also filter out less relevant questions based on participants' areas of interest. Furthermore, the data collection unit can analyze participants' current areas of interest and collect the most relevant questions. Specific methods and criteria for identifying areas of interest include, but are not limited to, survey results, past questions, and browsing history. For example, the data collection unit can analyze survey results to identify participants' areas of interest. It can also analyze past questions to identify topics that participants frequently ask about. Furthermore, it can analyze participants' browsing history to identify topics they are currently interested in. This allows the data collection unit to collect highly relevant questions based on participants' areas of interest.

[0066] The data collection unit can analyze participants' emotions and determine the priority of questions to collect based on those emotions. For example, if a participant is excited, the unit will prioritize collecting those questions. Conversely, if a participant is relaxed, the unit can collect questions with normal priority. Furthermore, if a participant is feeling anxious, the unit can postpone collecting those questions. Specific criteria and methods for determining question priority include, but are not limited to, the urgency of the question and the importance of the questioner. For example, the unit can assess the urgency of the questions and prioritize collecting the most urgent ones. It can also assess the importance of the questioner and prioritize collecting the most important questions. Furthermore, the unit can analyze the content of the questions and prioritize collecting the most relevant ones. This allows the data collection unit to adjust question priority according to the participants' emotions.

[0067] The data collection unit can prioritize collecting questions that are highly relevant based on the participant's geographical location information. For example, if a participant is in a specific region, the data collection unit will prioritize collecting questions related to that region. The data collection unit can also filter out irrelevant questions based on the participant's geographical location information. Furthermore, the data collection unit can collect the most relevant questions by considering the participant's current location. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location estimation from IP addresses. For example, the data collection unit can use GPS data to identify the participant's current location and prioritize collecting questions related to that region. The data collection unit can also obtain the participant's geographical location information using location estimation from IP addresses and filter out irrelevant questions. Furthermore, the data collection unit can develop algorithms to collect the most relevant questions by considering the participant's current location. This allows the data collection unit to collect highly relevant questions based on geographical location information.

[0068] The data collection unit can analyze participants' social media activity when collecting questions and collect relevant questions. For example, the data collection unit can prioritize collecting questions related to topics that participants have shown interest in on social media. The data collection unit can also filter out irrelevant questions based on participants' social media activity. Furthermore, the data collection unit can analyze participants' social media activity and collect the most relevant questions. Specific methods and criteria for analyzing social media activity include, but are not limited to, posts, number of likes, and number of followers. For example, the data collection unit can analyze participants' posts to identify topics they are interested in. The data collection unit can also identify topics that participants are interested in based on the number of likes. Furthermore, the data collection unit can assess participants' influence based on their number of followers and collect relevant questions. In this way, the data collection unit can collect relevant questions based on social media activity.

[0069] The analysis unit can analyze participants' emotions and adjust the presentation of the analysis based on those emotions. For example, if a participant is excited, the analysis unit may use a visually stimulating presentation. If a participant is relaxed, the analysis unit may use a calming presentation. Furthermore, if a participant is anxious, the analysis unit may use a reassuring presentation. Specific criteria and methods for presenting the analysis include, but are not limited to, graphs, text displays, and interactive dashboards. For example, the analysis unit may use graphs to provide a visually stimulating presentation. It may also use text displays to provide a calming presentation. Furthermore, it may use interactive dashboards to provide a reassuring presentation. This allows the analysis unit to provide analysis results in a presentation appropriate to the participant's emotions.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the questions during the analysis. For example, the analysis unit will perform a detailed analysis for high-importance questions. It can also perform a simplified analysis for low-importance questions. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the questions. Specific criteria and methods for evaluating the importance of questions include, but are not limited to, the scope of the question's impact and the questioner's position. For example, the analysis unit can evaluate the scope of the question's impact and perform a detailed analysis for questions with a wide scope of impact. It can also evaluate the questioner's position and perform a detailed analysis for questions from high-ranking questioners. Furthermore, the analysis unit can analyze the content of the questions and perform a detailed analysis for high-importance questions. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the questions.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. It can also apply a simpler analysis algorithm to general questions. Furthermore, the analysis unit can select the most suitable analysis algorithm depending on the category of the question. Specific types of analysis algorithms and their application methods include, but are not limited to, clustering algorithms and classification algorithms. For example, the analysis unit can use a clustering algorithm to classify technical questions and perform a specialized analysis. It can also use a classification algorithm to classify general questions and perform a simpler analysis. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the category of the question. This allows the analysis unit to apply the most suitable analysis algorithm depending on the category of the question.

[0072] The analysis unit can analyze the participant's emotions and adjust the length of the analysis based on the participant's emotions. For example, if the participant is excited, the analysis unit will perform a short, concise analysis. If the participant is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the participant is feeling anxious, the analysis unit can perform a detailed analysis to provide reassurance. Specific criteria and methods for adjusting the length of the analysis include, but are not limited to, the level of detail in the analysis results and the amount of information displayed. For example, the analysis unit can adjust the level of detail in the analysis results to provide a short, concise analysis. It can also adjust the amount of information displayed to provide a detailed analysis. Furthermore, the analysis unit can adjust the length of the analysis in stages according to the participant's emotions. This allows the analysis unit to adjust the length of the analysis according to the participant's emotions.

[0073] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit may prioritize the analysis of recently submitted questions. It can also postpone the analysis of older questions. Furthermore, the analysis unit can adjust the priority of analysis in stages according to when the questions were submitted. Specific criteria and methods for evaluating when a question was submitted include, but are not limited to, submission date and time, and the progress of the event. For example, the analysis unit may prioritize the analysis of recently submitted questions based on submission date and time. It can also postpone the analysis of older questions, taking into account the progress of the event. Furthermore, the analysis unit can develop algorithms to adjust the priority of analysis in stages according to when the questions were submitted. This allows the analysis unit to adjust the priority of analysis according to when the questions were submitted.

[0074] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant questions. It can also postpone the analysis of less relevant questions. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the questions. Specific criteria and methods for evaluating the relevance of questions include, but are not limited to, the similarity of the question content and related topics. For example, the analysis unit can evaluate the similarity of the question content and prioritize the analysis of highly relevant questions. It can also evaluate related topics and prioritize the analysis of highly relevant questions. Furthermore, the analysis unit can develop algorithms for adjusting the order of analysis in stages according to the relevance of the questions. This allows the analysis unit to adjust the order of analysis according to the relevance of the questions.

[0075] The information provider can analyze participants' emotions and adjust the method of information delivery based on the analyzed emotions. For example, if a participant is excited, the information provider can deliver information in a visually stimulating way. If a participant is relaxed, the information provider can deliver information in a calming way. Furthermore, if a participant is feeling anxious, the information provider can deliver information in a reassuring way. Specific criteria and methods for information delivery include, but are not limited to, email notifications, dashboard displays, and push notifications. For example, the information provider can deliver information in a visually stimulating way using email notifications. The information provider can also deliver information in a calming way using dashboard displays. Furthermore, the information provider can deliver information in a reassuring way using push notifications. This allows the information provider to deliver information in an appropriate way according to the participant's emotions.

[0076] The information provider can adjust the level of detail provided based on the importance of the question. For example, the provider can provide detailed information for high-importance questions, and simplified information for low-importance questions. Furthermore, the provider can adjust the level of detail progressively according to the importance of the question. Specific criteria and methods for adjusting the level of detail include, but are not limited to, summary information, detailed information, and technical explanations. For example, the provider can provide summary information and then provide detailed information for high-importance questions. Alternatively, the provider can provide technical explanations and then provide detailed information for high-importance questions. Furthermore, the provider can develop algorithms for progressively adjusting the level of detail according to the importance of the question. This allows the provider to adjust the level of detail according to the importance of the question.

[0077] The information provider can apply different information provision algorithms depending on the category of the question. For example, the provider can apply a specialized information provision algorithm to technical questions. It can also apply a simpler information provision algorithm to general questions. Furthermore, the provider can select the most suitable information provision algorithm depending on the category of the question. Specific types and application methods of information provision algorithms include, but are not limited to, recommendation algorithms and filtering algorithms. For example, the provider can use a recommendation algorithm to provide specialized information to technical questions. It can also use a filtering algorithm to provide simpler information to general questions. Furthermore, the provider can select and apply the most suitable information provision algorithm depending on the category of the question. This allows the provider to apply the most suitable information provision algorithm depending on the category of the question.

[0078] The information provider can analyze participants' emotions and adjust the length of the information provided based on those emotions. For example, if a participant is excited, the provider can provide short, concise information. If a participant is relaxed, the provider can also provide detailed information. Furthermore, if a participant is feeling anxious, the provider can provide detailed information to reassure them. Specific criteria and methods for adjusting the length of the information provided include, but are not limited to, the amount of information provided and the display time. For example, the provider can adjust the amount of information provided to provide short, concise information. The provider can also adjust the display time to provide detailed information. Furthermore, the provider can gradually adjust the length of the information provided according to the participant's emotions. This allows the provider to adjust the length of the information provided according to the participant's emotions.

[0079] The information provider can determine the priority of information provision based on when the questions were submitted. For example, the provider may prioritize information for recently submitted questions. Alternatively, the provider may postpone information for older questions. Furthermore, the provider may adjust the priority of information provision in stages according to when the questions were submitted. Specific criteria and methods for determining the priority of information provision include, but are not limited to, the urgency of the question and the importance of the questioner. For example, the provider may assess the urgency of the questions and prioritize information provision for high-urgency questions. The provider may also assess the importance of the questioners and prioritize information provision for high-importance questions. Furthermore, the provider may develop algorithms to adjust the priority of information provision in stages according to when the questions were submitted. This allows the provider to adjust the priority of information provision according to when the questions were submitted.

[0080] The information provider can adjust the order in which information is provided based on the relevance of the questions. For example, the provider will prioritize providing information to highly relevant questions. It can also postpone providing information to less relevant questions. Furthermore, the provider can adjust the order of information provision in stages according to the relevance of the questions. Specific criteria and methods for adjusting the order of information provision include, but are not limited to, prioritizing information by relevance or importance. For example, the provider can provide information in order of relevance, prioritizing information for highly relevant questions. It can also provide information in order of importance, prioritizing information for important questions. Furthermore, the provider can develop algorithms for adjusting the order of information provision in stages according to the relevance of the questions. This allows the provider to adjust the order of information provision according to the relevance of the questions.

[0081] The update unit can analyze the organizer's emotions and adjust the timing of information updates based on the analyzed emotions. For example, if the organizer is nervous, the update unit can speed up the timing of information updates to respond immediately. Conversely, if the organizer is relaxed, the update unit can update information at the normal timing. Furthermore, if the organizer is feeling anxious, the update unit can delay the timing of information updates to provide reassurance. Specific criteria and methods for adjusting the timing of information updates include, but are not limited to, real-time updates and periodic updates. For example, the update unit can use real-time updates to instantly update information. It can also use periodic updates to update information regularly. Furthermore, the update unit can develop algorithms to gradually adjust the timing of information updates in response to the organizer's emotions. This allows the update unit to adjust the timing of information updates according to the organizer's emotions.

[0082] The update unit can select an appropriate update method by referring to past update history when updating information. For example, the update unit can prioritize selecting update methods that have been effective in the past. Furthermore, the update unit can select an update method that is effective for a specific time period based on past update history. In addition, the update unit can analyze past update history and select the most effective update method. The specific content and storage method of the update history may include, but are not limited to, past update content, update date and time, and updater information. For example, the update unit can store past update content in a database and extract effective update methods. The update unit can also analyze update date and time to select an update method that is effective for a specific time period. Furthermore, the update unit can analyze updater information and develop an algorithm to select the most effective update method. This allows the update unit to effectively update information based on past update history.

[0083] The update unit can customize the content of updates based on the progress of the event. For example, the update unit can prioritize updating necessary information according to the progress of the event. It can also postpone updating less relevant information, taking the event's progress into consideration. Furthermore, the update unit can analyze the event's progress and select the most effective updates. Specific criteria and methods for evaluating the event's progress include, but are not limited to, the event's phase and participant activity. For example, the update unit can evaluate the event's phase and prioritize updating necessary information. It can also evaluate participant activity and postpone updating less relevant information. Furthermore, the update unit can develop algorithms to analyze the event's progress and select the most effective updates. This allows the update unit to update the optimal information according to the event's progress.

[0084] The update unit can analyze the organizer's emotions and determine update priorities based on those emotions. For example, if the organizer is nervous, the update unit will prioritize updating that information. If the organizer is relaxed, the update unit can update information with normal priorities. Furthermore, if the organizer is feeling anxious, the update unit can postpone updating that information. Specific criteria and methods for determining the priority of information updates include, but are not limited to, the urgency of the update and the importance of the update content. For example, the update unit can assess the urgency of the update and prioritize updating information with high urgency. It can also assess the importance of the update content and prioritize updating information with high importance. Furthermore, the update unit can develop algorithms to incrementally adjust update priorities according to the organizer's emotions. This allows the update unit to adjust the priority of information updates according to the organizer's emotions.

[0085] The update unit can select the optimal update method when updating information, taking into account the geographical context of the event. For example, the update unit can prioritize updating information relevant to a specific region. It can also postpone updating less relevant information based on the event's geographical context. Furthermore, the update unit can analyze the event's geographical context and select the most effective update method. Specific methods for obtaining and using geographical context include, but are not limited to, the event location and participant location information. For example, the update unit can prioritize updating relevant information based on the event location. It can also postpone updating less relevant information based on participant location information. Furthermore, the update unit can develop algorithms to analyze the event's geographical context and select the most effective update method. This allows the update unit to effectively update information based on geographical context.

[0086] The update unit can appropriately update information by referring to relevant external data when updating information. For example, the update unit can select the optimal update content based on relevant external data. The update unit can also refer to external data to postpone updating less relevant information. Furthermore, the update unit can analyze external data to select the most effective update content. Specific types of external data and methods of acquisition include, but are not limited to, public databases and data acquisition from APIs. For example, the update unit can refer to public databases to acquire relevant information. The update unit can also acquire external data using APIs and postpone updating less relevant information. Furthermore, the update unit can analyze external data and develop algorithms to select the most effective update content. This allows the update unit to update information optimally based on external data.

[0087] The anonymous question reception system can analyze participants' emotions and adjust the method of receiving anonymous questions based on the analyzed emotions. For example, if a participant is excited, the anonymous question reception system can accept anonymous questions through a simple interface. If a participant is relaxed, it can accept anonymous questions through a more detailed interface. Furthermore, if a participant is feeling anxious, it can accept anonymous questions through a reassuring interface. Specific criteria and methods for adjusting the anonymous question reception method include, but are not limited to, online forms and chatbots. For example, the anonymous question reception system can accept anonymous questions through a simple interface using an online form. It can also accept anonymous questions through a more detailed interface using a chatbot. Furthermore, the anonymous question reception system can develop algorithms to incrementally adjust the anonymous question reception method according to the participant's emotions. This allows the anonymous question reception system to accept anonymous questions in an appropriate manner according to the participant's emotions.

[0088] The anonymous question reception unit can select the most effective reception method when receiving an anonymous question by referring to past anonymous question history. For example, the anonymous question reception unit can prioritize selecting anonymous question reception methods that have been effective in the past. Furthermore, the anonymous question reception unit can select a reception method that is effective for a specific time period based on past anonymous question history. In addition, the anonymous question reception unit can analyze past anonymous question history to select the most effective reception method. The specific contents and storage methods of the anonymous question history include, but are not limited to, past question content, question date and time, and questioner information. For example, the anonymous question reception unit can store past question content in a database and extract effective reception methods. The anonymous question reception unit can also analyze question date and time to select a reception method that is effective for a specific time period. Furthermore, the anonymous question reception unit can analyze questioner information and develop an algorithm to select the most effective reception method. This allows the anonymous question reception unit to effectively receive anonymous questions based on past anonymous question history.

[0089] The anonymous reception desk can analyze participants' emotions and prioritize anonymous questions based on those emotions. For example, if a participant is excited, the anonymous reception desk will prioritize that question. If a participant is relaxed, the anonymous reception desk can prioritize the question at the normal priority level. Furthermore, if a participant is feeling anxious, the anonymous reception desk can postpone that question. Specific criteria and methods for determining the priority of anonymous questions include, but are not limited to, the urgency and importance of the question. For example, the anonymous reception desk can assess the urgency of the questions and prioritize those with higher urgency. It can also assess the importance of the questions and prioritize those with higher importance. Furthermore, the anonymous reception desk can develop algorithms to incrementally adjust the priority of anonymous questions according to the participant's emotions. This allows the anonymous reception desk to adjust the priority of anonymous questions according to the participant's emotions.

[0090] The anonymous question reception system can select the optimal reception method when receiving anonymous questions, taking into account the participant's device information. For example, if a participant is using a smartphone, the anonymous question reception system can accept anonymous questions with a simple interface. If a participant is using a tablet, the anonymous question reception system can accept anonymous questions with an interface optimized for a larger screen. Furthermore, if a participant is using a desktop computer, the anonymous question reception system can accept anonymous questions with a detailed interface. Specific methods for obtaining and using device information include, but are not limited to, device type, OS, and browser information. For example, the anonymous question reception system can identify the device type and provide a simple interface to participants using smartphones. It can also identify the OS and provide an interface optimized for a larger screen to participants using tablets. Furthermore, it can identify browser information and provide a detailed interface to participants using desktop computers. This allows the anonymous question reception system to effectively accept anonymous questions based on the participant's device information.

[0091] The real-time information provider can analyze participants' emotions and adjust the method of providing real-time information based on the analyzed emotions. For example, if a participant is excited, the real-time information provider can provide real-time information in a visually stimulating way. If a participant is relaxed, the real-time information provider can provide real-time information in a calming way. Furthermore, if a participant is feeling anxious, the real-time information provider can provide real-time information in a reassuring way. Specific criteria and methods for providing real-time information include, but are not limited to, live streaming and real-time chat. For example, the real-time information provider can use live streaming to provide real-time information in a visually stimulating way. It can also use real-time chat to provide real-time information in a calming way. Furthermore, the real-time information provider can develop algorithms to incrementally adjust the method of providing real-time information according to the participant's emotions. This allows the real-time information provider to provide real-time information in an appropriate way according to the participant's emotions.

[0092] The real-time information delivery unit can select the optimal delivery method by referring to past delivery history when providing real-time information. For example, the real-time information delivery unit can prioritize selecting real-time information delivery methods that have been effective in the past. Furthermore, the real-time information delivery unit can select a delivery method that is effective for a specific time period based on past delivery history. In addition, the real-time information delivery unit can analyze past delivery history to select the most effective delivery method. The specific contents and storage methods of the delivery history include, but are not limited to, past delivery content, delivery date and time, and provider information. For example, the real-time information delivery unit can store past delivery content in a database and extract effective delivery methods. It can also analyze delivery dates and times to select a delivery method that is effective for a specific time period. Furthermore, the real-time information delivery unit can analyze provider information and develop algorithms to select the most effective delivery method. This allows the real-time information delivery unit to effectively provide real-time information based on past delivery history.

[0093] The real-time information delivery unit can analyze participants' emotions and determine the priority of real-time information delivery based on the analyzed emotions. For example, if a participant is excited, the real-time information delivery unit will prioritize providing that information. Conversely, if a participant is relaxed, the real-time information delivery unit can provide information with normal priority. Furthermore, if a participant is feeling anxious, the real-time information delivery unit can postpone providing that information. Specific criteria and methods for determining the priority of real-time information delivery include, but are not limited to, the urgency and importance of the information. For example, the real-time information delivery unit can assess the urgency of the information and prioritize providing information with high urgency. It can also assess the importance of the information and prioritize providing information with high importance. Furthermore, the real-time information delivery unit can develop algorithms to incrementally adjust the priority of real-time information delivery according to participants' emotions. This allows the real-time information delivery unit to adjust the priority of real-time information delivery according to participants' emotions.

[0094] The real-time information delivery unit can select the optimal delivery method when providing real-time information, taking into account the participant's device information. For example, if a participant is using a smartphone, the real-time information delivery unit can provide a real-time information delivery method that is adapted to the screen size. Furthermore, if a participant is using a tablet, the real-time information delivery unit can provide a real-time information delivery method optimized for larger screens. In addition, if a participant is using a desktop, the real-time information delivery unit can provide a detailed real-time information delivery method. Specific criteria and methods for real-time information delivery include, but are not limited to, live streaming and real-time chat. For example, the real-time information delivery unit can use live streaming to provide information adapted to the screen size of a smartphone. It can also use real-time chat to provide information optimized for the larger screen of a tablet. Furthermore, the real-time information delivery unit can develop algorithms to provide detailed real-time information delivery methods to participants using desktops. This allows the real-time information delivery unit to effectively provide real-time information based on the participant's device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, update unit, anonymous reception unit, and real-time provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects questions from participants using the reception device 38 of the smart device 14. The analysis unit analyzes the questions using the identification processing unit 290 of the data processing unit 12. The provision unit provides information using the output device 40 of the smart device 14. The update unit updates the information entered by the organizer using the identification processing unit 290 of the data processing unit 12. The anonymous reception unit accepts questions anonymously using the reception device 38 of the smart device 14. The real-time provision unit provides information in real time using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, update unit, anonymous reception unit, and real-time provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects questions from participants using the microphone 238 of the smart glasses 214. The analysis unit analyzes the questions using the identification processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the smart glasses 214. The update unit updates the information entered by the organizer using the identification processing unit 290 of the data processing unit 12. The anonymous reception unit accepts questions anonymously using the microphone 238 of the smart glasses 214. The real-time provision unit provides information in real time using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, update unit, anonymous reception unit, and real-time provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects questions from participants using the microphone 238 of the headset terminal 314. The analysis unit analyzes the questions using the identification processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the headset terminal 314. The update unit updates the information entered by the organizer using the identification processing unit 290 of the data processing unit 12. The anonymous reception unit accepts questions anonymously using the microphone 238 of the headset terminal 314. The real-time provision unit provides information in real time using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, update unit, anonymous reception unit, and real-time provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects questions from participants using the microphone 238 of the robot 414. The analysis unit analyzes the questions using the identification processing unit 290 of the data processing unit 12. The provision unit provides information using the speaker 240 of the robot 414. The update unit updates the information entered by the organizer using the identification processing unit 290 of the data processing unit 12. The anonymous reception unit accepts questions anonymously using the microphone 238 of the robot 414. The real-time provision unit provides information in real time using the speaker 240 of the robot 414.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The EventInfo chatbot system can also include a feedback collection unit to gather participant feedback. This unit collects feedback from participants after the event and provides it to the organizers. For example, the feedback collection unit can collect participant opinions in the form of a questionnaire. It can also provide a feedback form where participants can freely enter comments. Furthermore, the feedback collection unit can analyze the collected feedback and report areas for improvement and successes to the organizers. This allows organizers to understand areas for improvement for future events and host better events.

[0097] The event info chatbot system can also include an entertainment section to further engage participants. This section provides content that participants can enjoy during the event. For example, it could offer quizzes and games to keep participants entertained while they participate. It could also provide live performances and video content. Furthermore, it could offer interactive content that participants can engage with in real time. This allows participants to learn while having fun during the event, increasing their satisfaction with the event.

[0098] The event info chatbot system can also include a networking support section to assist participants in networking. This section provides a platform for participants to interact with each other. For example, it can create group chats based on topics of interest to participants. It can also match participants based on their profile information, connecting those with shared interests. Furthermore, it can support the exchange of contact information so that participants can stay in touch even after the event ends. This allows participants to build new connections through the event, enhancing its value.

[0099] The EventInfo chatbot system can also be equipped with a health monitoring unit to monitor participants' health status. The health monitoring unit monitors participants' health status in real time and issues alerts if any abnormalities are detected. For example, the health monitoring unit can measure participants' heart rate and body temperature and notify the organizers if any abnormalities are detected. The health monitoring unit can also provide a function to contact medical staff if a participant feels unwell. Furthermore, the health monitoring unit can suggest appropriate rest times and hydration timings based on the participant's health status. This allows participants to participate in the event with peace of mind, and organizers can ensure the safety of participants.

[0100] The EventInfo chatbot system can also include a progress adjustment unit that analyzes participants' emotions and adjusts the event's progress based on those emotions. This unit analyzes participants' emotions in real time and optimizes the event's flow. For example, if a participant is excited, the unit can suggest a faster pace. If a participant is relaxed, it can suggest a more relaxed pace. Furthermore, if a participant is feeling anxious, it can suggest a reassuring pace. This allows participants to comfortably participate in the event, and enables organizers to conduct the event in a way that aligns with the participants' emotions.

[0101] The event info chatbot system can also include an answer adjustment unit that analyzes participants' emotions and adjusts how it answers questions based on those emotions. The answer adjustment unit analyzes participants' emotions in real time and selects the optimal answer. For example, if a participant is excited, the answer adjustment unit can provide a concise and clear answer. If a participant is relaxed, the answer adjustment unit can provide a detailed and polite answer. Furthermore, if a participant is feeling anxious, the answer adjustment unit can provide a reassuring answer. This allows participants to receive answers that match their emotions, increasing their satisfaction with the questions.

[0102] The event info chatbot system can also include a timing adjustment unit that analyzes participants' emotions and adjusts the timing of information delivery based on those emotions. The timing adjustment unit analyzes participants' emotions in real time and selects the optimal timing for information delivery. For example, if a participant is excited, the timing adjustment unit can provide information immediately. If a participant is relaxed, the timing adjustment unit can also provide information at a normal time. Furthermore, if a participant is feeling anxious, the timing adjustment unit can provide information at a time that provides reassurance. This allows participants to receive information at a time that matches their emotions, improving the effectiveness of information delivery.

[0103] The event info chatbot system can also include a feedback adjustment unit that analyzes participants' emotions and collects event feedback based on those emotions. The feedback adjustment unit analyzes participants' emotions in real time and selects the optimal feedback collection method. For example, if a participant is excited, the feedback adjustment unit can collect feedback in the form of a simple questionnaire. If a participant is relaxed, the feedback adjustment unit can provide a detailed feedback form. Furthermore, if a participant is feeling anxious, the feedback adjustment unit can provide a reassuring feedback collection method. This allows participants to provide feedback in a way that suits their emotions, improving the quality of the feedback.

[0104] The EventInfo chatbot system can also include an evaluation adjustment unit that analyzes participants' emotions and evaluates the event based on those emotions. The evaluation adjustment unit analyzes participants' emotions in real time and selects the optimal evaluation method. For example, if a participant is excited, the evaluation adjustment unit can provide a concise evaluation method. If a participant is relaxed, it can provide a more detailed evaluation method. Furthermore, if a participant is feeling anxious, it can provide a reassuring evaluation method. This allows participants to evaluate the event in a way that suits their emotions, improving the accuracy of the evaluation.

[0105] The EventInfo chatbot system can also include a progress adjustment unit that analyzes participants' emotions and adjusts the event's progress based on those emotions. This unit analyzes participants' emotions in real time and optimizes the event's flow. For example, if a participant is excited, the unit can suggest a faster pace. If a participant is relaxed, it can suggest a more relaxed pace. Furthermore, if a participant is feeling anxious, it can suggest a reassuring pace. This allows participants to comfortably participate in the event, and enables organizers to conduct the event in a way that aligns with the participants' emotions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The collection unit collects questions from participants. These questions may be in text format, audio format, or on specific topics. The collection unit collects text-based questions through a chatbot interface and audio-based questions using speech recognition technology. It can also filter and collect questions on specific topics. Step 2: The analysis unit analyzes the questions collected by the collection unit. The analysis is performed using natural language processing techniques and machine learning algorithms. For example, the collected questions are classified using topic models, and relevant information is extracted. It is also possible to analyze the intent of the questions using machine learning algorithms and generate appropriate answers. Step 3: The provision unit provides information based on the results analyzed by the analysis unit. The information provided includes text information, image information, links, etc. For example, it can generate text information based on the analysis results and provide it to participants. It can also generate image information and related links and provide them to participants. Step 4: The update section involves the organizer updating the information. This includes the event schedule, participant list, and venue map. For example, the organizer saves the new schedule information, participant list, and venue map they enter into the database and provides them to participants.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] [Explanation of Symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A question-collection team to gather questions from participants, An analysis unit analyzes the questions collected by the aforementioned collection unit, A providing unit that provides information based on the results of the analysis performed by the aforementioned analysis unit, It includes an update section where the organizer updates the information. A system characterized by the following features.

2. It has an anonymous question submission section that accepts questions anonymously. The system according to feature 1.

3. It is equipped with a real-time information provision unit that provides information in real time. The system according to feature 1.

4. The aforementioned collection unit is We analyze the participants' emotions and adjust the timing of question collection based on the analyzed emotions. The system according to feature 1.

5. The aforementioned collection unit is Analyze participants' past question history to select the appropriate data collection method. The system according to feature 1.

6. The aforementioned collection unit is When collecting questions, filter them based on the participants' current areas of interest. The system according to feature 1.

7. The aforementioned collection unit is Analyze the participants' emotions and prioritize the questions to collect based on the analyzed emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting questions, prioritize collecting questions that are highly relevant based on the participants' geographical location. The system according to feature 1.

9. The aforementioned collection unit is When collecting questions, we analyze participants' social media activity and gather relevant questions. The system according to feature 1.

10. The aforementioned analysis unit, We analyze the emotions of the participants and adjust the way the analysis is presented based on the analyzed emotions of the participants. The system according to feature 1.

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